Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

How will tradable green certificates affect electricity trading markets under renewable portfolio standards? A China perspective

How will tradable green certificates affect electricity trading markets under renewable portfolio... Abstract Renewable portfolio standards (RPS) are important guarantees to promote renewable energy (RE) consumption. The tradable green certificate (TGC) trading mechanism is a supporting mechanism of RPS, but the rate of TGC trading is low and there is a double-metering problem of RE consumption. With the introduction of new policies in China, we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation (BEG) projects are considered to participate in TGC trading. To explore the interaction between the TGC market and the electricity market, this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium model. The established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are analysed. The main conclusions are as follows. (i) The established market structure and model effectively solve the double-metering problem of RE consumption, making the TGC turnover rate reach 82.97 %, greatly improving the market efficiency. (ii) Increased demand for TGC will increase demand for RE electricity. The participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers (BEPs), reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based electricity. This will help promote the rapid development of China’s RE, especially the BEG industry. (iii) Among the influencing factors, the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of BEPs. The decline in TGC price and subsidy price will reduce the profit of BEPs. Finally, we put forward policy recommendations for China’s RPS and TGC trading mechanism. This study can provide a reference for the construction of China’s TGC market and electricity market and the development of RE. Open in new tabDownload slide tradable green certificate market, electricity market, renewable portfolio standards, biomass-based electricity generation, particle swarm optimization algorithm Introduction Promoting the development of renewable energy (RE) is crucial for accelerating the clean-energy revolution [1, 2]. Since the promulgation of the Renewable Energy Law of the People’s Republic of China in 2006, China has issued a series of policies to promote the development of the RE industry, mainly including feed-in tariff (FIT) and renewable portfolio standards (RPS) policies [3]. The implementation of FIT causes the installed power capacity of RE to increase rapidly. In 2021, the installed renewable power capacity accounted for 43.5% of the total installed power capacity in China [4]. But FIT also imposes a huge financial burden on the government. RPS is a mandatory policy instrument adopted by the government to nurture the RE market and bring RE generation to a guaranteed minimum level. From 2018 to 2020, China has set specific RPS targets for each province for both total RE-generation targets and non-hydropower targets. The RPS is seen as an important long-term guarantee to promote RE consumption [5]. The tradable green certificate (TGC) trading mechanism is the supporting mechanism of RPS. The TGC trading mechanism is a market-based subsidy scheme designed to promote the development of RE-generation projects [6]. China set up RPS to promote China’s RE consumption, the purchase of a TGC equivalent to complete 1 MWh of RE consumption. China’s TGC trading is also aimed at reducing RE subsidies, as RE used for TGC trading is no longer subsidized. Therefore, theoretically carrying out TGC transactions can promote the consumption of RE and reduce the burden of national financial subsidies. At present, many scholars have used a variety of methods to study the impact of FIT and RPS mechanisms on the RE-generation industry, including electricity prices, investment incentives, the profits of market subjects and social welfare. Ciarreta et al. [7] believed that the incentive FIT policy cannot reflect electricity market conditions or price signals and when the scale of RE-generation projects is obvious, it will bring a huge economic burden to consumers. Zhang et al. [8] showed that RPS can effectively help China’s distributed photovoltaic generation to achieve grid parity after the cancellation of electricity price subsidies. Pineda and Bock [9] believed that in the TGC market, increasing quota obligations and appropriate penalties for irregularities would encourage investment in RE-generation projects. Zhang et al. [10] showed that RPS could improve the installed capacity and profit of biomass-based electricity generation (BEG) in a fully competitive market, but the impact on other electricity market subjects was not analysed. Dong and Shimada [11] indicated that under the RPS and FIT mechanism, RE consumption in the electricity market would increase and non-renewable-energy enterprises’ revenues would decrease. Zhou et al. [12] found that when the parameter, i.e. renewable-energy consumption responsibility weight (RECRW), is within a certain range, the social welfare under the RPS mechanism is always higher than that under the FIT policy. Liu et al. [13] showed that the market competition force of RE producers was still guaranteed even if the subsidy policy of the electricity price was abolished. The above studies show that RPS will better promote the development of RE and enhance the profits of market subjects and social welfare than the FIT policy. Since TGC trading is closely related to electricity trading, the coupling effect of the TGC market and electricity market has been studied by many scholars, mainly including the determination of the TGC price, the efficiency of the TGC market and TGC trading. Wolfgang et al. [14] proposed a method to predict short-term and medium-term TGC prices to assist investment decisions in RE projects. Zhao et al. [15] analysed the interaction between the TGC market and the electricity market. Based on the maximization of social welfare, the optimal benchmark price of TGC is 0.06 USD/kWh. Based on the Nash bargaining model, Zuo et al. [16] obtained the benchmark price of China’s TGC as 49.59 USD/MWh. Due to the technical differences of different RE generation, Zhao et al. [17] considered the key parameter of technology conversion coefficient in the transaction, which can effectively promote the TGC transaction. Song et al. [18] found that the operation efficiency of most TGC markets in China was not high, and suggested unifying the goals of regional development and electricity-generation enterprises to encourage market subjects to participate in TGC transactions. Compared with the benchmark on-grid price, the benefits of electricity plants under the marketed on-grid price are higher, which can effectively stimulate coal-fired electricity plants to increase the consumption demand of TGC [19]. The above studies show that the TGC market and the electricity market interact, but the efficiency of China’s TGC market is low and most studies take coal-fired electricity enterprises as the demand subject of TGC. On 10 May 2019, the National Energy Administration (NEA) issued the Notice on Establishing and Improving the Safeguard Mechanism for Renewable Energy Consumption, which stipulates that the responsibility for RE consumption is assumed jointly by electricity sellers and electricity consumers [20]. This indicates that the subject of RE consumption responsibility is transferred from the electricity-generation side to the electricity-selling side. In addition, the suppliers of TGC in the above studies only include wind-based electricity producers (WEPs) and photovoltaic-based electricity producers (PVEPs). On 29 September 2020, the NEA supplemented Some Opinions on Promoting the Healthy Development of Non-hydropower Renewable Energy Generation [21]. The document stipulates that the whole life cycle of BEG projects can be subsidized for 82 500 hours. After 15 years from the date of grid connection, it no longer enjoys the financial subsidies and TGCs will be issued to allow participation in the TGC market for transactions. At present, there is little research on the participation of BEG enterprises in TGC trading and it is necessary to study it. Biomass energy is the fourth-largest energy after oil, coal and natural gas, and has the characteristics of being low-carbon, clean and renewable [22]. It will account for 20% of the global energy supply in 2050 [23]. Among the biomass energy utilization technologies, BEG technology is one of the most promising utilization technologies with good environmental benefits [24]. Compared with coal-fired electricity plants containing carbon capture and storage (CCS), biomass-based electricity plants containing CCS can reduce carbon emissions by 769 kg/MWh [25]. However, due to the high acquisition cost of biomass raw materials, the low electricity-generation rate of the equipment and imperfect related supporting policies, the cost of BEG is high, so biomass energy is still unable to compete with fossil energy [26]. Under the FIT policy, BEG projects have reasonable profit margins [27]. However, with the expansion of the BEG scale, China’s financial subsidy burden is increasingly heavy—even a huge subsidy gap. As of 2017, the subsidy gap of the BEG industry in China had reached 14.364 billion RMB [28]. The subsidy has become an important factor in restricting the development of the BEG industry and affecting the profit and loss of enterprises. The participation of BEG projects in the TGC market may provide new ideas for solving these problems. Through the above analysis, we put forward the following four questions. Against the background of the introduction of new policies, what problems need to be solved in the TGC market and the electricity market? What impact will the transfer of the demand subject of TGC to the electricity-selling side have on each market subject? Will the participation of BEG projects in the TGC market be conducive to the development of the BEG industry? How will different key factors affect each market subject? To complement existing research, this paper attempts to answer these questions and make contributions through the following work and innovation: (i) We analyse the current TGC market and electricity market, and find that the TGC transaction rate is low and there is a double-metering problem for the consumption of RE. To address these issues, this paper constructs a coupling market transaction structure and a market equilibrium model between the TGC market and the electricity market under RPS. (ii) This paper takes the electricity retailers (ERs) as the main demand subject for TGC and coal-fired electricity enterprises are no longer required. We also consider the introduction of BEG enterprises into TGC trading. In the competition of various types of electricity-generation enterprises, to study the interaction between green-certificate trading and electricity trading, and to make clear whether the TGC trading is conducive to promoting the development of BEG enterprises and the impact on other market subjects. (iii) We analyse the key factors affecting the profits of each market subject, including the RECRW, TGC price, subsidy price and BEG cost, which will affect the traded volume of electricity and TGC. So as to put forward policy recommendations for China’s RPS and TGC trading mechanism. The structure of this paper is as follows. Section 1 sets the market transaction structure of the electricity market and the TGC market. Section 2 establishes a multi-objective market equilibrium model with the optimal profits of each market subject. Section 3 carries out case analysis, presents the simulation results of primary parameters and conducts a sensitivity analysis of market subjects’ profits. Section 4 carries out a discussion. Conclusions and policy recommendations are shown in Section 5. 1 Market transaction structure At present, there is a dual measurement problem of RE consumption in the electricity market, which means that RE and its corresponding TGC can complete the RECRW. In addition, the TGC market has a low turnover rate. According to the data of the China Tradable Green Certificate Certification Platform [29], as of 28 May 2022, a total of 43.51 million TGCs have been issued in China, with a cumulative number of 8.01 million registered and a cumulative turnover of 2.01 million. The turnover rate is only 25.1%, which is not conducive to improving the awareness of RE consumption and the enthusiasm of RE-generation projects to participate in the TGC market. Since both electricity consumers and electricity sellers are the demand subjects of electricity and TGC, to simplify the research, this paper only considers the electricity sellers as the demand subject. To solve the above two types of problems, this paper sets up the market transaction structure of the electricity market and the TGC market under the RPS, as shown in Fig. 1. Fig. 1: Open in new tabDownload slide Market transaction structure under the RPS. Under this market transaction structure, this paper proposes the following five assumptions. First, to simplify the study, this paper assumes that the market transaction structure includes only two major types of trading entities, namely electricity producers and ERs, wherein electricity producers include fuel-based electricity producers (FEPs) and non-water renewable-energy producers. The latter include WEPs, PVEPs and biomass-based electricity producers (BEPs), all of them referred to as green electricity producers (GEPs). In China, hydropower producers do not have access to TGC, so we do not consider it to better analyse the impact of the TGC market on the electricity market. Moreover, in other scholars’ studies, hydropower producers were also not considered [30, 31]. Second, to solve the dual measurement problem of RE consumption, this paper separates the physical attributes and environmental attributes of RE and represents them by the electricity itself and the TGC connected with the electricity respectively. The electricity and the TGC are traded separately. That is to say, assuming that all electricity only has physical attributes and cannot complete the RECRW, the ERs can only purchase TGC in the TGC market to complete the RECRW. Third, considering the different electricity-generation costs of different types of GEPs, to encourage the balanced development of various types of GEPs, it is assumed that the number of TGCs obtained by each GEP is multiplied by the actual electricity generation and the technical type coefficient ε [17]. Based on the production of 1 MWh of green electricity to obtain a TGC, different types of GEPs can obtain ε TGC for each 1 MWh of green electricity according to their technical type coefficient ε. Fourth, to simplify the research, it is assumed that the transaction is a day-ahead electricity spot market clearing. The ERs predict the electricity demand through the consumers’ typical load curve and announce the electricity demand in the electricity market. According to the demand and the situation of their generating units, each electricity producer reports the tradable volume and trading price of electricity and TGC. The ERs purchase electricity in the electricity market to meet the load demand of users and purchases TGC in the TGC market to meet the RECRW. Finally, assuming that the RECRW of ERs is α, the proportion of the electricity connected with the purchase of TGC to the total purchase of electricity is not <α. Each purchase of TGC is equivalent to the consumption of 1 MWh of green electricity. The completion of the RE consumption responsibility of each trading cycle is strictly assessed by the government to ensure the feasibility of the trading mechanism. If ERs cannot satisfy the RECRW, then each lack of a TGC will require a penalty of β USD. 2 Market equilibrium model 2.1 Cost function of each generator (i) Assume that the generating cost of FEPs i in the trading period time t is Cci,t ⁠, as shown in Equation (1) [32, 33], where a1 ⁠, b1 and c1 represent the cost coefficients and qci,t represents the amount of trading electricity of FEPs: Cci,t=a1qci,t2+b1qci,t+c1(1) (ii) Assume that the number of WEPs, PVEPs and BEPs among GEPs j is n1 ⁠, n2 and n3 ⁠, respectively (⁠ n1+n2+n3=n ⁠), and their generating cost in the trading period time t is Cwj,t ⁠, Cpj,t and Cbj,t ⁠, respectively, as shown in Equations (2)–(4) [32, 33] where ak ⁠, bk and ck (⁠ k=2,3,4 ⁠) represent the cost coefficients, and qwj,t ⁠, qpj,t and qbj,t represent the amount of trading electricity, respectively: Cwj,t=a2qwj,t2+b2qwj,t+c2(2) Cpj,t=a3qpj,t2+b3qpj,t+c3(3) Cbj,t=a4qbj,t2+b4qbj,t+c4(4) 2.2 Equilibrium model 2.2.1 Objective function The objective function of the equilibrium model is to maximize the profits of all subjects in each trading period. (i) FEPs mainly obtain profit through the sale of electricity. Its profit is the revenue from electricity sales minus the cost of electricity generation. Assume that the profit of FEPs in the trading period time t is πc,t and the trading price of electricity reported by FEPs i is pci,t ⁠. The optimal profit model is: max πc,t=pci,t∑mi=1qci,t−∑mi=1Cci,t(5) (ii) GEPs mainly obtain profit through the sale of electricity and TGC. From 2021, the central government will no longer subsidize new centralized photovoltaic-based electricity plants, commercial and industrial distributed photovoltaic-based electricity projects, and newly approved onshore wind-based electricity projects, and will implement grid parity [34]. Therefore, in the context of gradual subsidy withdrawal, this paper does not consider the subsidies for WEPs and PVEPs, and the profit of both is the revenue from the sales of electricity and TGC minus the cost of electricity generation. At present, the cost of BEG is still high, so BEPs are subsidized, but the electricity corresponding to TGC trading is no longer subsidized. Its profit is the revenue from the sales of electricity and TGC plus the subsidies corresponding to the electricity removed from the TGC trading, then minus its cost of electricity generation. Assume that the profit of GEPs in the trading period time t is πw,t ⁠, πp,t and πb,t ⁠, respectively, and the amount of TGC trading of GEPs j is qwj,tTGC ⁠, qpj,tTGC and qbj,tTGC ⁠, respectively; then the trading price of electricity and TGC reported by GEPs j is pwj,t ⁠, ppj,t ⁠, pbj,t ⁠, pwj,tTGC ⁠, ppj,tTGC and pbj,tTGC ⁠, respectively. The unit price of electricity-generation subsidy for BEPs is psub in USD/MWh and the profit model of the three types of GEPs is: max πw,t=∑n1j=1pwj,tqwj,t+∑n1j=1pwj,tTGCqwj,tTGC−∑n1j=1Cwj,t(6) max πp,t=∑n2j=1ppj,tqpj,t+∑n2j=1ppj,tTGCqpj,tTGC−∑n2j=1Cpj,t(7) max πb,t=∑n3j=1pbj,tqbj,t+∑n3j=1pbj,tTGCqbj,tTGC+psub∑n3j=1(qbj,t−qbj,tTGC/ε)−∑n3j=1Cbj,t(8) (iii) The ERs obtain profits by selling electricity to customers and their profit is the revenue from the sales of electricity minus the penalties and the cost of purchasing TGC and electricity. Assume that the profit of ERs in the trading period time t is πs,t ⁠, the selling price of electricity is ps ⁠, the trading volume of electricity and TGC with GEPs j is qgj,t and qgj,tTGC ⁠, respectively, and the volume of TGC that ERs do not meet the RECRW is qβTGC ⁠. The optimal profit model is: max πs,t=(ps−pci,t)∑mi=1qci,t+(ps−pgj,t)∑nj=1qgj,t−∑nj=1pj,tTGCqgj,tTGC−βqβTGC(9) 2.2.2 Constraint condition (i) Electricity balance constraint: for each trading period time t, the total amount of electricity purchased by the ERs is consistent with the customer load ut ⁠: ∑mi=1qci,t+∑nj=1qgj,t=ut(10) (ii) Generation output constraint: for each trading period time t, the tradable electricity of each GEP satisfies the maximum and minimum output of the generating units. The minimum and maximum generation capacity of the FEPs in trading period time t are minqci,t and maxqci,t ⁠, and those of the GEPs are minqgj,t and maxqgj,t ⁠, respectively: min qci,t≤qci,t≤max qci,t(11) min qgj,t≤qgj,t≤max qgj,t(12) (iii) Quotation constraint: since both electricity and TGC are quoted transactions, to ensure the profits of all subjects, the electricity and TGC quotations of each electricity producer are constrained: min pelectricity≤pelectricity≤max pelectricity(13) min pTGC≤pTGC≤max pTGC(14) (iv) TGC trading volume constraint: for each trading period time t, the number of TGC traded by each GEP does not exceed the total amount available to it: 0≤qgj,tTGC≤ε ∑tt=1qgj,t−∑t−1t=1qgj,tTGC(15) (v) RECRW constraint: for the trading period time t, the proportion of electricity corresponding to the amount of TGC purchased by the ERs to the total electricity sales is not <α: α≤(∑nj=1qgj,tTGC+qβTGC)/(∑mi=1qci,t+∑nj=1qgj,t)(16) 2.3 Nash equilibrium analysis For the trading period time t, each electricity producer reports their trading volume and trading price of electricity and TGC, and the cost of each electricity producer can be derived from Equations (1)–(4). The ERs purchase electricity and TGC for their own best profit and the profit of each electricity producer and ERs is derived from Equations (5)–(8). Based on the above model, the electricity producer can continuously change their strategies of quoted trading volume and trading price to gain more profit. In this non-cooperative game model, S={qci,t,pci,t,qgj,t,pgj,t,qgj,tTGC,pgj,tTGC,qβTGC}is the strategy set of each market subject. For any game party, each market subject has no incentive to change its strategy under a certain strategy set S∗ ⁠, which means that each market subject reaches its optimal profit maxπ and the market reaches Nash equilibrium. The strategy set S∗ is called the Nash equilibrium solution, i.e. it satisfies: π(S)≤π(S∗)(17) 2.4 Model-solving method The algorithm steps for solving the Nash equilibrium solution for each trading period time t are: (i) Each electricity producer reports the trading volume and trading price of electricity and TGC to obtain the strategy set S={qci,t,pci,t,qgj,t,pgj,t,qgj,tTGC,pgj,tTGC,qβTGC} ⁠. (ii) Calculate the cost of each electricity producer. (iii) Calculate the profit set π(S)={πc,t,πw,t,πp,t,πb,t,πs,t} for each electricity producer and ERs. (iv) Each electricity producer modifies the trading volume and trading price to obtain a new strategy set S ′ ={qci,t ′ ,pci,t ′ ,qgj,t ′ ,pgj,t ′ ,qgj,tTGC ′ ,pgj,tTGC ′ ,qβTGC ′ } and a new profit set π(S ′ ) ⁠, and repeat steps (ii) and (iii). (v) If the profit of a market subject in π(S′) is greater than the profit in π(S) ⁠, assign π(S′)and S′ to π(S) and S′ ⁠, and return to step (iv). (vi) If the profit increment of each market subject in π(S′) is <λ (λ is a very small number), output the strategy set S and the profit set π(S) at this trading period time, otherwise return to step (iv). In this paper, the particle swarm optimization (PSO) algorithm is selected for simulation. The PSO algorithm has an efficient global search capability and is suitable for handling multiple types of objective functions and constraints. It is conducive to obtaining a Nash equilibrium solution under multiple objectives and has been widely used in the research of other scholars in the field, such as [31, 35]. Some scholars also use a genetic algorithm (GA) to solve this type of problem [36]. In the PSO algorithm, all particles will preserve the information of good solutions. However, in the GA algorithm, as the population changes, the information of previous solutions will be destroyed, so the solution speed of PSO is theoretically faster. In addition, the PSO algorithm is easier to implement as it does not require operations such as crossover and variation in the coding process. Therefore, the PSO algorithm is chosen in this study. The PSO algorithm starts from a stochastic solution and finds the optimal solution by iteration. Each particle k is a potential solution to the optimization problem with a fitness value pk determined by the objective function. During the iterative process, the particles’ search direction and distance are controlled by speed v. At the beginning of the algorithm, particles are randomly generated, and the individual extreme value pbest of each particle and the global extreme value gbest of the whole population are searched. Each particle updates its speed and fitness value according to the following equations, and during the iteration, the particles are continuously updated to find a better pbest and gbest ⁠. Suppose w is the inertia weight, i.e. the tendency of the particle to maintain its previous speed; l1 and l2 are the learning factors, which denote the tendency of the particle to approach the pbest and the gbest of the population, respectively: vk′=w∗vk+l1∗rand(pbest−pk)+l2∗rand(gbest−pk)(18) pk′=pk+vk′(19) 3 Case analysis 3.1 Case parameter setting It is assumed that one FEP, one WEP, one PVEP, one BEP and one ER participate in the market transaction in a certain market, and the economic technical parameters of the electricity producers are shown in Table 1 [17, 33, 37]. Typical load forecasting curves of customers on a trading day are shown in Fig. 2 and the generation output forecasts for WEPs and PVEPs are shown in Fig. 3 [38]. Since the outputs of FEPs and BEPs are more stable, their maximum output is their rated installed capacity. Based on the RECRW set by each province (district and city) in China in the relevant documents of the National Development and Reform Commission, the RECRW α of the ERs in this paper is 15% [20], penalty β is set at 74.45 USD per certificate, the generation subsidy of BEPs is set at 52.14 USD/MWh and the electricity sales price is 77.44 USD/MWh. Table 1: The economic technical parameters of the electricity producers Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Open in new tab Table 1: The economic technical parameters of the electricity producers Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Open in new tab Fig. 2: Open in new tabDownload slide Typical load forecasting curves of customers on a trading day. Fig. 3: Open in new tabDownload slide Generation output forecasts for WEPs and PVEPs. From 10 May 2020 to 10 May 2022, on the website of the TGC platform, the average price of TGC of WEPs and PVEPs was 19.49 and 9.74 USD per certificate, respectively, and we set the quotation range of electricity and TGC of each electricity producer as shown in Table 2. In the PSO algorithm of this case, the initial number of particles in the population is 1000, the maximum number of iterations is 1000, w is 0.5, l1 and l2 are 2.0, and the optimization simulation is performed in the MATLAB platform. Table 2: The quotation range of electricity and TGC of each electricity producer Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Open in new tab Table 2: The quotation range of electricity and TGC of each electricity producer Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Open in new tab 3.2 Model validation Based on the above parameters and data, the market equilibrium model proposed in this paper is verified and the Nash equilibrium solution is obtained by simulation. The traded electricity of each electricity producer is shown in Fig. 4, the traded TGC of each GEP is shown in Fig. 5 and the profit of each market subject is shown in Table 3. Table 3: The profit of each market subject Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Open in new tab Table 3: The profit of each market subject Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Open in new tab Fig. 4: Open in new tabDownload slide The traded electricity of each electricity producer. Fig. 5: Open in new tabDownload slide The traded TGC of each GEP. As can be seen from Fig. 4, the BEPs have lower generation output during the trading periods of 10:00–19:00 and the reason for this can be seen by analysing Fig. 5. WEPs and PVEPs get the priority trading right of electricity and TGC due to their price advantage, and the amount of TGC purchased by ERs from these two major GEPs during this period time can basically meet the RECRW, which makes the traded TGC of BEPs lower. The biomass generation output is at a low level because of the high cost of biomass generation and the weak competitiveness with FEPs. However, biomass generation output increased during the periods of 0:00–9:00 and 20:00–24:00. The reason is that during these trading periods, WEPs and PVEPs are not enough to meet the market demand for TGC and the ERs must purchase additional TGC from BEPs to fulfil the RECRW, which makes the traded TGC of BEPs increase, thus contributing to the increase in biomass generation output. The above discussion shows that there is a strong correlation between biomass generation output and the supply and demand of TGC in the market. In addition, under the market transaction structure established in this paper, all GEPs can obtain 2.359 thousand TGC and trade 1.957 thousand TGC, with a turnover rate of 82.97%, which effectively improves the trading efficiency of the TGC market. Table 3 shows that even though the cost of BEPs is still high at present, its profit per unit of electricity generation is the highest. Assuming the same amount of electricity traded of BEPs with or without the participation of TGC trading, Table 4 shows the profits in the two scenarios. From Table 4, it can be seen that the profit gained by BEPs participating in TGC trading increases by 9081.07 USD compared to the profit gained without participating in TGC trading, up 142.29%. From the profit model of BEPs (Equation (8)), it is clear that its profit is related to the technical type coefficient ε. In this case, BEPs can still enjoy part of the generation subsidy even if it participates in the TGC trading, which makes their profit increase more. Table 5 shows the comparison of the profits of BEPs with and without TGC trading under different technical type coefficients ε. Table 4: The profits of BEPs with or without the participation of TGC trading TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 Open in new tab Table 4: The profits of BEPs with or without the participation of TGC trading TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 Open in new tab Table 5: The profits of BEPs with and without TGC trading under different technical type coefficients ε Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Open in new tab Table 5: The profits of BEPs with and without TGC trading under different technical type coefficients ε Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Open in new tab As can be seen from Table 5, as the coefficient ε decreases, the profit of BEPs remains the same if BEPs do not participate in TGC trading, while it is gradually decreasing if BEPs participate in TGC trading and the annual subsidy reduction is gradually increasing. However, even when the coefficient ε decreases to 1, i.e. the same as the coefficient of WEPs and PVEPs, the profit of BEPs participating in the TGC trading increases by 8.37% compared to the profit of BEPs not participating in the TGC trading. This indicates that the introduction of BEG projects into the TGC trading can bring them greater profits. Therefore, BEPs are more likely to participate in TGC trading than to obtain government subsidies. Based on the 50-MW generating unit in this paper, if 492 TGC are sold on average per day, the corresponding 328 MWh of electricity can reduce the government’s financial subsidies by 6.24 million USD per year at a subsidized unit price of 350 RMB/MWh. According to the data published by the NEA, the installed capacity of BEG in China reached 29.52 million kW in 2020 [39] and if all of them are included in the TGC transaction, the government’s financial subsidies can be reduced by 3.68 billion USD per year, which is ~81.38% of the total annual subsidy funds for BEG [40]. Therefore, including BEG projects in TGC trading can not only greatly reduce the financial burden of the government, effectively alleviating the problem of subsidy arrears, but also promote the development of China’s BEG industry. 3.3 Sensitivity analysis of profits In this coupled market, the profit of each market subject is influenced by many factors. The government can guide each electricity producer to adjust its trading strategy by adjusting the RECRW, TGC price and electricity subsidy price, so as to promote the healthy development of the TGC market and electricity market. In addition, the cost of electricity generation from biomass is gradually decreasing, which has a large impact on the revenue of BEPs. Therefore, this paper focuses on four major factors: RECRW, TGC price and electricity subsidy price, as well as the cost of BEG. 3.3.1 Impact of RECRW When RECRW is 13%, 14%, 15%, 16% and 17%, respectively, the traded electricity, TGC turnover and profit of each market subject are as shown in Fig. 6a–c. Fig. 6: Open in new tabDownload slide (a) The traded electricity under different RECRW. (b) The traded TGC under different RECRW. (c) The profit of each market subject under different RECRW. From Fig. 6a and b, it can be seen that with the increase in RECRW, the traded electricity and TGC of BEPs increase significantly, while the traded electricity of FEPs decreases slightly, and the traded electricity and TGC of WEPs and PVEPs remain almost unchanged. As the increase in RECRW will increase the demand for TGC in the market, based on the installed capacity of each electricity producer remaining unchanged, BEPs can obtain a larger market share without affecting the consumption of renewable electricity from wind electricity and photovoltaic electricity, which is conducive to the promotion of China’s safeguard mechanism for renewable-energy consumption. From Fig. 6c, it can be seen that the profits of WEPs and PVEPs do not change obviously under different RECRW. With the increase in traded volume, the profit of BEPs increases significantly, whereas the FEPs’ profits slightly decrease due to the decrease in traded electricity. The ERs’ profit decreases due to the need to purchase more TGC. The results show that the increase in RECRW will greatly improve the enthusiasm of BEPs to participate in the electricity market and TGC market, which is conducive to improving the current situation of the generally low profit of BEPs and promoting their rapid development. To this end, under the premise of maintaining the profits of each market subject, the government needs to reasonably formulate the RECRW for the installed capacity ratio of RE in each region. According to the simulation results of this paper, the RECRW in the range of 14–16% is comparatively reasonable. 3.3.2 Impact of TGC price and electricity subsidy price of BEPs Since BEG projects are not currently included in TGC trading and there is a lack of data on the TGC price of BEPs in the market, the impact of the TGC price and electricity subsidy price of BEPs on the traded volume and profit of each market subject is analysed. Fig. 7a and b shows the volume of traded electricity and traded TGC, and the profit of each market subject when the TGC prices are in the range of [37.23,40.20], [44.67,47.65] and [52.12,55.09] (USD per certificate). Fig. 7c and d shows the volume of traded electricity and traded TGC and the profit of each market subject when the electricity subsidy price is 37.23, 44.67 and 52.12 USD/MWh, respectively. Fig. 7e compares the extent of the impact on the BEPs’ profit under the equal decreases in the TGC price and electricity subsidy price. Fig. 7: Open in new tabDownload slide Open in new tabDownload slide (a) The volume of traded electricity and traded TGC under different TGC prices. (b) The profit of each market subject under different TGC prices. (c) The volume of traded electricity and traded TGC under different electricity subsidy prices. (d) The profit of each market subject under different electricity subsidy prices. (e) BEPs’ profit under the equal decreases in TGC price and electricity subsidy price. From Fig. 7a and c, it can be seen that the changes in the TGC price of BEPs as well as the electricity subsidy price hardly affect the volume of traded electricity and traded TGC of each market subject. This indicates that the changes in the TGC price and electricity subsidy price of BEPs do not change the priority relationship of the trading rights of each market subject. From Fig. 7b, it can be seen that as the TGC price of BEPs increases, the profit of BEPs gradually increases, the profit of ERs gradually decreases and the profit of the rest of the market subjects does not change significantly. From Fig. 7d, it can be seen that with the increase in the electricity subsidy price of BEPs, the profit of BEPs is gradually increasing and the profit changes of the rest of the market subjects are not obvious. From Fig. 7e, it can be seen that although the increase in both the TGC price and electricity subsidy price will make the profit of BEPs increase, the TGC price results in a greater impact, which can be explained by BEPs benefiting more from TGC trading than from subsidies. Therefore, to protect the profit of the biomass-based electricity industry, the government or the relevant industry should set a reasonable macro-guidance price for TGC trading, so that the TGC price fluctuates within a certain range. In addition, the government should implement a gradual reduction in electricity subsidies for BEPs, rather than a one-size-fits-all policy. 3.3.3 Impact of BEG cost With the continuous improvement in BEG technology, the BEG cost will be reduced to a certain extent. After analysis, the cost coefficient b4 has the highest degree of influence on the cost of electricity generation, so we analyse the traded volume of electricity and TGC and the profit of each market subject under the cost coefficient b4 of 104.23, 96.79 and 89.34 (unit: USD/MWh), as shown in Fig. 8a and b. Fig. 8: Open in new tabDownload slide (a) The volume of traded electricity and traded TGC under different BEG costs. (b) The profit of each market subject under different electricity subsidy prices. As can be seen from Fig. 8a, the decrease in the BEG cost hardly affects the amount of traded electricity and traded TGC of each market subject. Because the feedstock cost of BEG is increasing, the overall electricity-generation cost has limited scope for reduction [28]. Although the BEG cost has been reduced, it is still higher than that of the other three types of electricity producer, so the priority relationship of the trading rights of each electricity producer has not changed. As shown in Fig. 8b, the profit of BEPs increases significantly with the decrease in BEG cost and hardly affects the profit of the remaining market subjects. Therefore, it is one of the important initiatives for BEPs to reduce their electricity-generation costs to improve their profit. 3.4 Robustness analysis of the algorithm The PSO algorithm, as an intelligent algorithm, can obtain the optimal solution by expanding the path of finding the optimal solution. In the iterative process, the number of population particles will have an impact on its convergence performance and the robustness of the algorithm needs to be tested. For the convenience of analysis, the profit of BEPs is chosen as the fitness function in this paper and the remaining parameters remain unchanged. Fig. 9 shows the convergence results of the algorithm when the number of population particles is 250, 500, 750 and 1000, respectively. It can be seen that the volatility of searching for the optimal solution decreases as the number of population particles increases. And the algorithm can converge to a stable equilibrium solution after a certain number of iterations, regardless of the value of the number of population particles. Therefore, the PSO algorithm has comparatively high robustness. Fig. 9: Open in new tabDownload slide The PSO algorithm’s convergence performance under different numbers of population particles. 4 Discussion For the problem of the low turnover rate of TGC in the TGC market, we propose a market transaction structure and market equilibrium model, and the TGC and electricity are traded separately. It greatly improves the turnover rate of TGC and effectively promotes the development of the TGC market, while ensuring the consumption of RE. This is consistent with Song et al.’s [41] conclusion. For suppliers in the TGC market, previous studies generally only considered WEPs and PVEPs, and this paper adds BEPs on this basis. Compared with not participating in TGC trading, BEPs’ profits have been effectively improved. In Li et al.’s [31] study, participation in TGC transactions can also increase the profits of GEPs. In GEPs, BEPs have the highest unit profit, followed by WEPs and PVEPs. However, in Li et al.’s [31] study, the profit of PVEPs is higher than that of WEPs, which is different from the results of this paper. Because in our study, according to the actual TGC market, the TGC prices of different GEPs take different values, while in Li et al.’s [31] study, they take the same values, which leads to different conclusions, and the results of this paper are more relevant to the actual situation. This paper also discusses the impacts of the RPS quota, TGC price and electricity subsidy prices on market-trading volumes and the profits of market subjects. An appropriate increase in the RPS quota can increase the electricity traded volume and TGC traded volume of BEPs, thus increasing their profits, and it is more appropriate to keep the quota in the range of 14–16%. This is consistent with the findings of Song et al. [41]. However, higher quotas can cause a greater loss of profits for ERs, so formulating quota planning goals should be reasonable. As suggested by Zhang et al. [8], when formulating quota planning goals, the government needs to consider the relation between the RPS quota, the installed capacity and the TGC-supply/demand/price. In this paper, the TGC price cap is set so that the cost of meeting the RECRW of the ERs does not increase too much, which promotes the development of the TGC market to some extent. The change in the TGC price has little effect on the generation capacity of each electricity producer, while the study by Zhao et al. [15] shows that the total generation capacity will increase with the increase in the TGC benchmark price. This is because they treat FEPs as the demanders of TGC and thus the TGC price affects the generation capacity of each electricity producer. In contrast, in this paper, ERs are treated as the demand subject of TGC, and electricity and TGC are sold separately. The change in the TGC price of BEPs does not change the priority of transactions, so it does not change the volume of electricity and TGC traded, but only affects the profit. The profits of BEPs are gradually increasing as the electricity subsidy price rises. For the same electricity subsidy price and TGC price increase, the TGC price leads to a higher profit increase. This shows that replacing the FIT subsidy policy with the RPS policy is a correct and feasible decision. However, under the circumstances that the FIT subsidy has not been completely removed, the TGC market should be developed in an orderly manner [42]. The unit penalty is usually assumed to be equal to the TGC price cap [9, 10] but Fang et al. [43] showed that the unit penalty should be appropriately higher than the TGC price cap, which can induce a more efficient TGC market. We also set the unit penalty in this way and the results show that ERs are more willing to purchase TGC than to pay higher price penalties, which increases the turnover rate of TGC and promotes the development of the TGC market. There are some limitations of our work that can provide directions for future research. The first limitation is that we only focused on the day-ahead electricity spot trading. Electricity trading also includes medium-term and long-term contract trading and future trading, and these trading methods also have different impacts on the trading volume, trading price and profit of each market subject in the electricity market and TGC market. The second limitation is that we only consider ERs as the demand subjects of TGC. Electricity consumers who purchase electricity through the wholesale electricity market and enterprises with captive electricity plants are also the demand subjects of TGC. This will increase the demand for TGC and affect the trading results of the electricity market and the TGC market. In addition, the coupling relationship between carbon trading as a means of reducing carbon emissions and TGC trading and electricity trading is an interesting and worthy research issue. 5 Conclusions and recommendations Based on the analysis of the policies related to RE-generation projects and the current situation of the TGC and electricity market, we constructed a coupling market transaction structure and a market equilibrium model between the TGC market and the electricity market under RPS. We also consider the introduction of BEG enterprises into TGC trading. In the competition of various types of electricity-generation enterprises, to study the interaction between green-certificate trading and electricity trading, and to make clear whether the TGC trading is conducive to promoting the development of BEG enterprises and the impact on other market subjects, in the end, we analysed the key factors affecting the profits of each market subject. The main findings are as follows: (i) The market transaction structure proposed in this paper effectively solves the current dual measurement problem of RE consumption, and the turnover rate of TGC in this paper reaches 82.97%, which greatly improves the efficiency of the TGC market and provides a guarantee for BEPs to obtain additional revenue from the TGC market to cover its high generation costs. (ii) The case study verifies the effectiveness and rationality of the market equilibrium model and optimization algorithm established in this paper. The results show that increased demand for TGC will increase demand for RE electricity. By including BEG projects in the TGC market, BEPs can earn higher profits with TGC transactions than with government subsidies. When the technical type coefficient ε of BEPs is in the range of 1–1.5, the participation in TGC trading increases BEPs’ profit by 8.37–142.29% and reduces the burden of government financial subsidies by 3.68–5.52 billion USD compared to non-TGC trading. This will increase the enthusiasm of BEPs to participate in the electricity market and the TGC market, which is conducive to the rapid development of China’s BEG industry. (iii) The analysis results of profit-influencing factors show that the increase in RECRW makes the TGC demand increase, thus increasing the BEPs’ traded volume of electricity and TGC, which greatly improves BEPs’ profits and does not affect the renewable-energy consumption of WEPs and PVEPs. Changes in the TGC price and subsidy price of BEPs and their electricity-generation costs hardly affect the traded volume of electricity and TGC of each market subject but have an impact on the profit of BEPs. Among these factors, the decrease in TGC prices and subsidy prices makes BEPs’ profits decrease and the degree of impact of the TGC price is greater, while the decrease in electricity-generation costs makes BEPs’ profits increase. In response to the conclusions drawn in this paper, the following policy recommendations are proposed: (i) The government should allow BEG projects to participate in the TGC market as early as possible. At present, although BEG projects still enjoy the electricity subsidy, the government must introduce feasible alternatives to the subsidy policy as the amount of electricity subsidy gradually declines. This paper shows that BEG projects can benefit more from participating in TGC trading than from the electricity subsidy. Therefore, the government should approve BEG projects that meet production emission standards to participate in the TGC market as early as possible, so that the electricity subsidy policy and TGC trading policy can go hand in hand, thus promoting a virtuous cycle of the BEG industry, while also reducing government financial subsidy. (ii) Let the market play a decisive role and the government macro-regulates the market. In the TGC market and electricity market structure constructed in this paper, the revenue of each market subject is influenced by various factors, among which the market price plays a key role. To ensure the normal operation of the market, the government needs to provide macro guidance to it. At present, the price of TGC remains high for a long time and the government needs to set a reasonable guideline price for TGC trading. In addition, there are differences in different RE-generation technologies and to encourage their reasonable competition, the government needs to set a reasonable technical type coefficient ε for RE-generation projects. (iii) RPS mechanism needs to be tailored to local conditions and the relevant parameters need to be reasonably set. The parameter RECRW determines the amount of RE consumption. The government needs to reasonably formulate the RECRW of each region according to the proportion of installed capacity of RE in each region to ensure the stable development of RE projects in each region. Subsequently, we will study the carbon emission reduction benefits of the RE projects, explore the feasibility and revenue status of their participation in carbon trading and electricity-generation rights trading, and coordinate the three types of trading mechanisms in conjunction with TGC trading, to help the RE industry, especially the BEG industry, to obtain greater revenue and promote the development and growth of the RE industry. Acknowledgements K.L.: conceptualization, methodology, software, validation, writing—original draft preparation. Z.L.: visualization, data curation, writing—reviewing and editing. Z.T.: methodology, software, supervision. Funding This research did not receive any grant from funding agencies in the public, commercial or not-for-profit sectors. Conflict of interest statement None declared. References [1] Li LL , Taeihagh A. An in-depth analysis of the evolution of the policy mix for the sustainable energy transition in China from 1981 to 2020 . Appl Energy , 2020 , 263 : 114611 . Google Scholar Crossref Search ADS WorldCat [2] Liu CC , Li N, Zha DL. On the impact of FIT policies on renewable energy investment: Based on the solar power support policies in China’s power market . Renew Energy , 2016 , 94 : 251 – 267 . Google Scholar Crossref Search ADS WorldCat [3] Xiao MZ , Simon S, Pregger T. Scenario analysis of energy system transition: a case study of two coastal metropolitan regions, eastern China . Energy Strategy Rev , 1004 , 2019 : 23 . Google Scholar OpenURL Placeholder Text WorldCat [4] China’s renewable energy power generation installed capacity in 2021. China, 29 November 2021. http://www.gov.cn/xinwen/2021-11/29/content_5653908.htm ( 29 May 2022 , date last accessed). [5] Yu BY , Zhao ZH, Zhao GP, et al. Provincial renewable energy dispatch optimization in line with Renewable Portfolio Standard policy in China . Renew Energy , 2021 , 174 : 236 – 252 . Google Scholar Crossref Search ADS WorldCat [6] Currier K , MA . regulatory adjustment process for the determination of the optimal percentage requirement in an electricity market with Tradable Green Certificates . Energy Policy , 2013 , 62 : 1053 – 1057 . Google Scholar Crossref Search ADS WorldCat [7] Ciarreta A , Espinosa MP, Pizarro-Irizar C. Optimal regulation of renewable energy: a comparison of feed-in tariffs and tradable green certificates in the Spanish electricity system . Energy Econ , 2017 , 67 : 387 – 399 . Google Scholar Crossref Search ADS WorldCat [8] Zhang LB , Chen CQ, Wang QW, et al. The impact of feed-in tariff reduction and renewable portfolio standard on the development of distributed photovoltaic generation in China . Energy , 2021 , 232 : 120933 . Google Scholar Crossref Search ADS WorldCat [9] Pineda S , Bock A. Renewable-based generation expansion under a green certificate market . Renew Energy , 2016 , 91 : 53 – 63 . Google Scholar Crossref Search ADS WorldCat [10] Zhang YZ , Zhao XG, Ren LZ, et al. The development of China’s biomass power industry under feed-in tariff and renewable portfolio standard: a system dynamics analysis . Energy , 2017 , 139 : 947 – 961 . Google Scholar OpenURL Placeholder Text WorldCat [11] Dong Y , Shimada K. Evolution from the renewable portfolio standards to feed-in tariff for the deployment of renewable energy in Japan . Renew Energy , 2017 , 107 : 590 – 596 . Google Scholar Crossref Search ADS WorldCat [12] Zhou Y , Zhao XG, Jia XF, et al. Can the renewable portfolio standards improve social welfare in China’s electricity market? . Energy Policy , 2021 , 9 : 112242 . Google Scholar OpenURL Placeholder Text WorldCat [13] Liu DN , Wang WY, Li H, et al. Joint optimization of quota policy design and electric market behavior based on renewable portfolio standard in China . IEEE Access , 2021 , 9 : 113347 – 113361 . Google Scholar Crossref Search ADS WorldCat [14] Wolfgang O , Jaehnert S, Mo B. Methodology for forecasting in the Swedish–Norwegian market for el-certificates . Energy , 2015 , 88 : 322 – 333 . Google Scholar Crossref Search ADS WorldCat [15] Zhao XG , Zhou Y, Zuo Y, et al. Research on optimal benchmark price of tradable green certificate based on system dynamics: a China perspective . J Clean Prod , 2019 , 230 : 241 – 252 . Google Scholar Crossref Search ADS WorldCat [16] Zuo Y , Zhao XG, Meng X, et al. Research on tradable green certificate benchmark price and technical conversion coefficient: bargaining-based cooperative trading . Energy , 2020 , 208 : 118376 . Google Scholar OpenURL Placeholder Text WorldCat [17] Zhao XG , Xu L, Zhou Y. How to promote the effective implementation of China’s renewable portfolio standards considering non-neutral technology? Energy , 2022 , 238 : 121748 . Google Scholar OpenURL Placeholder Text WorldCat [18] Song XH , Han JJ, Shan YQ, et al. Efficiency of tradable green certificate markets in China . J Clean Prod , 2020 , 264 : 121518 . Google Scholar Crossref Search ADS WorldCat [19] Zhou Y , Zhao XG, Wang Z. Demand side incentive under renewable portfolio standards: a system dynamics analysis . Energy Policy , 2020 , 114 : 111652 . Google Scholar OpenURL Placeholder Text WorldCat [20] National Energy Administration. Notice on the establishment and improvement of the renewable energy power consumption guarantee mechanism. China, 10 May 2019. http://zfxxgk.nea.gov.cn/auto87/201905/t20190515_3662.htm ( 29 May 2022 , date last accessed). [21] National Energy Administration. Supplementary notice on matters relating to certain opinions on promoting the healthy development of non-water renewable energy power generation. China, 29 September 2020. http://jjs.mof.gov.cn/zhengcefagui/202010/t20201015_3604104.htm. ( 29 May 2022 , date last accessed). [22] National Energy Administration. Notice of the NEA on printing and distributing the 13th five-year plan for biomass energy development. China, 6 December 2016. http://www.gov.cn/xinwen/2016-12/06/content_5143612.htm ( 29 May 2022 , date last accessed). [23] IEA. Bioenergy Annual Report 2021. April 2022. https://www.ieabioenergy.com/blog/publications/iea-bioenergy-annual-report-2021/ ( 29 May 2022 , date last accessed). [24] Lin BQ , He JX. Is biomass power a good choice for governments in China? Renew Sustain Energy Rev , 2017 , 73 : 1218 – 1230 . Google Scholar Crossref Search ADS WorldCat [25] Mohamed U , Zhao YJ, Yi Q, et al. Evaluation of life cycle energy, economy and CO2 emissions for biomass chemical looping gasification to power generation . Renew Energy , 2021 , 176 : 366 – 387 . Google Scholar Crossref Search ADS WorldCat [26] Liu DN , Liu MG, Xiao BW, et al. Exploring biomass power generation’s development under encouraged policies in China . J Clean Prod , 2020 , 258 : 120786 . Google Scholar Crossref Search ADS WorldCat [27] Zhao XG , Wang JY, Liu XM, et al. China’s wind, biomass and solar power generation: what the situation tells us? . Renew Sustain Energy Rev , 2012 , 16 : 6173 – 6182 . Google Scholar OpenURL Placeholder Text WorldCat [28] Research Report on biomass electricity price policy. BEIPA, November 2018. http://www.cn-bea.com/filedownload/230676 ( 29 May 2022 , date last accessed). [29] China Tradable Green Certificate Certification Platform. http://www.greenenergy.org.cn/ ( 29 May 2022 , date last accessed). [30] Sun Y , Ling J, Qin Y, et al. A bidding optimization method for renewable energy cross-regional transaction under green certificate trading mechanism . Renewable Energy Resources , 2018 , 36 : 942 – 948 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [31] Li YC , Han CY, Xiao YW, et al. Tradable green certificate market transaction based on economic scheduling timing simulation of renewable energy . Smart Power , 2021 , 49 : 58 – 65 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [32] An X , Zhang S, Li X, et al. Two-stage joint equilibrium model of electricity market with tradable green certificates . Trans Inst Meas Control , 2019 , 41 : 1615 – 1626 . Google Scholar Crossref Search ADS WorldCat [33] Xiao Y , Wang XF, Wang XL, et al. Behavior analysis of wind power producer in electricity market . Appl Energy , 2016 , 171 : 325 – 335 . Google Scholar Crossref Search ADS WorldCat [34] National Development and Reform Commission. Notice of the NDRC on matters related to the new energy on grid tariff policy in the 2021 WWW Document. China, 7 June 2021. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202106/t20210611_1283088.html?code=&state=123 ( 29 May 2022 , date last accessed). [35] Zhu JZ , Feng YQ, Xie PP, et al. Equilibrium model of Chinese electricity market considering renewable portfolio standard . Automation of Electric Power Systems , 2019 , 43 : 168 – 175 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [36] Liu YM , Chen HY, Huang L, et al. Equilibrium model of electricity market based on multi-swarm co-evolution . Power System Protection and Control , 2020 , 48 : 38 – 45 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [37] An XN , Zhang SH, Li X. Equilibrium analysis of oligopolistic electricity markets considering tradable green certificates . Automation of Electric Power Systems , 2017 , 41 : 84 – 89 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [38] Sun YQ , Ling J, Qin YH, et al. A bidding optimization method for renewable energy cross-regional transaction under green certificate trading mechanism . Renewable Energy Resources , 2018 , 36 : 942 – 948 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [39] A press conference on China’s renewable energy development. China, 30 March 2021. http://www.nea.gov.cn/2021-03/30/c_139846095.htm ( 29 May 2022 , date last accessed). [40] Where will the biomass power generation industry go in 2020? 6 January 2020. http://www.forestry.gov.cn/zlszz/4264/20200106/164859172722924.html ( 29 May 2022 , date last accessed). [41] Song XH , Han JJ, Zhang L, et al. Impacts of renewable portfolio standards on multi-market coupling trading of renewable energy in China: a scenario-based system dynamics model . Energy Policy , 2021 , 159 : 112647 . Google Scholar Crossref Search ADS WorldCat [42] Dong ZJ , Yu XY, Chang CT, et al. How does feed-in tariff and renewable portfolio standard evolve synergistically? An integrated approach of tripartite evolutionary game and system dynamics . Renew Energy , 2022 , 186 : 864 – 877 . Google Scholar Crossref Search ADS WorldCat [43] Fang DB , Zhao CY, Kleit AN. The impact of the under enforcement of RPS in China: an evolutionary approach . Energy Policy , 2019 , 135 : 111021 . Google Scholar Crossref Search ADS WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Clean Energy Oxford University Press

How will tradable green certificates affect electricity trading markets under renewable portfolio standards? A China perspective

Clean Energy , Volume 6 (4): 14 – Aug 1, 2022

How will tradable green certificates affect electricity trading markets under renewable portfolio standards? A China perspective

Clean Energy , Volume 6 (4): 14 – Aug 1, 2022

Abstract

Abstract Renewable portfolio standards (RPS) are important guarantees to promote renewable energy (RE) consumption. The tradable green certificate (TGC) trading mechanism is a supporting mechanism of RPS, but the rate of TGC trading is low and there is a double-metering problem of RE consumption. With the introduction of new policies in China, we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation (BEG) projects are considered to participate in TGC trading. To explore the interaction between the TGC market and the electricity market, this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium model. The established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are analysed. The main conclusions are as follows. (i) The established market structure and model effectively solve the double-metering problem of RE consumption, making the TGC turnover rate reach 82.97 %, greatly improving the market efficiency. (ii) Increased demand for TGC will increase demand for RE electricity. The participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers (BEPs), reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based electricity. This will help promote the rapid development of China’s RE, especially the BEG industry. (iii) Among the influencing factors, the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of BEPs. The decline in TGC price and subsidy price will reduce the profit of BEPs. Finally, we put forward policy recommendations for China’s RPS and TGC trading mechanism. This study can provide a reference for the construction of China’s TGC market and electricity market and the development of RE. Open in new tabDownload slide tradable green certificate market, electricity market, renewable portfolio standards, biomass-based electricity generation, particle swarm optimization algorithm Introduction Promoting the development of renewable energy (RE) is crucial for accelerating the clean-energy revolution [1, 2]. Since the promulgation of the Renewable Energy Law of the People’s Republic of China in 2006, China has issued a series of policies to promote the development of the RE industry, mainly including feed-in tariff (FIT) and renewable portfolio standards (RPS) policies [3]. The implementation of FIT causes the installed power capacity of RE to increase rapidly. In 2021, the installed renewable power capacity accounted for 43.5% of the total installed power capacity in China [4]. But FIT also imposes a huge financial burden on the government. RPS is a mandatory policy instrument adopted by the government to nurture the RE market and bring RE generation to a guaranteed minimum level. From 2018 to 2020, China has set specific RPS targets for each province for both total RE-generation targets and non-hydropower targets. The RPS is seen as an important long-term guarantee to promote RE consumption [5]. The tradable green certificate (TGC) trading mechanism is the supporting mechanism of RPS. The TGC trading mechanism is a market-based subsidy scheme designed to promote the development of RE-generation projects [6]. China set up RPS to promote China’s RE consumption, the purchase of a TGC equivalent to complete 1 MWh of RE consumption. China’s TGC trading is also aimed at reducing RE subsidies, as RE used for TGC trading is no longer subsidized. Therefore, theoretically carrying out TGC transactions can promote the consumption of RE and reduce the burden of national financial subsidies. At present, many scholars have used a variety of methods to study the impact of FIT and RPS mechanisms on the RE-generation industry, including electricity prices, investment incentives, the profits of market subjects and social welfare. Ciarreta et al. [7] believed that the incentive FIT policy cannot reflect electricity market conditions or price signals and when the scale of RE-generation projects is obvious, it will bring a huge economic burden to consumers. Zhang et al. [8] showed that RPS can effectively help China’s distributed photovoltaic generation to achieve grid parity after the cancellation of electricity price subsidies. Pineda and Bock [9] believed that in the TGC market, increasing quota obligations and appropriate penalties for irregularities would encourage investment in RE-generation projects. Zhang et al. [10] showed that RPS could improve the installed capacity and profit of biomass-based electricity generation (BEG) in a fully competitive market, but the impact on other electricity market subjects was not analysed. Dong and Shimada [11] indicated that under the RPS and FIT mechanism, RE consumption in the electricity market would increase and non-renewable-energy enterprises’ revenues would decrease. Zhou et al. [12] found that when the parameter, i.e. renewable-energy consumption responsibility weight (RECRW), is within a certain range, the social welfare under the RPS mechanism is always higher than that under the FIT policy. Liu et al. [13] showed that the market competition force of RE producers was still guaranteed even if the subsidy policy of the electricity price was abolished. The above studies show that RPS will better promote the development of RE and enhance the profits of market subjects and social welfare than the FIT policy. Since TGC trading is closely related to electricity trading, the coupling effect of the TGC market and electricity market has been studied by many scholars, mainly including the determination of the TGC price, the efficiency of the TGC market and TGC trading. Wolfgang et al. [14] proposed a method to predict short-term and medium-term TGC prices to assist investment decisions in RE projects. Zhao et al. [15] analysed the interaction between the TGC market and the electricity market. Based on the maximization of social welfare, the optimal benchmark price of TGC is 0.06 USD/kWh. Based on the Nash bargaining model, Zuo et al. [16] obtained the benchmark price of China’s TGC as 49.59 USD/MWh. Due to the technical differences of different RE generation, Zhao et al. [17] considered the key parameter of technology conversion coefficient in the transaction, which can effectively promote the TGC transaction. Song et al. [18] found that the operation efficiency of most TGC markets in China was not high, and suggested unifying the goals of regional development and electricity-generation enterprises to encourage market subjects to participate in TGC transactions. Compared with the benchmark on-grid price, the benefits of electricity plants under the marketed on-grid price are higher, which can effectively stimulate coal-fired electricity plants to increase the consumption demand of TGC [19]. The above studies show that the TGC market and the electricity market interact, but the efficiency of China’s TGC market is low and most studies take coal-fired electricity enterprises as the demand subject of TGC. On 10 May 2019, the National Energy Administration (NEA) issued the Notice on Establishing and Improving the Safeguard Mechanism for Renewable Energy Consumption, which stipulates that the responsibility for RE consumption is assumed jointly by electricity sellers and electricity consumers [20]. This indicates that the subject of RE consumption responsibility is transferred from the electricity-generation side to the electricity-selling side. In addition, the suppliers of TGC in the above studies only include wind-based electricity producers (WEPs) and photovoltaic-based electricity producers (PVEPs). On 29 September 2020, the NEA supplemented Some Opinions on Promoting the Healthy Development of Non-hydropower Renewable Energy Generation [21]. The document stipulates that the whole life cycle of BEG projects can be subsidized for 82 500 hours. After 15 years from the date of grid connection, it no longer enjoys the financial subsidies and TGCs will be issued to allow participation in the TGC market for transactions. At present, there is little research on the participation of BEG enterprises in TGC trading and it is necessary to study it. Biomass energy is the fourth-largest energy after oil, coal and natural gas, and has the characteristics of being low-carbon, clean and renewable [22]. It will account for 20% of the global energy supply in 2050 [23]. Among the biomass energy utilization technologies, BEG technology is one of the most promising utilization technologies with good environmental benefits [24]. Compared with coal-fired electricity plants containing carbon capture and storage (CCS), biomass-based electricity plants containing CCS can reduce carbon emissions by 769 kg/MWh [25]. However, due to the high acquisition cost of biomass raw materials, the low electricity-generation rate of the equipment and imperfect related supporting policies, the cost of BEG is high, so biomass energy is still unable to compete with fossil energy [26]. Under the FIT policy, BEG projects have reasonable profit margins [27]. However, with the expansion of the BEG scale, China’s financial subsidy burden is increasingly heavy—even a huge subsidy gap. As of 2017, the subsidy gap of the BEG industry in China had reached 14.364 billion RMB [28]. The subsidy has become an important factor in restricting the development of the BEG industry and affecting the profit and loss of enterprises. The participation of BEG projects in the TGC market may provide new ideas for solving these problems. Through the above analysis, we put forward the following four questions. Against the background of the introduction of new policies, what problems need to be solved in the TGC market and the electricity market? What impact will the transfer of the demand subject of TGC to the electricity-selling side have on each market subject? Will the participation of BEG projects in the TGC market be conducive to the development of the BEG industry? How will different key factors affect each market subject? To complement existing research, this paper attempts to answer these questions and make contributions through the following work and innovation: (i) We analyse the current TGC market and electricity market, and find that the TGC transaction rate is low and there is a double-metering problem for the consumption of RE. To address these issues, this paper constructs a coupling market transaction structure and a market equilibrium model between the TGC market and the electricity market under RPS. (ii) This paper takes the electricity retailers (ERs) as the main demand subject for TGC and coal-fired electricity enterprises are no longer required. We also consider the introduction of BEG enterprises into TGC trading. In the competition of various types of electricity-generation enterprises, to study the interaction between green-certificate trading and electricity trading, and to make clear whether the TGC trading is conducive to promoting the development of BEG enterprises and the impact on other market subjects. (iii) We analyse the key factors affecting the profits of each market subject, including the RECRW, TGC price, subsidy price and BEG cost, which will affect the traded volume of electricity and TGC. So as to put forward policy recommendations for China’s RPS and TGC trading mechanism. The structure of this paper is as follows. Section 1 sets the market transaction structure of the electricity market and the TGC market. Section 2 establishes a multi-objective market equilibrium model with the optimal profits of each market subject. Section 3 carries out case analysis, presents the simulation results of primary parameters and conducts a sensitivity analysis of market subjects’ profits. Section 4 carries out a discussion. Conclusions and policy recommendations are shown in Section 5. 1 Market transaction structure At present, there is a dual measurement problem of RE consumption in the electricity market, which means that RE and its corresponding TGC can complete the RECRW. In addition, the TGC market has a low turnover rate. According to the data of the China Tradable Green Certificate Certification Platform [29], as of 28 May 2022, a total of 43.51 million TGCs have been issued in China, with a cumulative number of 8.01 million registered and a cumulative turnover of 2.01 million. The turnover rate is only 25.1%, which is not conducive to improving the awareness of RE consumption and the enthusiasm of RE-generation projects to participate in the TGC market. Since both electricity consumers and electricity sellers are the demand subjects of electricity and TGC, to simplify the research, this paper only considers the electricity sellers as the demand subject. To solve the above two types of problems, this paper sets up the market transaction structure of the electricity market and the TGC market under the RPS, as shown in Fig. 1. Fig. 1: Open in new tabDownload slide Market transaction structure under the RPS. Under this market transaction structure, this paper proposes the following five assumptions. First, to simplify the study, this paper assumes that the market transaction structure includes only two major types of trading entities, namely electricity producers and ERs, wherein electricity producers include fuel-based electricity producers (FEPs) and non-water renewable-energy producers. The latter include WEPs, PVEPs and biomass-based electricity producers (BEPs), all of them referred to as green electricity producers (GEPs). In China, hydropower producers do not have access to TGC, so we do not consider it to better analyse the impact of the TGC market on the electricity market. Moreover, in other scholars’ studies, hydropower producers were also not considered [30, 31]. Second, to solve the dual measurement problem of RE consumption, this paper separates the physical attributes and environmental attributes of RE and represents them by the electricity itself and the TGC connected with the electricity respectively. The electricity and the TGC are traded separately. That is to say, assuming that all electricity only has physical attributes and cannot complete the RECRW, the ERs can only purchase TGC in the TGC market to complete the RECRW. Third, considering the different electricity-generation costs of different types of GEPs, to encourage the balanced development of various types of GEPs, it is assumed that the number of TGCs obtained by each GEP is multiplied by the actual electricity generation and the technical type coefficient ε [17]. Based on the production of 1 MWh of green electricity to obtain a TGC, different types of GEPs can obtain ε TGC for each 1 MWh of green electricity according to their technical type coefficient ε. Fourth, to simplify the research, it is assumed that the transaction is a day-ahead electricity spot market clearing. The ERs predict the electricity demand through the consumers’ typical load curve and announce the electricity demand in the electricity market. According to the demand and the situation of their generating units, each electricity producer reports the tradable volume and trading price of electricity and TGC. The ERs purchase electricity in the electricity market to meet the load demand of users and purchases TGC in the TGC market to meet the RECRW. Finally, assuming that the RECRW of ERs is α, the proportion of the electricity connected with the purchase of TGC to the total purchase of electricity is not <α. Each purchase of TGC is equivalent to the consumption of 1 MWh of green electricity. The completion of the RE consumption responsibility of each trading cycle is strictly assessed by the government to ensure the feasibility of the trading mechanism. If ERs cannot satisfy the RECRW, then each lack of a TGC will require a penalty of β USD. 2 Market equilibrium model 2.1 Cost function of each generator (i) Assume that the generating cost of FEPs i in the trading period time t is Cci,t ⁠, as shown in Equation (1) [32, 33], where a1 ⁠, b1 and c1 represent the cost coefficients and qci,t represents the amount of trading electricity of FEPs: Cci,t=a1qci,t2+b1qci,t+c1(1) (ii) Assume that the number of WEPs, PVEPs and BEPs among GEPs j is n1 ⁠, n2 and n3 ⁠, respectively (⁠ n1+n2+n3=n ⁠), and their generating cost in the trading period time t is Cwj,t ⁠, Cpj,t and Cbj,t ⁠, respectively, as shown in Equations (2)–(4) [32, 33] where ak ⁠, bk and ck (⁠ k=2,3,4 ⁠) represent the cost coefficients, and qwj,t ⁠, qpj,t and qbj,t represent the amount of trading electricity, respectively: Cwj,t=a2qwj,t2+b2qwj,t+c2(2) Cpj,t=a3qpj,t2+b3qpj,t+c3(3) Cbj,t=a4qbj,t2+b4qbj,t+c4(4) 2.2 Equilibrium model 2.2.1 Objective function The objective function of the equilibrium model is to maximize the profits of all subjects in each trading period. (i) FEPs mainly obtain profit through the sale of electricity. Its profit is the revenue from electricity sales minus the cost of electricity generation. Assume that the profit of FEPs in the trading period time t is πc,t and the trading price of electricity reported by FEPs i is pci,t ⁠. The optimal profit model is: max πc,t=pci,t∑mi=1qci,t−∑mi=1Cci,t(5) (ii) GEPs mainly obtain profit through the sale of electricity and TGC. From 2021, the central government will no longer subsidize new centralized photovoltaic-based electricity plants, commercial and industrial distributed photovoltaic-based electricity projects, and newly approved onshore wind-based electricity projects, and will implement grid parity [34]. Therefore, in the context of gradual subsidy withdrawal, this paper does not consider the subsidies for WEPs and PVEPs, and the profit of both is the revenue from the sales of electricity and TGC minus the cost of electricity generation. At present, the cost of BEG is still high, so BEPs are subsidized, but the electricity corresponding to TGC trading is no longer subsidized. Its profit is the revenue from the sales of electricity and TGC plus the subsidies corresponding to the electricity removed from the TGC trading, then minus its cost of electricity generation. Assume that the profit of GEPs in the trading period time t is πw,t ⁠, πp,t and πb,t ⁠, respectively, and the amount of TGC trading of GEPs j is qwj,tTGC ⁠, qpj,tTGC and qbj,tTGC ⁠, respectively; then the trading price of electricity and TGC reported by GEPs j is pwj,t ⁠, ppj,t ⁠, pbj,t ⁠, pwj,tTGC ⁠, ppj,tTGC and pbj,tTGC ⁠, respectively. The unit price of electricity-generation subsidy for BEPs is psub in USD/MWh and the profit model of the three types of GEPs is: max πw,t=∑n1j=1pwj,tqwj,t+∑n1j=1pwj,tTGCqwj,tTGC−∑n1j=1Cwj,t(6) max πp,t=∑n2j=1ppj,tqpj,t+∑n2j=1ppj,tTGCqpj,tTGC−∑n2j=1Cpj,t(7) max πb,t=∑n3j=1pbj,tqbj,t+∑n3j=1pbj,tTGCqbj,tTGC+psub∑n3j=1(qbj,t−qbj,tTGC/ε)−∑n3j=1Cbj,t(8) (iii) The ERs obtain profits by selling electricity to customers and their profit is the revenue from the sales of electricity minus the penalties and the cost of purchasing TGC and electricity. Assume that the profit of ERs in the trading period time t is πs,t ⁠, the selling price of electricity is ps ⁠, the trading volume of electricity and TGC with GEPs j is qgj,t and qgj,tTGC ⁠, respectively, and the volume of TGC that ERs do not meet the RECRW is qβTGC ⁠. The optimal profit model is: max πs,t=(ps−pci,t)∑mi=1qci,t+(ps−pgj,t)∑nj=1qgj,t−∑nj=1pj,tTGCqgj,tTGC−βqβTGC(9) 2.2.2 Constraint condition (i) Electricity balance constraint: for each trading period time t, the total amount of electricity purchased by the ERs is consistent with the customer load ut ⁠: ∑mi=1qci,t+∑nj=1qgj,t=ut(10) (ii) Generation output constraint: for each trading period time t, the tradable electricity of each GEP satisfies the maximum and minimum output of the generating units. The minimum and maximum generation capacity of the FEPs in trading period time t are minqci,t and maxqci,t ⁠, and those of the GEPs are minqgj,t and maxqgj,t ⁠, respectively: min qci,t≤qci,t≤max qci,t(11) min qgj,t≤qgj,t≤max qgj,t(12) (iii) Quotation constraint: since both electricity and TGC are quoted transactions, to ensure the profits of all subjects, the electricity and TGC quotations of each electricity producer are constrained: min pelectricity≤pelectricity≤max pelectricity(13) min pTGC≤pTGC≤max pTGC(14) (iv) TGC trading volume constraint: for each trading period time t, the number of TGC traded by each GEP does not exceed the total amount available to it: 0≤qgj,tTGC≤ε ∑tt=1qgj,t−∑t−1t=1qgj,tTGC(15) (v) RECRW constraint: for the trading period time t, the proportion of electricity corresponding to the amount of TGC purchased by the ERs to the total electricity sales is not <α: α≤(∑nj=1qgj,tTGC+qβTGC)/(∑mi=1qci,t+∑nj=1qgj,t)(16) 2.3 Nash equilibrium analysis For the trading period time t, each electricity producer reports their trading volume and trading price of electricity and TGC, and the cost of each electricity producer can be derived from Equations (1)–(4). The ERs purchase electricity and TGC for their own best profit and the profit of each electricity producer and ERs is derived from Equations (5)–(8). Based on the above model, the electricity producer can continuously change their strategies of quoted trading volume and trading price to gain more profit. In this non-cooperative game model, S={qci,t,pci,t,qgj,t,pgj,t,qgj,tTGC,pgj,tTGC,qβTGC}is the strategy set of each market subject. For any game party, each market subject has no incentive to change its strategy under a certain strategy set S∗ ⁠, which means that each market subject reaches its optimal profit maxπ and the market reaches Nash equilibrium. The strategy set S∗ is called the Nash equilibrium solution, i.e. it satisfies: π(S)≤π(S∗)(17) 2.4 Model-solving method The algorithm steps for solving the Nash equilibrium solution for each trading period time t are: (i) Each electricity producer reports the trading volume and trading price of electricity and TGC to obtain the strategy set S={qci,t,pci,t,qgj,t,pgj,t,qgj,tTGC,pgj,tTGC,qβTGC} ⁠. (ii) Calculate the cost of each electricity producer. (iii) Calculate the profit set π(S)={πc,t,πw,t,πp,t,πb,t,πs,t} for each electricity producer and ERs. (iv) Each electricity producer modifies the trading volume and trading price to obtain a new strategy set S ′ ={qci,t ′ ,pci,t ′ ,qgj,t ′ ,pgj,t ′ ,qgj,tTGC ′ ,pgj,tTGC ′ ,qβTGC ′ } and a new profit set π(S ′ ) ⁠, and repeat steps (ii) and (iii). (v) If the profit of a market subject in π(S′) is greater than the profit in π(S) ⁠, assign π(S′)and S′ to π(S) and S′ ⁠, and return to step (iv). (vi) If the profit increment of each market subject in π(S′) is <λ (λ is a very small number), output the strategy set S and the profit set π(S) at this trading period time, otherwise return to step (iv). In this paper, the particle swarm optimization (PSO) algorithm is selected for simulation. The PSO algorithm has an efficient global search capability and is suitable for handling multiple types of objective functions and constraints. It is conducive to obtaining a Nash equilibrium solution under multiple objectives and has been widely used in the research of other scholars in the field, such as [31, 35]. Some scholars also use a genetic algorithm (GA) to solve this type of problem [36]. In the PSO algorithm, all particles will preserve the information of good solutions. However, in the GA algorithm, as the population changes, the information of previous solutions will be destroyed, so the solution speed of PSO is theoretically faster. In addition, the PSO algorithm is easier to implement as it does not require operations such as crossover and variation in the coding process. Therefore, the PSO algorithm is chosen in this study. The PSO algorithm starts from a stochastic solution and finds the optimal solution by iteration. Each particle k is a potential solution to the optimization problem with a fitness value pk determined by the objective function. During the iterative process, the particles’ search direction and distance are controlled by speed v. At the beginning of the algorithm, particles are randomly generated, and the individual extreme value pbest of each particle and the global extreme value gbest of the whole population are searched. Each particle updates its speed and fitness value according to the following equations, and during the iteration, the particles are continuously updated to find a better pbest and gbest ⁠. Suppose w is the inertia weight, i.e. the tendency of the particle to maintain its previous speed; l1 and l2 are the learning factors, which denote the tendency of the particle to approach the pbest and the gbest of the population, respectively: vk′=w∗vk+l1∗rand(pbest−pk)+l2∗rand(gbest−pk)(18) pk′=pk+vk′(19) 3 Case analysis 3.1 Case parameter setting It is assumed that one FEP, one WEP, one PVEP, one BEP and one ER participate in the market transaction in a certain market, and the economic technical parameters of the electricity producers are shown in Table 1 [17, 33, 37]. Typical load forecasting curves of customers on a trading day are shown in Fig. 2 and the generation output forecasts for WEPs and PVEPs are shown in Fig. 3 [38]. Since the outputs of FEPs and BEPs are more stable, their maximum output is their rated installed capacity. Based on the RECRW set by each province (district and city) in China in the relevant documents of the National Development and Reform Commission, the RECRW α of the ERs in this paper is 15% [20], penalty β is set at 74.45 USD per certificate, the generation subsidy of BEPs is set at 52.14 USD/MWh and the electricity sales price is 77.44 USD/MWh. Table 1: The economic technical parameters of the electricity producers Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Open in new tab Table 1: The economic technical parameters of the electricity producers Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Open in new tab Fig. 2: Open in new tabDownload slide Typical load forecasting curves of customers on a trading day. Fig. 3: Open in new tabDownload slide Generation output forecasts for WEPs and PVEPs. From 10 May 2020 to 10 May 2022, on the website of the TGC platform, the average price of TGC of WEPs and PVEPs was 19.49 and 9.74 USD per certificate, respectively, and we set the quotation range of electricity and TGC of each electricity producer as shown in Table 2. In the PSO algorithm of this case, the initial number of particles in the population is 1000, the maximum number of iterations is 1000, w is 0.5, l1 and l2 are 2.0, and the optimization simulation is performed in the MATLAB platform. Table 2: The quotation range of electricity and TGC of each electricity producer Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Open in new tab Table 2: The quotation range of electricity and TGC of each electricity producer Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Open in new tab 3.2 Model validation Based on the above parameters and data, the market equilibrium model proposed in this paper is verified and the Nash equilibrium solution is obtained by simulation. The traded electricity of each electricity producer is shown in Fig. 4, the traded TGC of each GEP is shown in Fig. 5 and the profit of each market subject is shown in Table 3. Table 3: The profit of each market subject Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Open in new tab Table 3: The profit of each market subject Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Open in new tab Fig. 4: Open in new tabDownload slide The traded electricity of each electricity producer. Fig. 5: Open in new tabDownload slide The traded TGC of each GEP. As can be seen from Fig. 4, the BEPs have lower generation output during the trading periods of 10:00–19:00 and the reason for this can be seen by analysing Fig. 5. WEPs and PVEPs get the priority trading right of electricity and TGC due to their price advantage, and the amount of TGC purchased by ERs from these two major GEPs during this period time can basically meet the RECRW, which makes the traded TGC of BEPs lower. The biomass generation output is at a low level because of the high cost of biomass generation and the weak competitiveness with FEPs. However, biomass generation output increased during the periods of 0:00–9:00 and 20:00–24:00. The reason is that during these trading periods, WEPs and PVEPs are not enough to meet the market demand for TGC and the ERs must purchase additional TGC from BEPs to fulfil the RECRW, which makes the traded TGC of BEPs increase, thus contributing to the increase in biomass generation output. The above discussion shows that there is a strong correlation between biomass generation output and the supply and demand of TGC in the market. In addition, under the market transaction structure established in this paper, all GEPs can obtain 2.359 thousand TGC and trade 1.957 thousand TGC, with a turnover rate of 82.97%, which effectively improves the trading efficiency of the TGC market. Table 3 shows that even though the cost of BEPs is still high at present, its profit per unit of electricity generation is the highest. Assuming the same amount of electricity traded of BEPs with or without the participation of TGC trading, Table 4 shows the profits in the two scenarios. From Table 4, it can be seen that the profit gained by BEPs participating in TGC trading increases by 9081.07 USD compared to the profit gained without participating in TGC trading, up 142.29%. From the profit model of BEPs (Equation (8)), it is clear that its profit is related to the technical type coefficient ε. In this case, BEPs can still enjoy part of the generation subsidy even if it participates in the TGC trading, which makes their profit increase more. Table 5 shows the comparison of the profits of BEPs with and without TGC trading under different technical type coefficients ε. Table 4: The profits of BEPs with or without the participation of TGC trading TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 Open in new tab Table 4: The profits of BEPs with or without the participation of TGC trading TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 Open in new tab Table 5: The profits of BEPs with and without TGC trading under different technical type coefficients ε Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Open in new tab Table 5: The profits of BEPs with and without TGC trading under different technical type coefficients ε Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Open in new tab As can be seen from Table 5, as the coefficient ε decreases, the profit of BEPs remains the same if BEPs do not participate in TGC trading, while it is gradually decreasing if BEPs participate in TGC trading and the annual subsidy reduction is gradually increasing. However, even when the coefficient ε decreases to 1, i.e. the same as the coefficient of WEPs and PVEPs, the profit of BEPs participating in the TGC trading increases by 8.37% compared to the profit of BEPs not participating in the TGC trading. This indicates that the introduction of BEG projects into the TGC trading can bring them greater profits. Therefore, BEPs are more likely to participate in TGC trading than to obtain government subsidies. Based on the 50-MW generating unit in this paper, if 492 TGC are sold on average per day, the corresponding 328 MWh of electricity can reduce the government’s financial subsidies by 6.24 million USD per year at a subsidized unit price of 350 RMB/MWh. According to the data published by the NEA, the installed capacity of BEG in China reached 29.52 million kW in 2020 [39] and if all of them are included in the TGC transaction, the government’s financial subsidies can be reduced by 3.68 billion USD per year, which is ~81.38% of the total annual subsidy funds for BEG [40]. Therefore, including BEG projects in TGC trading can not only greatly reduce the financial burden of the government, effectively alleviating the problem of subsidy arrears, but also promote the development of China’s BEG industry. 3.3 Sensitivity analysis of profits In this coupled market, the profit of each market subject is influenced by many factors. The government can guide each electricity producer to adjust its trading strategy by adjusting the RECRW, TGC price and electricity subsidy price, so as to promote the healthy development of the TGC market and electricity market. In addition, the cost of electricity generation from biomass is gradually decreasing, which has a large impact on the revenue of BEPs. Therefore, this paper focuses on four major factors: RECRW, TGC price and electricity subsidy price, as well as the cost of BEG. 3.3.1 Impact of RECRW When RECRW is 13%, 14%, 15%, 16% and 17%, respectively, the traded electricity, TGC turnover and profit of each market subject are as shown in Fig. 6a–c. Fig. 6: Open in new tabDownload slide (a) The traded electricity under different RECRW. (b) The traded TGC under different RECRW. (c) The profit of each market subject under different RECRW. From Fig. 6a and b, it can be seen that with the increase in RECRW, the traded electricity and TGC of BEPs increase significantly, while the traded electricity of FEPs decreases slightly, and the traded electricity and TGC of WEPs and PVEPs remain almost unchanged. As the increase in RECRW will increase the demand for TGC in the market, based on the installed capacity of each electricity producer remaining unchanged, BEPs can obtain a larger market share without affecting the consumption of renewable electricity from wind electricity and photovoltaic electricity, which is conducive to the promotion of China’s safeguard mechanism for renewable-energy consumption. From Fig. 6c, it can be seen that the profits of WEPs and PVEPs do not change obviously under different RECRW. With the increase in traded volume, the profit of BEPs increases significantly, whereas the FEPs’ profits slightly decrease due to the decrease in traded electricity. The ERs’ profit decreases due to the need to purchase more TGC. The results show that the increase in RECRW will greatly improve the enthusiasm of BEPs to participate in the electricity market and TGC market, which is conducive to improving the current situation of the generally low profit of BEPs and promoting their rapid development. To this end, under the premise of maintaining the profits of each market subject, the government needs to reasonably formulate the RECRW for the installed capacity ratio of RE in each region. According to the simulation results of this paper, the RECRW in the range of 14–16% is comparatively reasonable. 3.3.2 Impact of TGC price and electricity subsidy price of BEPs Since BEG projects are not currently included in TGC trading and there is a lack of data on the TGC price of BEPs in the market, the impact of the TGC price and electricity subsidy price of BEPs on the traded volume and profit of each market subject is analysed. Fig. 7a and b shows the volume of traded electricity and traded TGC, and the profit of each market subject when the TGC prices are in the range of [37.23,40.20], [44.67,47.65] and [52.12,55.09] (USD per certificate). Fig. 7c and d shows the volume of traded electricity and traded TGC and the profit of each market subject when the electricity subsidy price is 37.23, 44.67 and 52.12 USD/MWh, respectively. Fig. 7e compares the extent of the impact on the BEPs’ profit under the equal decreases in the TGC price and electricity subsidy price. Fig. 7: Open in new tabDownload slide Open in new tabDownload slide (a) The volume of traded electricity and traded TGC under different TGC prices. (b) The profit of each market subject under different TGC prices. (c) The volume of traded electricity and traded TGC under different electricity subsidy prices. (d) The profit of each market subject under different electricity subsidy prices. (e) BEPs’ profit under the equal decreases in TGC price and electricity subsidy price. From Fig. 7a and c, it can be seen that the changes in the TGC price of BEPs as well as the electricity subsidy price hardly affect the volume of traded electricity and traded TGC of each market subject. This indicates that the changes in the TGC price and electricity subsidy price of BEPs do not change the priority relationship of the trading rights of each market subject. From Fig. 7b, it can be seen that as the TGC price of BEPs increases, the profit of BEPs gradually increases, the profit of ERs gradually decreases and the profit of the rest of the market subjects does not change significantly. From Fig. 7d, it can be seen that with the increase in the electricity subsidy price of BEPs, the profit of BEPs is gradually increasing and the profit changes of the rest of the market subjects are not obvious. From Fig. 7e, it can be seen that although the increase in both the TGC price and electricity subsidy price will make the profit of BEPs increase, the TGC price results in a greater impact, which can be explained by BEPs benefiting more from TGC trading than from subsidies. Therefore, to protect the profit of the biomass-based electricity industry, the government or the relevant industry should set a reasonable macro-guidance price for TGC trading, so that the TGC price fluctuates within a certain range. In addition, the government should implement a gradual reduction in electricity subsidies for BEPs, rather than a one-size-fits-all policy. 3.3.3 Impact of BEG cost With the continuous improvement in BEG technology, the BEG cost will be reduced to a certain extent. After analysis, the cost coefficient b4 has the highest degree of influence on the cost of electricity generation, so we analyse the traded volume of electricity and TGC and the profit of each market subject under the cost coefficient b4 of 104.23, 96.79 and 89.34 (unit: USD/MWh), as shown in Fig. 8a and b. Fig. 8: Open in new tabDownload slide (a) The volume of traded electricity and traded TGC under different BEG costs. (b) The profit of each market subject under different electricity subsidy prices. As can be seen from Fig. 8a, the decrease in the BEG cost hardly affects the amount of traded electricity and traded TGC of each market subject. Because the feedstock cost of BEG is increasing, the overall electricity-generation cost has limited scope for reduction [28]. Although the BEG cost has been reduced, it is still higher than that of the other three types of electricity producer, so the priority relationship of the trading rights of each electricity producer has not changed. As shown in Fig. 8b, the profit of BEPs increases significantly with the decrease in BEG cost and hardly affects the profit of the remaining market subjects. Therefore, it is one of the important initiatives for BEPs to reduce their electricity-generation costs to improve their profit. 3.4 Robustness analysis of the algorithm The PSO algorithm, as an intelligent algorithm, can obtain the optimal solution by expanding the path of finding the optimal solution. In the iterative process, the number of population particles will have an impact on its convergence performance and the robustness of the algorithm needs to be tested. For the convenience of analysis, the profit of BEPs is chosen as the fitness function in this paper and the remaining parameters remain unchanged. Fig. 9 shows the convergence results of the algorithm when the number of population particles is 250, 500, 750 and 1000, respectively. It can be seen that the volatility of searching for the optimal solution decreases as the number of population particles increases. And the algorithm can converge to a stable equilibrium solution after a certain number of iterations, regardless of the value of the number of population particles. Therefore, the PSO algorithm has comparatively high robustness. Fig. 9: Open in new tabDownload slide The PSO algorithm’s convergence performance under different numbers of population particles. 4 Discussion For the problem of the low turnover rate of TGC in the TGC market, we propose a market transaction structure and market equilibrium model, and the TGC and electricity are traded separately. It greatly improves the turnover rate of TGC and effectively promotes the development of the TGC market, while ensuring the consumption of RE. This is consistent with Song et al.’s [41] conclusion. For suppliers in the TGC market, previous studies generally only considered WEPs and PVEPs, and this paper adds BEPs on this basis. Compared with not participating in TGC trading, BEPs’ profits have been effectively improved. In Li et al.’s [31] study, participation in TGC transactions can also increase the profits of GEPs. In GEPs, BEPs have the highest unit profit, followed by WEPs and PVEPs. However, in Li et al.’s [31] study, the profit of PVEPs is higher than that of WEPs, which is different from the results of this paper. Because in our study, according to the actual TGC market, the TGC prices of different GEPs take different values, while in Li et al.’s [31] study, they take the same values, which leads to different conclusions, and the results of this paper are more relevant to the actual situation. This paper also discusses the impacts of the RPS quota, TGC price and electricity subsidy prices on market-trading volumes and the profits of market subjects. An appropriate increase in the RPS quota can increase the electricity traded volume and TGC traded volume of BEPs, thus increasing their profits, and it is more appropriate to keep the quota in the range of 14–16%. This is consistent with the findings of Song et al. [41]. However, higher quotas can cause a greater loss of profits for ERs, so formulating quota planning goals should be reasonable. As suggested by Zhang et al. [8], when formulating quota planning goals, the government needs to consider the relation between the RPS quota, the installed capacity and the TGC-supply/demand/price. In this paper, the TGC price cap is set so that the cost of meeting the RECRW of the ERs does not increase too much, which promotes the development of the TGC market to some extent. The change in the TGC price has little effect on the generation capacity of each electricity producer, while the study by Zhao et al. [15] shows that the total generation capacity will increase with the increase in the TGC benchmark price. This is because they treat FEPs as the demanders of TGC and thus the TGC price affects the generation capacity of each electricity producer. In contrast, in this paper, ERs are treated as the demand subject of TGC, and electricity and TGC are sold separately. The change in the TGC price of BEPs does not change the priority of transactions, so it does not change the volume of electricity and TGC traded, but only affects the profit. The profits of BEPs are gradually increasing as the electricity subsidy price rises. For the same electricity subsidy price and TGC price increase, the TGC price leads to a higher profit increase. This shows that replacing the FIT subsidy policy with the RPS policy is a correct and feasible decision. However, under the circumstances that the FIT subsidy has not been completely removed, the TGC market should be developed in an orderly manner [42]. The unit penalty is usually assumed to be equal to the TGC price cap [9, 10] but Fang et al. [43] showed that the unit penalty should be appropriately higher than the TGC price cap, which can induce a more efficient TGC market. We also set the unit penalty in this way and the results show that ERs are more willing to purchase TGC than to pay higher price penalties, which increases the turnover rate of TGC and promotes the development of the TGC market. There are some limitations of our work that can provide directions for future research. The first limitation is that we only focused on the day-ahead electricity spot trading. Electricity trading also includes medium-term and long-term contract trading and future trading, and these trading methods also have different impacts on the trading volume, trading price and profit of each market subject in the electricity market and TGC market. The second limitation is that we only consider ERs as the demand subjects of TGC. Electricity consumers who purchase electricity through the wholesale electricity market and enterprises with captive electricity plants are also the demand subjects of TGC. This will increase the demand for TGC and affect the trading results of the electricity market and the TGC market. In addition, the coupling relationship between carbon trading as a means of reducing carbon emissions and TGC trading and electricity trading is an interesting and worthy research issue. 5 Conclusions and recommendations Based on the analysis of the policies related to RE-generation projects and the current situation of the TGC and electricity market, we constructed a coupling market transaction structure and a market equilibrium model between the TGC market and the electricity market under RPS. We also consider the introduction of BEG enterprises into TGC trading. In the competition of various types of electricity-generation enterprises, to study the interaction between green-certificate trading and electricity trading, and to make clear whether the TGC trading is conducive to promoting the development of BEG enterprises and the impact on other market subjects, in the end, we analysed the key factors affecting the profits of each market subject. The main findings are as follows: (i) The market transaction structure proposed in this paper effectively solves the current dual measurement problem of RE consumption, and the turnover rate of TGC in this paper reaches 82.97%, which greatly improves the efficiency of the TGC market and provides a guarantee for BEPs to obtain additional revenue from the TGC market to cover its high generation costs. (ii) The case study verifies the effectiveness and rationality of the market equilibrium model and optimization algorithm established in this paper. The results show that increased demand for TGC will increase demand for RE electricity. By including BEG projects in the TGC market, BEPs can earn higher profits with TGC transactions than with government subsidies. When the technical type coefficient ε of BEPs is in the range of 1–1.5, the participation in TGC trading increases BEPs’ profit by 8.37–142.29% and reduces the burden of government financial subsidies by 3.68–5.52 billion USD compared to non-TGC trading. This will increase the enthusiasm of BEPs to participate in the electricity market and the TGC market, which is conducive to the rapid development of China’s BEG industry. (iii) The analysis results of profit-influencing factors show that the increase in RECRW makes the TGC demand increase, thus increasing the BEPs’ traded volume of electricity and TGC, which greatly improves BEPs’ profits and does not affect the renewable-energy consumption of WEPs and PVEPs. Changes in the TGC price and subsidy price of BEPs and their electricity-generation costs hardly affect the traded volume of electricity and TGC of each market subject but have an impact on the profit of BEPs. Among these factors, the decrease in TGC prices and subsidy prices makes BEPs’ profits decrease and the degree of impact of the TGC price is greater, while the decrease in electricity-generation costs makes BEPs’ profits increase. In response to the conclusions drawn in this paper, the following policy recommendations are proposed: (i) The government should allow BEG projects to participate in the TGC market as early as possible. At present, although BEG projects still enjoy the electricity subsidy, the government must introduce feasible alternatives to the subsidy policy as the amount of electricity subsidy gradually declines. This paper shows that BEG projects can benefit more from participating in TGC trading than from the electricity subsidy. Therefore, the government should approve BEG projects that meet production emission standards to participate in the TGC market as early as possible, so that the electricity subsidy policy and TGC trading policy can go hand in hand, thus promoting a virtuous cycle of the BEG industry, while also reducing government financial subsidy. (ii) Let the market play a decisive role and the government macro-regulates the market. In the TGC market and electricity market structure constructed in this paper, the revenue of each market subject is influenced by various factors, among which the market price plays a key role. To ensure the normal operation of the market, the government needs to provide macro guidance to it. At present, the price of TGC remains high for a long time and the government needs to set a reasonable guideline price for TGC trading. In addition, there are differences in different RE-generation technologies and to encourage their reasonable competition, the government needs to set a reasonable technical type coefficient ε for RE-generation projects. (iii) RPS mechanism needs to be tailored to local conditions and the relevant parameters need to be reasonably set. The parameter RECRW determines the amount of RE consumption. The government needs to reasonably formulate the RECRW of each region according to the proportion of installed capacity of RE in each region to ensure the stable development of RE projects in each region. Subsequently, we will study the carbon emission reduction benefits of the RE projects, explore the feasibility and revenue status of their participation in carbon trading and electricity-generation rights trading, and coordinate the three types of trading mechanisms in conjunction with TGC trading, to help the RE industry, especially the BEG industry, to obtain greater revenue and promote the development and growth of the RE industry. Acknowledgements K.L.: conceptualization, methodology, software, validation, writing—original draft preparation. Z.L.: visualization, data curation, writing—reviewing and editing. Z.T.: methodology, software, supervision. Funding This research did not receive any grant from funding agencies in the public, commercial or not-for-profit sectors. Conflict of interest statement None declared. References [1] Li LL , Taeihagh A. An in-depth analysis of the evolution of the policy mix for the sustainable energy transition in China from 1981 to 2020 . Appl Energy , 2020 , 263 : 114611 . Google Scholar Crossref Search ADS WorldCat [2] Liu CC , Li N, Zha DL. On the impact of FIT policies on renewable energy investment: Based on the solar power support policies in China’s power market . Renew Energy , 2016 , 94 : 251 – 267 . Google Scholar Crossref Search ADS WorldCat [3] Xiao MZ , Simon S, Pregger T. Scenario analysis of energy system transition: a case study of two coastal metropolitan regions, eastern China . Energy Strategy Rev , 1004 , 2019 : 23 . Google Scholar OpenURL Placeholder Text WorldCat [4] China’s renewable energy power generation installed capacity in 2021. China, 29 November 2021. http://www.gov.cn/xinwen/2021-11/29/content_5653908.htm ( 29 May 2022 , date last accessed). [5] Yu BY , Zhao ZH, Zhao GP, et al. Provincial renewable energy dispatch optimization in line with Renewable Portfolio Standard policy in China . Renew Energy , 2021 , 174 : 236 – 252 . Google Scholar Crossref Search ADS WorldCat [6] Currier K , MA . regulatory adjustment process for the determination of the optimal percentage requirement in an electricity market with Tradable Green Certificates . Energy Policy , 2013 , 62 : 1053 – 1057 . Google Scholar Crossref Search ADS WorldCat [7] Ciarreta A , Espinosa MP, Pizarro-Irizar C. Optimal regulation of renewable energy: a comparison of feed-in tariffs and tradable green certificates in the Spanish electricity system . Energy Econ , 2017 , 67 : 387 – 399 . Google Scholar Crossref Search ADS WorldCat [8] Zhang LB , Chen CQ, Wang QW, et al. The impact of feed-in tariff reduction and renewable portfolio standard on the development of distributed photovoltaic generation in China . Energy , 2021 , 232 : 120933 . Google Scholar Crossref Search ADS WorldCat [9] Pineda S , Bock A. Renewable-based generation expansion under a green certificate market . Renew Energy , 2016 , 91 : 53 – 63 . Google Scholar Crossref Search ADS WorldCat [10] Zhang YZ , Zhao XG, Ren LZ, et al. The development of China’s biomass power industry under feed-in tariff and renewable portfolio standard: a system dynamics analysis . Energy , 2017 , 139 : 947 – 961 . Google Scholar OpenURL Placeholder Text WorldCat [11] Dong Y , Shimada K. Evolution from the renewable portfolio standards to feed-in tariff for the deployment of renewable energy in Japan . Renew Energy , 2017 , 107 : 590 – 596 . Google Scholar Crossref Search ADS WorldCat [12] Zhou Y , Zhao XG, Jia XF, et al. Can the renewable portfolio standards improve social welfare in China’s electricity market? . Energy Policy , 2021 , 9 : 112242 . Google Scholar OpenURL Placeholder Text WorldCat [13] Liu DN , Wang WY, Li H, et al. Joint optimization of quota policy design and electric market behavior based on renewable portfolio standard in China . IEEE Access , 2021 , 9 : 113347 – 113361 . Google Scholar Crossref Search ADS WorldCat [14] Wolfgang O , Jaehnert S, Mo B. Methodology for forecasting in the Swedish–Norwegian market for el-certificates . Energy , 2015 , 88 : 322 – 333 . Google Scholar Crossref Search ADS WorldCat [15] Zhao XG , Zhou Y, Zuo Y, et al. Research on optimal benchmark price of tradable green certificate based on system dynamics: a China perspective . J Clean Prod , 2019 , 230 : 241 – 252 . Google Scholar Crossref Search ADS WorldCat [16] Zuo Y , Zhao XG, Meng X, et al. Research on tradable green certificate benchmark price and technical conversion coefficient: bargaining-based cooperative trading . Energy , 2020 , 208 : 118376 . Google Scholar OpenURL Placeholder Text WorldCat [17] Zhao XG , Xu L, Zhou Y. How to promote the effective implementation of China’s renewable portfolio standards considering non-neutral technology? Energy , 2022 , 238 : 121748 . Google Scholar OpenURL Placeholder Text WorldCat [18] Song XH , Han JJ, Shan YQ, et al. Efficiency of tradable green certificate markets in China . J Clean Prod , 2020 , 264 : 121518 . Google Scholar Crossref Search ADS WorldCat [19] Zhou Y , Zhao XG, Wang Z. Demand side incentive under renewable portfolio standards: a system dynamics analysis . Energy Policy , 2020 , 114 : 111652 . Google Scholar OpenURL Placeholder Text WorldCat [20] National Energy Administration. Notice on the establishment and improvement of the renewable energy power consumption guarantee mechanism. China, 10 May 2019. http://zfxxgk.nea.gov.cn/auto87/201905/t20190515_3662.htm ( 29 May 2022 , date last accessed). [21] National Energy Administration. Supplementary notice on matters relating to certain opinions on promoting the healthy development of non-water renewable energy power generation. China, 29 September 2020. http://jjs.mof.gov.cn/zhengcefagui/202010/t20201015_3604104.htm. ( 29 May 2022 , date last accessed). [22] National Energy Administration. Notice of the NEA on printing and distributing the 13th five-year plan for biomass energy development. China, 6 December 2016. http://www.gov.cn/xinwen/2016-12/06/content_5143612.htm ( 29 May 2022 , date last accessed). [23] IEA. Bioenergy Annual Report 2021. April 2022. https://www.ieabioenergy.com/blog/publications/iea-bioenergy-annual-report-2021/ ( 29 May 2022 , date last accessed). [24] Lin BQ , He JX. Is biomass power a good choice for governments in China? Renew Sustain Energy Rev , 2017 , 73 : 1218 – 1230 . Google Scholar Crossref Search ADS WorldCat [25] Mohamed U , Zhao YJ, Yi Q, et al. Evaluation of life cycle energy, economy and CO2 emissions for biomass chemical looping gasification to power generation . Renew Energy , 2021 , 176 : 366 – 387 . Google Scholar Crossref Search ADS WorldCat [26] Liu DN , Liu MG, Xiao BW, et al. Exploring biomass power generation’s development under encouraged policies in China . J Clean Prod , 2020 , 258 : 120786 . Google Scholar Crossref Search ADS WorldCat [27] Zhao XG , Wang JY, Liu XM, et al. China’s wind, biomass and solar power generation: what the situation tells us? . Renew Sustain Energy Rev , 2012 , 16 : 6173 – 6182 . Google Scholar OpenURL Placeholder Text WorldCat [28] Research Report on biomass electricity price policy. BEIPA, November 2018. http://www.cn-bea.com/filedownload/230676 ( 29 May 2022 , date last accessed). [29] China Tradable Green Certificate Certification Platform. http://www.greenenergy.org.cn/ ( 29 May 2022 , date last accessed). [30] Sun Y , Ling J, Qin Y, et al. A bidding optimization method for renewable energy cross-regional transaction under green certificate trading mechanism . Renewable Energy Resources , 2018 , 36 : 942 – 948 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [31] Li YC , Han CY, Xiao YW, et al. Tradable green certificate market transaction based on economic scheduling timing simulation of renewable energy . Smart Power , 2021 , 49 : 58 – 65 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [32] An X , Zhang S, Li X, et al. Two-stage joint equilibrium model of electricity market with tradable green certificates . Trans Inst Meas Control , 2019 , 41 : 1615 – 1626 . Google Scholar Crossref Search ADS WorldCat [33] Xiao Y , Wang XF, Wang XL, et al. Behavior analysis of wind power producer in electricity market . Appl Energy , 2016 , 171 : 325 – 335 . Google Scholar Crossref Search ADS WorldCat [34] National Development and Reform Commission. Notice of the NDRC on matters related to the new energy on grid tariff policy in the 2021 WWW Document. China, 7 June 2021. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202106/t20210611_1283088.html?code=&state=123 ( 29 May 2022 , date last accessed). [35] Zhu JZ , Feng YQ, Xie PP, et al. Equilibrium model of Chinese electricity market considering renewable portfolio standard . Automation of Electric Power Systems , 2019 , 43 : 168 – 175 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [36] Liu YM , Chen HY, Huang L, et al. Equilibrium model of electricity market based on multi-swarm co-evolution . Power System Protection and Control , 2020 , 48 : 38 – 45 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [37] An XN , Zhang SH, Li X. Equilibrium analysis of oligopolistic electricity markets considering tradable green certificates . Automation of Electric Power Systems , 2017 , 41 : 84 – 89 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [38] Sun YQ , Ling J, Qin YH, et al. A bidding optimization method for renewable energy cross-regional transaction under green certificate trading mechanism . Renewable Energy Resources , 2018 , 36 : 942 – 948 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [39] A press conference on China’s renewable energy development. China, 30 March 2021. http://www.nea.gov.cn/2021-03/30/c_139846095.htm ( 29 May 2022 , date last accessed). [40] Where will the biomass power generation industry go in 2020? 6 January 2020. http://www.forestry.gov.cn/zlszz/4264/20200106/164859172722924.html ( 29 May 2022 , date last accessed). [41] Song XH , Han JJ, Zhang L, et al. Impacts of renewable portfolio standards on multi-market coupling trading of renewable energy in China: a scenario-based system dynamics model . Energy Policy , 2021 , 159 : 112647 . Google Scholar Crossref Search ADS WorldCat [42] Dong ZJ , Yu XY, Chang CT, et al. How does feed-in tariff and renewable portfolio standard evolve synergistically? An integrated approach of tripartite evolutionary game and system dynamics . Renew Energy , 2022 , 186 : 864 – 877 . Google Scholar Crossref Search ADS WorldCat [43] Fang DB , Zhao CY, Kleit AN. The impact of the under enforcement of RPS in China: an evolutionary approach . Energy Policy , 2019 , 135 : 111021 . Google Scholar Crossref Search ADS WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy

Loading next page...
 
/lp/oxford-university-press/how-will-tradable-green-certificates-affect-electricity-trading-K42yffVgXt
Publisher
Oxford University Press
Copyright
Copyright © 2022 National Institute of Clean-and-Low-Carbon Energy
ISSN
2515-4230
eISSN
2515-396X
DOI
10.1093/ce/zkac038
Publisher site
See Article on Publisher Site

Abstract

Abstract Renewable portfolio standards (RPS) are important guarantees to promote renewable energy (RE) consumption. The tradable green certificate (TGC) trading mechanism is a supporting mechanism of RPS, but the rate of TGC trading is low and there is a double-metering problem of RE consumption. With the introduction of new policies in China, we innovatively take the electricity-selling side as the subject of RE consumption responsibility and biomass-based electricity-generation (BEG) projects are considered to participate in TGC trading. To explore the interaction between the TGC market and the electricity market, this paper sets up a day-ahead spot market-trading structure combining both markets under RPS and establishes a market equilibrium model. The established model is solved and validated based on the particle swarm optimization algorithm and the profits of each market player under different influencing factors are analysed. The main conclusions are as follows. (i) The established market structure and model effectively solve the double-metering problem of RE consumption, making the TGC turnover rate reach 82.97 %, greatly improving the market efficiency. (ii) Increased demand for TGC will increase demand for RE electricity. The participation of BEG projects in the TGC market can effectively improve the profit of biomass-based electricity producers (BEPs), reduce the burden of government financial subsidies and will not affect the consumption of wind-based electricity and photovoltaic-based electricity. This will help promote the rapid development of China’s RE, especially the BEG industry. (iii) Among the influencing factors, the increase in renewable-energy consumption responsibility weight and the decrease in electricity-generation cost can increase the profit of BEPs. The decline in TGC price and subsidy price will reduce the profit of BEPs. Finally, we put forward policy recommendations for China’s RPS and TGC trading mechanism. This study can provide a reference for the construction of China’s TGC market and electricity market and the development of RE. Open in new tabDownload slide tradable green certificate market, electricity market, renewable portfolio standards, biomass-based electricity generation, particle swarm optimization algorithm Introduction Promoting the development of renewable energy (RE) is crucial for accelerating the clean-energy revolution [1, 2]. Since the promulgation of the Renewable Energy Law of the People’s Republic of China in 2006, China has issued a series of policies to promote the development of the RE industry, mainly including feed-in tariff (FIT) and renewable portfolio standards (RPS) policies [3]. The implementation of FIT causes the installed power capacity of RE to increase rapidly. In 2021, the installed renewable power capacity accounted for 43.5% of the total installed power capacity in China [4]. But FIT also imposes a huge financial burden on the government. RPS is a mandatory policy instrument adopted by the government to nurture the RE market and bring RE generation to a guaranteed minimum level. From 2018 to 2020, China has set specific RPS targets for each province for both total RE-generation targets and non-hydropower targets. The RPS is seen as an important long-term guarantee to promote RE consumption [5]. The tradable green certificate (TGC) trading mechanism is the supporting mechanism of RPS. The TGC trading mechanism is a market-based subsidy scheme designed to promote the development of RE-generation projects [6]. China set up RPS to promote China’s RE consumption, the purchase of a TGC equivalent to complete 1 MWh of RE consumption. China’s TGC trading is also aimed at reducing RE subsidies, as RE used for TGC trading is no longer subsidized. Therefore, theoretically carrying out TGC transactions can promote the consumption of RE and reduce the burden of national financial subsidies. At present, many scholars have used a variety of methods to study the impact of FIT and RPS mechanisms on the RE-generation industry, including electricity prices, investment incentives, the profits of market subjects and social welfare. Ciarreta et al. [7] believed that the incentive FIT policy cannot reflect electricity market conditions or price signals and when the scale of RE-generation projects is obvious, it will bring a huge economic burden to consumers. Zhang et al. [8] showed that RPS can effectively help China’s distributed photovoltaic generation to achieve grid parity after the cancellation of electricity price subsidies. Pineda and Bock [9] believed that in the TGC market, increasing quota obligations and appropriate penalties for irregularities would encourage investment in RE-generation projects. Zhang et al. [10] showed that RPS could improve the installed capacity and profit of biomass-based electricity generation (BEG) in a fully competitive market, but the impact on other electricity market subjects was not analysed. Dong and Shimada [11] indicated that under the RPS and FIT mechanism, RE consumption in the electricity market would increase and non-renewable-energy enterprises’ revenues would decrease. Zhou et al. [12] found that when the parameter, i.e. renewable-energy consumption responsibility weight (RECRW), is within a certain range, the social welfare under the RPS mechanism is always higher than that under the FIT policy. Liu et al. [13] showed that the market competition force of RE producers was still guaranteed even if the subsidy policy of the electricity price was abolished. The above studies show that RPS will better promote the development of RE and enhance the profits of market subjects and social welfare than the FIT policy. Since TGC trading is closely related to electricity trading, the coupling effect of the TGC market and electricity market has been studied by many scholars, mainly including the determination of the TGC price, the efficiency of the TGC market and TGC trading. Wolfgang et al. [14] proposed a method to predict short-term and medium-term TGC prices to assist investment decisions in RE projects. Zhao et al. [15] analysed the interaction between the TGC market and the electricity market. Based on the maximization of social welfare, the optimal benchmark price of TGC is 0.06 USD/kWh. Based on the Nash bargaining model, Zuo et al. [16] obtained the benchmark price of China’s TGC as 49.59 USD/MWh. Due to the technical differences of different RE generation, Zhao et al. [17] considered the key parameter of technology conversion coefficient in the transaction, which can effectively promote the TGC transaction. Song et al. [18] found that the operation efficiency of most TGC markets in China was not high, and suggested unifying the goals of regional development and electricity-generation enterprises to encourage market subjects to participate in TGC transactions. Compared with the benchmark on-grid price, the benefits of electricity plants under the marketed on-grid price are higher, which can effectively stimulate coal-fired electricity plants to increase the consumption demand of TGC [19]. The above studies show that the TGC market and the electricity market interact, but the efficiency of China’s TGC market is low and most studies take coal-fired electricity enterprises as the demand subject of TGC. On 10 May 2019, the National Energy Administration (NEA) issued the Notice on Establishing and Improving the Safeguard Mechanism for Renewable Energy Consumption, which stipulates that the responsibility for RE consumption is assumed jointly by electricity sellers and electricity consumers [20]. This indicates that the subject of RE consumption responsibility is transferred from the electricity-generation side to the electricity-selling side. In addition, the suppliers of TGC in the above studies only include wind-based electricity producers (WEPs) and photovoltaic-based electricity producers (PVEPs). On 29 September 2020, the NEA supplemented Some Opinions on Promoting the Healthy Development of Non-hydropower Renewable Energy Generation [21]. The document stipulates that the whole life cycle of BEG projects can be subsidized for 82 500 hours. After 15 years from the date of grid connection, it no longer enjoys the financial subsidies and TGCs will be issued to allow participation in the TGC market for transactions. At present, there is little research on the participation of BEG enterprises in TGC trading and it is necessary to study it. Biomass energy is the fourth-largest energy after oil, coal and natural gas, and has the characteristics of being low-carbon, clean and renewable [22]. It will account for 20% of the global energy supply in 2050 [23]. Among the biomass energy utilization technologies, BEG technology is one of the most promising utilization technologies with good environmental benefits [24]. Compared with coal-fired electricity plants containing carbon capture and storage (CCS), biomass-based electricity plants containing CCS can reduce carbon emissions by 769 kg/MWh [25]. However, due to the high acquisition cost of biomass raw materials, the low electricity-generation rate of the equipment and imperfect related supporting policies, the cost of BEG is high, so biomass energy is still unable to compete with fossil energy [26]. Under the FIT policy, BEG projects have reasonable profit margins [27]. However, with the expansion of the BEG scale, China’s financial subsidy burden is increasingly heavy—even a huge subsidy gap. As of 2017, the subsidy gap of the BEG industry in China had reached 14.364 billion RMB [28]. The subsidy has become an important factor in restricting the development of the BEG industry and affecting the profit and loss of enterprises. The participation of BEG projects in the TGC market may provide new ideas for solving these problems. Through the above analysis, we put forward the following four questions. Against the background of the introduction of new policies, what problems need to be solved in the TGC market and the electricity market? What impact will the transfer of the demand subject of TGC to the electricity-selling side have on each market subject? Will the participation of BEG projects in the TGC market be conducive to the development of the BEG industry? How will different key factors affect each market subject? To complement existing research, this paper attempts to answer these questions and make contributions through the following work and innovation: (i) We analyse the current TGC market and electricity market, and find that the TGC transaction rate is low and there is a double-metering problem for the consumption of RE. To address these issues, this paper constructs a coupling market transaction structure and a market equilibrium model between the TGC market and the electricity market under RPS. (ii) This paper takes the electricity retailers (ERs) as the main demand subject for TGC and coal-fired electricity enterprises are no longer required. We also consider the introduction of BEG enterprises into TGC trading. In the competition of various types of electricity-generation enterprises, to study the interaction between green-certificate trading and electricity trading, and to make clear whether the TGC trading is conducive to promoting the development of BEG enterprises and the impact on other market subjects. (iii) We analyse the key factors affecting the profits of each market subject, including the RECRW, TGC price, subsidy price and BEG cost, which will affect the traded volume of electricity and TGC. So as to put forward policy recommendations for China’s RPS and TGC trading mechanism. The structure of this paper is as follows. Section 1 sets the market transaction structure of the electricity market and the TGC market. Section 2 establishes a multi-objective market equilibrium model with the optimal profits of each market subject. Section 3 carries out case analysis, presents the simulation results of primary parameters and conducts a sensitivity analysis of market subjects’ profits. Section 4 carries out a discussion. Conclusions and policy recommendations are shown in Section 5. 1 Market transaction structure At present, there is a dual measurement problem of RE consumption in the electricity market, which means that RE and its corresponding TGC can complete the RECRW. In addition, the TGC market has a low turnover rate. According to the data of the China Tradable Green Certificate Certification Platform [29], as of 28 May 2022, a total of 43.51 million TGCs have been issued in China, with a cumulative number of 8.01 million registered and a cumulative turnover of 2.01 million. The turnover rate is only 25.1%, which is not conducive to improving the awareness of RE consumption and the enthusiasm of RE-generation projects to participate in the TGC market. Since both electricity consumers and electricity sellers are the demand subjects of electricity and TGC, to simplify the research, this paper only considers the electricity sellers as the demand subject. To solve the above two types of problems, this paper sets up the market transaction structure of the electricity market and the TGC market under the RPS, as shown in Fig. 1. Fig. 1: Open in new tabDownload slide Market transaction structure under the RPS. Under this market transaction structure, this paper proposes the following five assumptions. First, to simplify the study, this paper assumes that the market transaction structure includes only two major types of trading entities, namely electricity producers and ERs, wherein electricity producers include fuel-based electricity producers (FEPs) and non-water renewable-energy producers. The latter include WEPs, PVEPs and biomass-based electricity producers (BEPs), all of them referred to as green electricity producers (GEPs). In China, hydropower producers do not have access to TGC, so we do not consider it to better analyse the impact of the TGC market on the electricity market. Moreover, in other scholars’ studies, hydropower producers were also not considered [30, 31]. Second, to solve the dual measurement problem of RE consumption, this paper separates the physical attributes and environmental attributes of RE and represents them by the electricity itself and the TGC connected with the electricity respectively. The electricity and the TGC are traded separately. That is to say, assuming that all electricity only has physical attributes and cannot complete the RECRW, the ERs can only purchase TGC in the TGC market to complete the RECRW. Third, considering the different electricity-generation costs of different types of GEPs, to encourage the balanced development of various types of GEPs, it is assumed that the number of TGCs obtained by each GEP is multiplied by the actual electricity generation and the technical type coefficient ε [17]. Based on the production of 1 MWh of green electricity to obtain a TGC, different types of GEPs can obtain ε TGC for each 1 MWh of green electricity according to their technical type coefficient ε. Fourth, to simplify the research, it is assumed that the transaction is a day-ahead electricity spot market clearing. The ERs predict the electricity demand through the consumers’ typical load curve and announce the electricity demand in the electricity market. According to the demand and the situation of their generating units, each electricity producer reports the tradable volume and trading price of electricity and TGC. The ERs purchase electricity in the electricity market to meet the load demand of users and purchases TGC in the TGC market to meet the RECRW. Finally, assuming that the RECRW of ERs is α, the proportion of the electricity connected with the purchase of TGC to the total purchase of electricity is not <α. Each purchase of TGC is equivalent to the consumption of 1 MWh of green electricity. The completion of the RE consumption responsibility of each trading cycle is strictly assessed by the government to ensure the feasibility of the trading mechanism. If ERs cannot satisfy the RECRW, then each lack of a TGC will require a penalty of β USD. 2 Market equilibrium model 2.1 Cost function of each generator (i) Assume that the generating cost of FEPs i in the trading period time t is Cci,t ⁠, as shown in Equation (1) [32, 33], where a1 ⁠, b1 and c1 represent the cost coefficients and qci,t represents the amount of trading electricity of FEPs: Cci,t=a1qci,t2+b1qci,t+c1(1) (ii) Assume that the number of WEPs, PVEPs and BEPs among GEPs j is n1 ⁠, n2 and n3 ⁠, respectively (⁠ n1+n2+n3=n ⁠), and their generating cost in the trading period time t is Cwj,t ⁠, Cpj,t and Cbj,t ⁠, respectively, as shown in Equations (2)–(4) [32, 33] where ak ⁠, bk and ck (⁠ k=2,3,4 ⁠) represent the cost coefficients, and qwj,t ⁠, qpj,t and qbj,t represent the amount of trading electricity, respectively: Cwj,t=a2qwj,t2+b2qwj,t+c2(2) Cpj,t=a3qpj,t2+b3qpj,t+c3(3) Cbj,t=a4qbj,t2+b4qbj,t+c4(4) 2.2 Equilibrium model 2.2.1 Objective function The objective function of the equilibrium model is to maximize the profits of all subjects in each trading period. (i) FEPs mainly obtain profit through the sale of electricity. Its profit is the revenue from electricity sales minus the cost of electricity generation. Assume that the profit of FEPs in the trading period time t is πc,t and the trading price of electricity reported by FEPs i is pci,t ⁠. The optimal profit model is: max πc,t=pci,t∑mi=1qci,t−∑mi=1Cci,t(5) (ii) GEPs mainly obtain profit through the sale of electricity and TGC. From 2021, the central government will no longer subsidize new centralized photovoltaic-based electricity plants, commercial and industrial distributed photovoltaic-based electricity projects, and newly approved onshore wind-based electricity projects, and will implement grid parity [34]. Therefore, in the context of gradual subsidy withdrawal, this paper does not consider the subsidies for WEPs and PVEPs, and the profit of both is the revenue from the sales of electricity and TGC minus the cost of electricity generation. At present, the cost of BEG is still high, so BEPs are subsidized, but the electricity corresponding to TGC trading is no longer subsidized. Its profit is the revenue from the sales of electricity and TGC plus the subsidies corresponding to the electricity removed from the TGC trading, then minus its cost of electricity generation. Assume that the profit of GEPs in the trading period time t is πw,t ⁠, πp,t and πb,t ⁠, respectively, and the amount of TGC trading of GEPs j is qwj,tTGC ⁠, qpj,tTGC and qbj,tTGC ⁠, respectively; then the trading price of electricity and TGC reported by GEPs j is pwj,t ⁠, ppj,t ⁠, pbj,t ⁠, pwj,tTGC ⁠, ppj,tTGC and pbj,tTGC ⁠, respectively. The unit price of electricity-generation subsidy for BEPs is psub in USD/MWh and the profit model of the three types of GEPs is: max πw,t=∑n1j=1pwj,tqwj,t+∑n1j=1pwj,tTGCqwj,tTGC−∑n1j=1Cwj,t(6) max πp,t=∑n2j=1ppj,tqpj,t+∑n2j=1ppj,tTGCqpj,tTGC−∑n2j=1Cpj,t(7) max πb,t=∑n3j=1pbj,tqbj,t+∑n3j=1pbj,tTGCqbj,tTGC+psub∑n3j=1(qbj,t−qbj,tTGC/ε)−∑n3j=1Cbj,t(8) (iii) The ERs obtain profits by selling electricity to customers and their profit is the revenue from the sales of electricity minus the penalties and the cost of purchasing TGC and electricity. Assume that the profit of ERs in the trading period time t is πs,t ⁠, the selling price of electricity is ps ⁠, the trading volume of electricity and TGC with GEPs j is qgj,t and qgj,tTGC ⁠, respectively, and the volume of TGC that ERs do not meet the RECRW is qβTGC ⁠. The optimal profit model is: max πs,t=(ps−pci,t)∑mi=1qci,t+(ps−pgj,t)∑nj=1qgj,t−∑nj=1pj,tTGCqgj,tTGC−βqβTGC(9) 2.2.2 Constraint condition (i) Electricity balance constraint: for each trading period time t, the total amount of electricity purchased by the ERs is consistent with the customer load ut ⁠: ∑mi=1qci,t+∑nj=1qgj,t=ut(10) (ii) Generation output constraint: for each trading period time t, the tradable electricity of each GEP satisfies the maximum and minimum output of the generating units. The minimum and maximum generation capacity of the FEPs in trading period time t are minqci,t and maxqci,t ⁠, and those of the GEPs are minqgj,t and maxqgj,t ⁠, respectively: min qci,t≤qci,t≤max qci,t(11) min qgj,t≤qgj,t≤max qgj,t(12) (iii) Quotation constraint: since both electricity and TGC are quoted transactions, to ensure the profits of all subjects, the electricity and TGC quotations of each electricity producer are constrained: min pelectricity≤pelectricity≤max pelectricity(13) min pTGC≤pTGC≤max pTGC(14) (iv) TGC trading volume constraint: for each trading period time t, the number of TGC traded by each GEP does not exceed the total amount available to it: 0≤qgj,tTGC≤ε ∑tt=1qgj,t−∑t−1t=1qgj,tTGC(15) (v) RECRW constraint: for the trading period time t, the proportion of electricity corresponding to the amount of TGC purchased by the ERs to the total electricity sales is not <α: α≤(∑nj=1qgj,tTGC+qβTGC)/(∑mi=1qci,t+∑nj=1qgj,t)(16) 2.3 Nash equilibrium analysis For the trading period time t, each electricity producer reports their trading volume and trading price of electricity and TGC, and the cost of each electricity producer can be derived from Equations (1)–(4). The ERs purchase electricity and TGC for their own best profit and the profit of each electricity producer and ERs is derived from Equations (5)–(8). Based on the above model, the electricity producer can continuously change their strategies of quoted trading volume and trading price to gain more profit. In this non-cooperative game model, S={qci,t,pci,t,qgj,t,pgj,t,qgj,tTGC,pgj,tTGC,qβTGC}is the strategy set of each market subject. For any game party, each market subject has no incentive to change its strategy under a certain strategy set S∗ ⁠, which means that each market subject reaches its optimal profit maxπ and the market reaches Nash equilibrium. The strategy set S∗ is called the Nash equilibrium solution, i.e. it satisfies: π(S)≤π(S∗)(17) 2.4 Model-solving method The algorithm steps for solving the Nash equilibrium solution for each trading period time t are: (i) Each electricity producer reports the trading volume and trading price of electricity and TGC to obtain the strategy set S={qci,t,pci,t,qgj,t,pgj,t,qgj,tTGC,pgj,tTGC,qβTGC} ⁠. (ii) Calculate the cost of each electricity producer. (iii) Calculate the profit set π(S)={πc,t,πw,t,πp,t,πb,t,πs,t} for each electricity producer and ERs. (iv) Each electricity producer modifies the trading volume and trading price to obtain a new strategy set S ′ ={qci,t ′ ,pci,t ′ ,qgj,t ′ ,pgj,t ′ ,qgj,tTGC ′ ,pgj,tTGC ′ ,qβTGC ′ } and a new profit set π(S ′ ) ⁠, and repeat steps (ii) and (iii). (v) If the profit of a market subject in π(S′) is greater than the profit in π(S) ⁠, assign π(S′)and S′ to π(S) and S′ ⁠, and return to step (iv). (vi) If the profit increment of each market subject in π(S′) is <λ (λ is a very small number), output the strategy set S and the profit set π(S) at this trading period time, otherwise return to step (iv). In this paper, the particle swarm optimization (PSO) algorithm is selected for simulation. The PSO algorithm has an efficient global search capability and is suitable for handling multiple types of objective functions and constraints. It is conducive to obtaining a Nash equilibrium solution under multiple objectives and has been widely used in the research of other scholars in the field, such as [31, 35]. Some scholars also use a genetic algorithm (GA) to solve this type of problem [36]. In the PSO algorithm, all particles will preserve the information of good solutions. However, in the GA algorithm, as the population changes, the information of previous solutions will be destroyed, so the solution speed of PSO is theoretically faster. In addition, the PSO algorithm is easier to implement as it does not require operations such as crossover and variation in the coding process. Therefore, the PSO algorithm is chosen in this study. The PSO algorithm starts from a stochastic solution and finds the optimal solution by iteration. Each particle k is a potential solution to the optimization problem with a fitness value pk determined by the objective function. During the iterative process, the particles’ search direction and distance are controlled by speed v. At the beginning of the algorithm, particles are randomly generated, and the individual extreme value pbest of each particle and the global extreme value gbest of the whole population are searched. Each particle updates its speed and fitness value according to the following equations, and during the iteration, the particles are continuously updated to find a better pbest and gbest ⁠. Suppose w is the inertia weight, i.e. the tendency of the particle to maintain its previous speed; l1 and l2 are the learning factors, which denote the tendency of the particle to approach the pbest and the gbest of the population, respectively: vk′=w∗vk+l1∗rand(pbest−pk)+l2∗rand(gbest−pk)(18) pk′=pk+vk′(19) 3 Case analysis 3.1 Case parameter setting It is assumed that one FEP, one WEP, one PVEP, one BEP and one ER participate in the market transaction in a certain market, and the economic technical parameters of the electricity producers are shown in Table 1 [17, 33, 37]. Typical load forecasting curves of customers on a trading day are shown in Fig. 2 and the generation output forecasts for WEPs and PVEPs are shown in Fig. 3 [38]. Since the outputs of FEPs and BEPs are more stable, their maximum output is their rated installed capacity. Based on the RECRW set by each province (district and city) in China in the relevant documents of the National Development and Reform Commission, the RECRW α of the ERs in this paper is 15% [20], penalty β is set at 74.45 USD per certificate, the generation subsidy of BEPs is set at 52.14 USD/MWh and the electricity sales price is 77.44 USD/MWh. Table 1: The economic technical parameters of the electricity producers Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Open in new tab Table 1: The economic technical parameters of the electricity producers Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Generation units . Cost coefficient . . . Installed capacity (MW) . ε . . a (USD/(MWh)2) . b (USD/MWh) . c (USD) . . . FEPs –0.0049 56.00 22.79 800 0 WEPs –0.0348 52.87 0 250 1 PVEPs –0.0383 54.36 0 250 1 BEPs –0.0604 104.25 25.91 50 1.5 Open in new tab Fig. 2: Open in new tabDownload slide Typical load forecasting curves of customers on a trading day. Fig. 3: Open in new tabDownload slide Generation output forecasts for WEPs and PVEPs. From 10 May 2020 to 10 May 2022, on the website of the TGC platform, the average price of TGC of WEPs and PVEPs was 19.49 and 9.74 USD per certificate, respectively, and we set the quotation range of electricity and TGC of each electricity producer as shown in Table 2. In the PSO algorithm of this case, the initial number of particles in the population is 1000, the maximum number of iterations is 1000, w is 0.5, l1 and l2 are 2.0, and the optimization simulation is performed in the MATLAB platform. Table 2: The quotation range of electricity and TGC of each electricity producer Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Open in new tab Table 2: The quotation range of electricity and TGC of each electricity producer Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Power producers . min pelectricity (USD/MWh) . max pelectricity (USD/MWh) . min pTGC (USD/piece) . max pTGC (USD/piece) . FEPs 58.08 61.06 0 0 WEPs 55.10 58.08 17.87 20.85 PVEPs 56.59 59.57 8.19 11.17 BEPs 61.06 64.03 52.12 55.10 Open in new tab 3.2 Model validation Based on the above parameters and data, the market equilibrium model proposed in this paper is verified and the Nash equilibrium solution is obtained by simulation. The traded electricity of each electricity producer is shown in Fig. 4, the traded TGC of each GEP is shown in Fig. 5 and the profit of each market subject is shown in Table 3. Table 3: The profit of each market subject Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Open in new tab Table 3: The profit of each market subject Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Market subject . Traded electricity (MWh) . Traded TGC (piece) . Profit (USD) . Unit profit (USD/MWh) . FEPs 10 986.86 0 64 142.53 5.84 WEPs 1161.23 1161 28 178.21 24.27 PVEPs 304.00 304 5289.84 17.40 BEPs 596.15 492 15 463.00 25.94 ERs 13 048.24 1957 183 771.39 14.08 Open in new tab Fig. 4: Open in new tabDownload slide The traded electricity of each electricity producer. Fig. 5: Open in new tabDownload slide The traded TGC of each GEP. As can be seen from Fig. 4, the BEPs have lower generation output during the trading periods of 10:00–19:00 and the reason for this can be seen by analysing Fig. 5. WEPs and PVEPs get the priority trading right of electricity and TGC due to their price advantage, and the amount of TGC purchased by ERs from these two major GEPs during this period time can basically meet the RECRW, which makes the traded TGC of BEPs lower. The biomass generation output is at a low level because of the high cost of biomass generation and the weak competitiveness with FEPs. However, biomass generation output increased during the periods of 0:00–9:00 and 20:00–24:00. The reason is that during these trading periods, WEPs and PVEPs are not enough to meet the market demand for TGC and the ERs must purchase additional TGC from BEPs to fulfil the RECRW, which makes the traded TGC of BEPs increase, thus contributing to the increase in biomass generation output. The above discussion shows that there is a strong correlation between biomass generation output and the supply and demand of TGC in the market. In addition, under the market transaction structure established in this paper, all GEPs can obtain 2.359 thousand TGC and trade 1.957 thousand TGC, with a turnover rate of 82.97%, which effectively improves the trading efficiency of the TGC market. Table 3 shows that even though the cost of BEPs is still high at present, its profit per unit of electricity generation is the highest. Assuming the same amount of electricity traded of BEPs with or without the participation of TGC trading, Table 4 shows the profits in the two scenarios. From Table 4, it can be seen that the profit gained by BEPs participating in TGC trading increases by 9081.07 USD compared to the profit gained without participating in TGC trading, up 142.29%. From the profit model of BEPs (Equation (8)), it is clear that its profit is related to the technical type coefficient ε. In this case, BEPs can still enjoy part of the generation subsidy even if it participates in the TGC trading, which makes their profit increase more. Table 5 shows the comparison of the profits of BEPs with and without TGC trading under different technical type coefficients ε. Table 4: The profits of BEPs with or without the participation of TGC trading TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 Open in new tab Table 4: The profits of BEPs with or without the participation of TGC trading TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 TGC trading . Traded electricity (MWh) . Traded TGC (piece) . Electricity with subsidy (MWh) . Profit (USD) . Unit profit (USD/MWh) . Participation 596.15 492 268.14 15 463.00 25.94 Non-participation 596.15 0 596.15 6381.93 10.71 Difference 0.00 492 328.01 9081.07 15.23 Open in new tab Table 5: The profits of BEPs with and without TGC trading under different technical type coefficients ε Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Open in new tab Table 5: The profits of BEPs with and without TGC trading under different technical type coefficients ε Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Technical type coefficient ε . 1.4 . 1.3 . 1.2 . 1.1 . 1.0 . Participation (USD) 14 241.99 12 833.14 11 189.47 9246.96 6915.94 Non-participation (USD) 6381.93 6381.93 6381.93 6381.93 6381.93 Difference (USD) 7860.06 6451.21 4807.54 2865.03 534.01 Growth rate 123.16% 101.09% 75.33% 44.89% 8.37% Annual subsidy reduction (billion USD) 3.94 4.25 4.6 5.02 5.52 Open in new tab As can be seen from Table 5, as the coefficient ε decreases, the profit of BEPs remains the same if BEPs do not participate in TGC trading, while it is gradually decreasing if BEPs participate in TGC trading and the annual subsidy reduction is gradually increasing. However, even when the coefficient ε decreases to 1, i.e. the same as the coefficient of WEPs and PVEPs, the profit of BEPs participating in the TGC trading increases by 8.37% compared to the profit of BEPs not participating in the TGC trading. This indicates that the introduction of BEG projects into the TGC trading can bring them greater profits. Therefore, BEPs are more likely to participate in TGC trading than to obtain government subsidies. Based on the 50-MW generating unit in this paper, if 492 TGC are sold on average per day, the corresponding 328 MWh of electricity can reduce the government’s financial subsidies by 6.24 million USD per year at a subsidized unit price of 350 RMB/MWh. According to the data published by the NEA, the installed capacity of BEG in China reached 29.52 million kW in 2020 [39] and if all of them are included in the TGC transaction, the government’s financial subsidies can be reduced by 3.68 billion USD per year, which is ~81.38% of the total annual subsidy funds for BEG [40]. Therefore, including BEG projects in TGC trading can not only greatly reduce the financial burden of the government, effectively alleviating the problem of subsidy arrears, but also promote the development of China’s BEG industry. 3.3 Sensitivity analysis of profits In this coupled market, the profit of each market subject is influenced by many factors. The government can guide each electricity producer to adjust its trading strategy by adjusting the RECRW, TGC price and electricity subsidy price, so as to promote the healthy development of the TGC market and electricity market. In addition, the cost of electricity generation from biomass is gradually decreasing, which has a large impact on the revenue of BEPs. Therefore, this paper focuses on four major factors: RECRW, TGC price and electricity subsidy price, as well as the cost of BEG. 3.3.1 Impact of RECRW When RECRW is 13%, 14%, 15%, 16% and 17%, respectively, the traded electricity, TGC turnover and profit of each market subject are as shown in Fig. 6a–c. Fig. 6: Open in new tabDownload slide (a) The traded electricity under different RECRW. (b) The traded TGC under different RECRW. (c) The profit of each market subject under different RECRW. From Fig. 6a and b, it can be seen that with the increase in RECRW, the traded electricity and TGC of BEPs increase significantly, while the traded electricity of FEPs decreases slightly, and the traded electricity and TGC of WEPs and PVEPs remain almost unchanged. As the increase in RECRW will increase the demand for TGC in the market, based on the installed capacity of each electricity producer remaining unchanged, BEPs can obtain a larger market share without affecting the consumption of renewable electricity from wind electricity and photovoltaic electricity, which is conducive to the promotion of China’s safeguard mechanism for renewable-energy consumption. From Fig. 6c, it can be seen that the profits of WEPs and PVEPs do not change obviously under different RECRW. With the increase in traded volume, the profit of BEPs increases significantly, whereas the FEPs’ profits slightly decrease due to the decrease in traded electricity. The ERs’ profit decreases due to the need to purchase more TGC. The results show that the increase in RECRW will greatly improve the enthusiasm of BEPs to participate in the electricity market and TGC market, which is conducive to improving the current situation of the generally low profit of BEPs and promoting their rapid development. To this end, under the premise of maintaining the profits of each market subject, the government needs to reasonably formulate the RECRW for the installed capacity ratio of RE in each region. According to the simulation results of this paper, the RECRW in the range of 14–16% is comparatively reasonable. 3.3.2 Impact of TGC price and electricity subsidy price of BEPs Since BEG projects are not currently included in TGC trading and there is a lack of data on the TGC price of BEPs in the market, the impact of the TGC price and electricity subsidy price of BEPs on the traded volume and profit of each market subject is analysed. Fig. 7a and b shows the volume of traded electricity and traded TGC, and the profit of each market subject when the TGC prices are in the range of [37.23,40.20], [44.67,47.65] and [52.12,55.09] (USD per certificate). Fig. 7c and d shows the volume of traded electricity and traded TGC and the profit of each market subject when the electricity subsidy price is 37.23, 44.67 and 52.12 USD/MWh, respectively. Fig. 7e compares the extent of the impact on the BEPs’ profit under the equal decreases in the TGC price and electricity subsidy price. Fig. 7: Open in new tabDownload slide Open in new tabDownload slide (a) The volume of traded electricity and traded TGC under different TGC prices. (b) The profit of each market subject under different TGC prices. (c) The volume of traded electricity and traded TGC under different electricity subsidy prices. (d) The profit of each market subject under different electricity subsidy prices. (e) BEPs’ profit under the equal decreases in TGC price and electricity subsidy price. From Fig. 7a and c, it can be seen that the changes in the TGC price of BEPs as well as the electricity subsidy price hardly affect the volume of traded electricity and traded TGC of each market subject. This indicates that the changes in the TGC price and electricity subsidy price of BEPs do not change the priority relationship of the trading rights of each market subject. From Fig. 7b, it can be seen that as the TGC price of BEPs increases, the profit of BEPs gradually increases, the profit of ERs gradually decreases and the profit of the rest of the market subjects does not change significantly. From Fig. 7d, it can be seen that with the increase in the electricity subsidy price of BEPs, the profit of BEPs is gradually increasing and the profit changes of the rest of the market subjects are not obvious. From Fig. 7e, it can be seen that although the increase in both the TGC price and electricity subsidy price will make the profit of BEPs increase, the TGC price results in a greater impact, which can be explained by BEPs benefiting more from TGC trading than from subsidies. Therefore, to protect the profit of the biomass-based electricity industry, the government or the relevant industry should set a reasonable macro-guidance price for TGC trading, so that the TGC price fluctuates within a certain range. In addition, the government should implement a gradual reduction in electricity subsidies for BEPs, rather than a one-size-fits-all policy. 3.3.3 Impact of BEG cost With the continuous improvement in BEG technology, the BEG cost will be reduced to a certain extent. After analysis, the cost coefficient b4 has the highest degree of influence on the cost of electricity generation, so we analyse the traded volume of electricity and TGC and the profit of each market subject under the cost coefficient b4 of 104.23, 96.79 and 89.34 (unit: USD/MWh), as shown in Fig. 8a and b. Fig. 8: Open in new tabDownload slide (a) The volume of traded electricity and traded TGC under different BEG costs. (b) The profit of each market subject under different electricity subsidy prices. As can be seen from Fig. 8a, the decrease in the BEG cost hardly affects the amount of traded electricity and traded TGC of each market subject. Because the feedstock cost of BEG is increasing, the overall electricity-generation cost has limited scope for reduction [28]. Although the BEG cost has been reduced, it is still higher than that of the other three types of electricity producer, so the priority relationship of the trading rights of each electricity producer has not changed. As shown in Fig. 8b, the profit of BEPs increases significantly with the decrease in BEG cost and hardly affects the profit of the remaining market subjects. Therefore, it is one of the important initiatives for BEPs to reduce their electricity-generation costs to improve their profit. 3.4 Robustness analysis of the algorithm The PSO algorithm, as an intelligent algorithm, can obtain the optimal solution by expanding the path of finding the optimal solution. In the iterative process, the number of population particles will have an impact on its convergence performance and the robustness of the algorithm needs to be tested. For the convenience of analysis, the profit of BEPs is chosen as the fitness function in this paper and the remaining parameters remain unchanged. Fig. 9 shows the convergence results of the algorithm when the number of population particles is 250, 500, 750 and 1000, respectively. It can be seen that the volatility of searching for the optimal solution decreases as the number of population particles increases. And the algorithm can converge to a stable equilibrium solution after a certain number of iterations, regardless of the value of the number of population particles. Therefore, the PSO algorithm has comparatively high robustness. Fig. 9: Open in new tabDownload slide The PSO algorithm’s convergence performance under different numbers of population particles. 4 Discussion For the problem of the low turnover rate of TGC in the TGC market, we propose a market transaction structure and market equilibrium model, and the TGC and electricity are traded separately. It greatly improves the turnover rate of TGC and effectively promotes the development of the TGC market, while ensuring the consumption of RE. This is consistent with Song et al.’s [41] conclusion. For suppliers in the TGC market, previous studies generally only considered WEPs and PVEPs, and this paper adds BEPs on this basis. Compared with not participating in TGC trading, BEPs’ profits have been effectively improved. In Li et al.’s [31] study, participation in TGC transactions can also increase the profits of GEPs. In GEPs, BEPs have the highest unit profit, followed by WEPs and PVEPs. However, in Li et al.’s [31] study, the profit of PVEPs is higher than that of WEPs, which is different from the results of this paper. Because in our study, according to the actual TGC market, the TGC prices of different GEPs take different values, while in Li et al.’s [31] study, they take the same values, which leads to different conclusions, and the results of this paper are more relevant to the actual situation. This paper also discusses the impacts of the RPS quota, TGC price and electricity subsidy prices on market-trading volumes and the profits of market subjects. An appropriate increase in the RPS quota can increase the electricity traded volume and TGC traded volume of BEPs, thus increasing their profits, and it is more appropriate to keep the quota in the range of 14–16%. This is consistent with the findings of Song et al. [41]. However, higher quotas can cause a greater loss of profits for ERs, so formulating quota planning goals should be reasonable. As suggested by Zhang et al. [8], when formulating quota planning goals, the government needs to consider the relation between the RPS quota, the installed capacity and the TGC-supply/demand/price. In this paper, the TGC price cap is set so that the cost of meeting the RECRW of the ERs does not increase too much, which promotes the development of the TGC market to some extent. The change in the TGC price has little effect on the generation capacity of each electricity producer, while the study by Zhao et al. [15] shows that the total generation capacity will increase with the increase in the TGC benchmark price. This is because they treat FEPs as the demanders of TGC and thus the TGC price affects the generation capacity of each electricity producer. In contrast, in this paper, ERs are treated as the demand subject of TGC, and electricity and TGC are sold separately. The change in the TGC price of BEPs does not change the priority of transactions, so it does not change the volume of electricity and TGC traded, but only affects the profit. The profits of BEPs are gradually increasing as the electricity subsidy price rises. For the same electricity subsidy price and TGC price increase, the TGC price leads to a higher profit increase. This shows that replacing the FIT subsidy policy with the RPS policy is a correct and feasible decision. However, under the circumstances that the FIT subsidy has not been completely removed, the TGC market should be developed in an orderly manner [42]. The unit penalty is usually assumed to be equal to the TGC price cap [9, 10] but Fang et al. [43] showed that the unit penalty should be appropriately higher than the TGC price cap, which can induce a more efficient TGC market. We also set the unit penalty in this way and the results show that ERs are more willing to purchase TGC than to pay higher price penalties, which increases the turnover rate of TGC and promotes the development of the TGC market. There are some limitations of our work that can provide directions for future research. The first limitation is that we only focused on the day-ahead electricity spot trading. Electricity trading also includes medium-term and long-term contract trading and future trading, and these trading methods also have different impacts on the trading volume, trading price and profit of each market subject in the electricity market and TGC market. The second limitation is that we only consider ERs as the demand subjects of TGC. Electricity consumers who purchase electricity through the wholesale electricity market and enterprises with captive electricity plants are also the demand subjects of TGC. This will increase the demand for TGC and affect the trading results of the electricity market and the TGC market. In addition, the coupling relationship between carbon trading as a means of reducing carbon emissions and TGC trading and electricity trading is an interesting and worthy research issue. 5 Conclusions and recommendations Based on the analysis of the policies related to RE-generation projects and the current situation of the TGC and electricity market, we constructed a coupling market transaction structure and a market equilibrium model between the TGC market and the electricity market under RPS. We also consider the introduction of BEG enterprises into TGC trading. In the competition of various types of electricity-generation enterprises, to study the interaction between green-certificate trading and electricity trading, and to make clear whether the TGC trading is conducive to promoting the development of BEG enterprises and the impact on other market subjects, in the end, we analysed the key factors affecting the profits of each market subject. The main findings are as follows: (i) The market transaction structure proposed in this paper effectively solves the current dual measurement problem of RE consumption, and the turnover rate of TGC in this paper reaches 82.97%, which greatly improves the efficiency of the TGC market and provides a guarantee for BEPs to obtain additional revenue from the TGC market to cover its high generation costs. (ii) The case study verifies the effectiveness and rationality of the market equilibrium model and optimization algorithm established in this paper. The results show that increased demand for TGC will increase demand for RE electricity. By including BEG projects in the TGC market, BEPs can earn higher profits with TGC transactions than with government subsidies. When the technical type coefficient ε of BEPs is in the range of 1–1.5, the participation in TGC trading increases BEPs’ profit by 8.37–142.29% and reduces the burden of government financial subsidies by 3.68–5.52 billion USD compared to non-TGC trading. This will increase the enthusiasm of BEPs to participate in the electricity market and the TGC market, which is conducive to the rapid development of China’s BEG industry. (iii) The analysis results of profit-influencing factors show that the increase in RECRW makes the TGC demand increase, thus increasing the BEPs’ traded volume of electricity and TGC, which greatly improves BEPs’ profits and does not affect the renewable-energy consumption of WEPs and PVEPs. Changes in the TGC price and subsidy price of BEPs and their electricity-generation costs hardly affect the traded volume of electricity and TGC of each market subject but have an impact on the profit of BEPs. Among these factors, the decrease in TGC prices and subsidy prices makes BEPs’ profits decrease and the degree of impact of the TGC price is greater, while the decrease in electricity-generation costs makes BEPs’ profits increase. In response to the conclusions drawn in this paper, the following policy recommendations are proposed: (i) The government should allow BEG projects to participate in the TGC market as early as possible. At present, although BEG projects still enjoy the electricity subsidy, the government must introduce feasible alternatives to the subsidy policy as the amount of electricity subsidy gradually declines. This paper shows that BEG projects can benefit more from participating in TGC trading than from the electricity subsidy. Therefore, the government should approve BEG projects that meet production emission standards to participate in the TGC market as early as possible, so that the electricity subsidy policy and TGC trading policy can go hand in hand, thus promoting a virtuous cycle of the BEG industry, while also reducing government financial subsidy. (ii) Let the market play a decisive role and the government macro-regulates the market. In the TGC market and electricity market structure constructed in this paper, the revenue of each market subject is influenced by various factors, among which the market price plays a key role. To ensure the normal operation of the market, the government needs to provide macro guidance to it. At present, the price of TGC remains high for a long time and the government needs to set a reasonable guideline price for TGC trading. In addition, there are differences in different RE-generation technologies and to encourage their reasonable competition, the government needs to set a reasonable technical type coefficient ε for RE-generation projects. (iii) RPS mechanism needs to be tailored to local conditions and the relevant parameters need to be reasonably set. The parameter RECRW determines the amount of RE consumption. The government needs to reasonably formulate the RECRW of each region according to the proportion of installed capacity of RE in each region to ensure the stable development of RE projects in each region. Subsequently, we will study the carbon emission reduction benefits of the RE projects, explore the feasibility and revenue status of their participation in carbon trading and electricity-generation rights trading, and coordinate the three types of trading mechanisms in conjunction with TGC trading, to help the RE industry, especially the BEG industry, to obtain greater revenue and promote the development and growth of the RE industry. Acknowledgements K.L.: conceptualization, methodology, software, validation, writing—original draft preparation. Z.L.: visualization, data curation, writing—reviewing and editing. Z.T.: methodology, software, supervision. Funding This research did not receive any grant from funding agencies in the public, commercial or not-for-profit sectors. Conflict of interest statement None declared. References [1] Li LL , Taeihagh A. An in-depth analysis of the evolution of the policy mix for the sustainable energy transition in China from 1981 to 2020 . Appl Energy , 2020 , 263 : 114611 . Google Scholar Crossref Search ADS WorldCat [2] Liu CC , Li N, Zha DL. On the impact of FIT policies on renewable energy investment: Based on the solar power support policies in China’s power market . Renew Energy , 2016 , 94 : 251 – 267 . Google Scholar Crossref Search ADS WorldCat [3] Xiao MZ , Simon S, Pregger T. Scenario analysis of energy system transition: a case study of two coastal metropolitan regions, eastern China . Energy Strategy Rev , 1004 , 2019 : 23 . Google Scholar OpenURL Placeholder Text WorldCat [4] China’s renewable energy power generation installed capacity in 2021. China, 29 November 2021. http://www.gov.cn/xinwen/2021-11/29/content_5653908.htm ( 29 May 2022 , date last accessed). [5] Yu BY , Zhao ZH, Zhao GP, et al. Provincial renewable energy dispatch optimization in line with Renewable Portfolio Standard policy in China . Renew Energy , 2021 , 174 : 236 – 252 . Google Scholar Crossref Search ADS WorldCat [6] Currier K , MA . regulatory adjustment process for the determination of the optimal percentage requirement in an electricity market with Tradable Green Certificates . Energy Policy , 2013 , 62 : 1053 – 1057 . Google Scholar Crossref Search ADS WorldCat [7] Ciarreta A , Espinosa MP, Pizarro-Irizar C. Optimal regulation of renewable energy: a comparison of feed-in tariffs and tradable green certificates in the Spanish electricity system . Energy Econ , 2017 , 67 : 387 – 399 . Google Scholar Crossref Search ADS WorldCat [8] Zhang LB , Chen CQ, Wang QW, et al. The impact of feed-in tariff reduction and renewable portfolio standard on the development of distributed photovoltaic generation in China . Energy , 2021 , 232 : 120933 . Google Scholar Crossref Search ADS WorldCat [9] Pineda S , Bock A. Renewable-based generation expansion under a green certificate market . Renew Energy , 2016 , 91 : 53 – 63 . Google Scholar Crossref Search ADS WorldCat [10] Zhang YZ , Zhao XG, Ren LZ, et al. The development of China’s biomass power industry under feed-in tariff and renewable portfolio standard: a system dynamics analysis . Energy , 2017 , 139 : 947 – 961 . Google Scholar OpenURL Placeholder Text WorldCat [11] Dong Y , Shimada K. Evolution from the renewable portfolio standards to feed-in tariff for the deployment of renewable energy in Japan . Renew Energy , 2017 , 107 : 590 – 596 . Google Scholar Crossref Search ADS WorldCat [12] Zhou Y , Zhao XG, Jia XF, et al. Can the renewable portfolio standards improve social welfare in China’s electricity market? . Energy Policy , 2021 , 9 : 112242 . Google Scholar OpenURL Placeholder Text WorldCat [13] Liu DN , Wang WY, Li H, et al. Joint optimization of quota policy design and electric market behavior based on renewable portfolio standard in China . IEEE Access , 2021 , 9 : 113347 – 113361 . Google Scholar Crossref Search ADS WorldCat [14] Wolfgang O , Jaehnert S, Mo B. Methodology for forecasting in the Swedish–Norwegian market for el-certificates . Energy , 2015 , 88 : 322 – 333 . Google Scholar Crossref Search ADS WorldCat [15] Zhao XG , Zhou Y, Zuo Y, et al. Research on optimal benchmark price of tradable green certificate based on system dynamics: a China perspective . J Clean Prod , 2019 , 230 : 241 – 252 . Google Scholar Crossref Search ADS WorldCat [16] Zuo Y , Zhao XG, Meng X, et al. Research on tradable green certificate benchmark price and technical conversion coefficient: bargaining-based cooperative trading . Energy , 2020 , 208 : 118376 . Google Scholar OpenURL Placeholder Text WorldCat [17] Zhao XG , Xu L, Zhou Y. How to promote the effective implementation of China’s renewable portfolio standards considering non-neutral technology? Energy , 2022 , 238 : 121748 . Google Scholar OpenURL Placeholder Text WorldCat [18] Song XH , Han JJ, Shan YQ, et al. Efficiency of tradable green certificate markets in China . J Clean Prod , 2020 , 264 : 121518 . Google Scholar Crossref Search ADS WorldCat [19] Zhou Y , Zhao XG, Wang Z. Demand side incentive under renewable portfolio standards: a system dynamics analysis . Energy Policy , 2020 , 114 : 111652 . Google Scholar OpenURL Placeholder Text WorldCat [20] National Energy Administration. Notice on the establishment and improvement of the renewable energy power consumption guarantee mechanism. China, 10 May 2019. http://zfxxgk.nea.gov.cn/auto87/201905/t20190515_3662.htm ( 29 May 2022 , date last accessed). [21] National Energy Administration. Supplementary notice on matters relating to certain opinions on promoting the healthy development of non-water renewable energy power generation. China, 29 September 2020. http://jjs.mof.gov.cn/zhengcefagui/202010/t20201015_3604104.htm. ( 29 May 2022 , date last accessed). [22] National Energy Administration. Notice of the NEA on printing and distributing the 13th five-year plan for biomass energy development. China, 6 December 2016. http://www.gov.cn/xinwen/2016-12/06/content_5143612.htm ( 29 May 2022 , date last accessed). [23] IEA. Bioenergy Annual Report 2021. April 2022. https://www.ieabioenergy.com/blog/publications/iea-bioenergy-annual-report-2021/ ( 29 May 2022 , date last accessed). [24] Lin BQ , He JX. Is biomass power a good choice for governments in China? Renew Sustain Energy Rev , 2017 , 73 : 1218 – 1230 . Google Scholar Crossref Search ADS WorldCat [25] Mohamed U , Zhao YJ, Yi Q, et al. Evaluation of life cycle energy, economy and CO2 emissions for biomass chemical looping gasification to power generation . Renew Energy , 2021 , 176 : 366 – 387 . Google Scholar Crossref Search ADS WorldCat [26] Liu DN , Liu MG, Xiao BW, et al. Exploring biomass power generation’s development under encouraged policies in China . J Clean Prod , 2020 , 258 : 120786 . Google Scholar Crossref Search ADS WorldCat [27] Zhao XG , Wang JY, Liu XM, et al. China’s wind, biomass and solar power generation: what the situation tells us? . Renew Sustain Energy Rev , 2012 , 16 : 6173 – 6182 . Google Scholar OpenURL Placeholder Text WorldCat [28] Research Report on biomass electricity price policy. BEIPA, November 2018. http://www.cn-bea.com/filedownload/230676 ( 29 May 2022 , date last accessed). [29] China Tradable Green Certificate Certification Platform. http://www.greenenergy.org.cn/ ( 29 May 2022 , date last accessed). [30] Sun Y , Ling J, Qin Y, et al. A bidding optimization method for renewable energy cross-regional transaction under green certificate trading mechanism . Renewable Energy Resources , 2018 , 36 : 942 – 948 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [31] Li YC , Han CY, Xiao YW, et al. Tradable green certificate market transaction based on economic scheduling timing simulation of renewable energy . Smart Power , 2021 , 49 : 58 – 65 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [32] An X , Zhang S, Li X, et al. Two-stage joint equilibrium model of electricity market with tradable green certificates . Trans Inst Meas Control , 2019 , 41 : 1615 – 1626 . Google Scholar Crossref Search ADS WorldCat [33] Xiao Y , Wang XF, Wang XL, et al. Behavior analysis of wind power producer in electricity market . Appl Energy , 2016 , 171 : 325 – 335 . Google Scholar Crossref Search ADS WorldCat [34] National Development and Reform Commission. Notice of the NDRC on matters related to the new energy on grid tariff policy in the 2021 WWW Document. China, 7 June 2021. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202106/t20210611_1283088.html?code=&state=123 ( 29 May 2022 , date last accessed). [35] Zhu JZ , Feng YQ, Xie PP, et al. Equilibrium model of Chinese electricity market considering renewable portfolio standard . Automation of Electric Power Systems , 2019 , 43 : 168 – 175 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [36] Liu YM , Chen HY, Huang L, et al. Equilibrium model of electricity market based on multi-swarm co-evolution . Power System Protection and Control , 2020 , 48 : 38 – 45 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [37] An XN , Zhang SH, Li X. Equilibrium analysis of oligopolistic electricity markets considering tradable green certificates . Automation of Electric Power Systems , 2017 , 41 : 84 – 89 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [38] Sun YQ , Ling J, Qin YH, et al. A bidding optimization method for renewable energy cross-regional transaction under green certificate trading mechanism . Renewable Energy Resources , 2018 , 36 : 942 – 948 (in Chinese). Google Scholar OpenURL Placeholder Text WorldCat [39] A press conference on China’s renewable energy development. China, 30 March 2021. http://www.nea.gov.cn/2021-03/30/c_139846095.htm ( 29 May 2022 , date last accessed). [40] Where will the biomass power generation industry go in 2020? 6 January 2020. http://www.forestry.gov.cn/zlszz/4264/20200106/164859172722924.html ( 29 May 2022 , date last accessed). [41] Song XH , Han JJ, Zhang L, et al. Impacts of renewable portfolio standards on multi-market coupling trading of renewable energy in China: a scenario-based system dynamics model . Energy Policy , 2021 , 159 : 112647 . Google Scholar Crossref Search ADS WorldCat [42] Dong ZJ , Yu XY, Chang CT, et al. How does feed-in tariff and renewable portfolio standard evolve synergistically? An integrated approach of tripartite evolutionary game and system dynamics . Renew Energy , 2022 , 186 : 864 – 877 . Google Scholar Crossref Search ADS WorldCat [43] Fang DB , Zhao CY, Kleit AN. The impact of the under enforcement of RPS in China: an evolutionary approach . Energy Policy , 2019 , 135 : 111021 . Google Scholar Crossref Search ADS WorldCat © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com © The Author(s) 2022. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy

Journal

Clean EnergyOxford University Press

Published: Aug 1, 2022

References