## Experimental performance analysis of an installed microgrid-based PV/battery/EV grid-connected system

**Abstract**

Abstract Global energy demand, which is largely based on fossil fuels, is expected to increase rapidly. An effort must be made to mitigate carbon emissions and climate change to ensure sustainable and clean development. In recent years, the increasing share of renewable energy and energy-storage systems, the development of electric vehicles (EVs), promotion of energy efficiency and demand-side management (DSM) have become today’s solution technologies. The microgrid (MG), which involves the interconnection of several generation and storage units capable of operating locally with or without connection to the power grid, is also a very useful emerging technology. This study allowed the experimental operation and performance analysis of a grid-connected photovoltaic (PV)/battery/EV MG hybrid system, which was used for maximizing PV self-consumption and DSM objectives. The entire MG consisting of two subsystems (polycrystalline PV array of 2.16 kWp for Subsystem 1, monocrystalline PV system of 2.4 kWp for Subsystem 2, EV with lithium-ion battery capacity of 6.1 kWh) was installed under outdoor conditions at the University Institute of Technology in Mulhouse, France in August 2018. The operation and behaviour of the system components, including the inverter, batteries and power grid, were analysed in both scenarios with and without EV connection. The results shows that the total cumulative energy injected into the grid during the entire system operating cycle is estimated to be 3466.82 and 5836.58 kWh for Subsystems 1 and 2, respectively. In addition, the energy produced by Subsystem 2 during its lifetime and the emissions emitted are respectively estimated at 5597.65 kWh and 4.17 tons. The other results in terms of PV output power, energy yield, feed-in power and self-consumed energy were quantified and analysed in detail. Open in new tabDownload slide microgrid, photovoltaic, electric vehicle (EV), self-consumption, demand-side management (DSM), energy management Introduction The United Nations Framework Convention on Climate Change was adopted in Paris in 2015 to reduce the impacts and risks of climate change and to keep the average global temperature increase well below 2°C or even 1.5°C [1]. In 2018, greenhouse gas (GHG) emissions from fossil-fuel-based energy production reached a historical level of 33.1 Gigatons. This is the effect of energy production that follows an ever-increasing energy consumption [2]. The results of rapid population growth and economic development are that global energy demand is increasing by ~8–10% per year and is expected to reach 40% by 2040, as shown in Fig. 1. The fastest growth in energy sources is projected for renewable energy (RE), which will provide 14% of primary energy in 2040 [3]. Fig. 1: Open in new tabDownload slide Projected global energy consumption 1970–2040 [3]. One of Europe’s current challenges is to reduce energy consumption, especially in the building sector, which is responsible for >40% of total energy consumption and ~55% of electricity consumption, accounting for 24% of GHG emissions [4]. To this end, the promotion and improvement of energy efficiency, the development of renewable-energy programmes and on-site electricity generation are encouraged by EU rules to promote near-zero energy buildings (NZEBs) with solar PV and solar thermal technologies [5]. Photovoltaic (PV) energy is one of the most important RE resources in many countries, where installed capacity has increased significantly in recent years worldwide. Global installed PV capacity has increased by >4000% since 2007, and by 2017 it had reached 98 GW (including on-grid and off-grid), almost a third of the total 402-GW filler, and by the end of 2018 it will exceed 500 GW [6–8]. The same is true for electric vehicle (EV) technology, which is increasing exponentially, exceeding 3 100 000 vehicles in 2018 against 61 000 in 2011 [9]. PV technology is used to meet the energy demand in different sectors and applications and its utilization for EV recharging has been suggested in the literature [10]. In general, there are two types of PV installations: grid-connected and off-grid systems, known as stand-alone systems. The latter is more widely adopted by residential buildings because of its ease of installation without any need for a battery storage system. In this configuration, there is an exchange of energy with the power grid, buying energy from the grid when there is a need for electricity and selling the surplus when the PV system generates more energy compared to the building’s load [11]. Today, the recent trend towards the use of distributed generation, such as solar PV, solar thermal energy, biomass and wind turbines, has led to the increasing use of hybrid poly-generation and microgrid (MG) systems to meet electricity needs, which is important from both sustainability and energy security perspectives [12, 13]. MG energy systems represent a natural step in the evolution towards smart grids. Essentially, this system is an active distribution network composed of distributed generation technologies that are located near load centres to supply a specific localized area at the distribution voltage level [14]. The generators used in an MG are typically renewable and/or non-conventional distributed energy resources based on local distributed power generation, storage and various controllable and even non-controllable loads. In addition, due to their advantage of autonomous operation, MGs are attracting attention in various fields and applications such as electrification of villages and remote areas, industries, communities, residential, etc. [15]. The MG can operate on alternating current (AC), direct current (DC) or a combination of both. Fig. 2 shows an example of the general structure of an MG. The structure includes two AC and DC buses, power converters, power sources and storage units, and of course the connected loads [16]. Fig. 2: Open in new tabDownload slide Structural example of a hybrid microgrid. In terms of connection, MGs can operate in two modes: in autonomous mode and in grid-connected mode. In autonomous mode, the MG provides all connected loads. On the other hand, the MG imports or exports electricity with the power grid and other MGs in the system when it is in grid-connected mode. In addition, RE can be used to reduce peaks and troughs in the load curve, thereby reducing the deficit and the cost of electricity [14, 15, 17]. According to [16, 18], the advantages of the MG include optimizing energy use, reducing GHG emissions, improving energy quality, reducing energy losses and grid congestion, and increasing energy efficiency. In addition, it eliminates the need to invest in additional generation and transmission infrastructure to power remote loads. In recent years, the integration of storage batteries into electric MGs has received considerable attention. Furthermore, the power grid MG system is a means to increase the use of RE, including high-efficiency cogeneration and/or trigeneration systems. The utility of MGs based on renewable energy sources (RES) is that they can be used to reduce the carbon intensity of electricity and achieve the global decarburization target by 2050, where operating costs and emissions depend mainly on the types of distributed energy resources used [19]. There is a large body of research literature on MG technology that is used in different applications and in different parts of the world. These studies generally address the planning and design aspect, sizing, control and energy management of MGs, optimization and review. In the literature, several energy configurations are studied with different renewable and non-renewable components, including the fuel cell, gas turbine, hydrogen production unit, wind turbine, thermal storage system, pumped storage system, micro-turbines, pumped hydro storage, diesel generator and combined heat and power. The study of these different electrical energy systems and MGs is performed taking into account many economic parameters, including the economic cost, cost of energy or levelized cost of energy, MG cost, net present cost, operation cost, payback period, pollution cost, lifetime benefits and annual electricity cost. Technical indices are also addressed such as the energy generation, electricity consumption, the excess energy (EE), operation efficiency, optimized self-consumption, optimization problem, power balance, renewable-energy friendliness index, reliability indices, system security, independence performance index, loss of power supply probability, energy management system and voltage deviation, in addition to the environmental parameters for example the GHG emissions and the emission cost EC. As shown in Table 1 and based on the research papers in the literature [20–52], a large amount of software and many algorithms are used for the study and optimization of energy systems, including the analytical problem analysis, artificial neural networks, crow search algorithm, energy flow management algorithm, genetic algorithm, hybrid economic model predictive control, iterative optimization method, multi-agent system, moth flame optimization algorithm, mixed-integer linear programming, mixed-integer non-linear programming, multi-objective crow search algorithm, multi-objective seeker optimization algorithm, particle swarm optimization, stochastic compromise programming and the two-archive many-objective evolutionary algorithm. The general algebraic modelling system, hybrid optimization of multiple energy resources (HOMER), Matrix laboratory and the transient energy system simulation program are some of the software used. Table 1: Literature research paper on microgrid Architecture . Mode . Analysed metrics . Methodology . Ref. . PV/battery microgrid Grid-connected EG exchange GA [20] MGS-based PV/PHS Grid-connected AEC, PSP and LTM ANNs [21] PV/WT/battery/DG Off-grid LCOE, REFI HOMER [22] PV/battery/DG Off-grid CoE, VD and CE GA [23] PV/DG/battery/EV Grid-connected CE, OE and SS MSOA [24] WT/ESS/TSS/PHEV/CHP/boiler Grid-connected TC, EC and CPS MINLP [25] PV/battery/CHP/MT/FC Grid-connected MGC and EC HVAC and DSM [26] MGS-based PV/ABESS system Grid-connected RI Markov [27] PV/WT/DG/FC/MT/battery Grid-connected IPI, CO2 emission and cost SCP [28] Photovoltaic, storage and BIPV Grid-connected OSC EFMA [29] PV/WT/BESS/TESS/FC/boiler Grid-connected TC, GHE emission and FC PSO [30] SB based CHP/battery/TS Grid-connected OC GAMS, MIP [31] Microgrid-based PV/battery/WT Grid-connected EM MPC [32] PV/battery/DG Off-grid CoE, EE and NPC HOMER [33] PV/WT/battery/DG Grid-connected Reliability, cost and emission TA-MaEA [34] PV/battery/WT/DG/HPS/EV Off-grid CoE, EE, emission, NPC and RF IOM [35] PV/wind/battery Grid-connected Cost HEMPC [36] HES-based PV/battery/DG Off-grid Emission, unmet load and TSC PSO [37] PV/battery Grid-connected NPC, CoE and emission HOMER [38] PV/WT/battery/biodiesel Off-grid OC MILP [39] PV/wind/battery/diesel Off-grid NPC, CoE, emission and power quality HOMER and MATLAB® [40] PV/battery/WT-based HES Off-grid LPSP CSA and GA [41] PV/battery/wind/GT Grid-connected OC MFOA [42] HES-based PV/WT/battery Off-grid CoE, LPSP and OC GA, HOMER [43] PV/battery/FC/HPU Off-grid Control MATLAB® [44] PV/Batterybased HES system Off-grid CoE, LPSP and OC Monte Carlo [45] PVSHP/ESS/VE Grid-connected Voltage and losses MAS [46] HES-based PV/battery/WT Off-grid NPC and CoE MATLAB®, HOMER [47] HES-based PV/battery/WT Grid-connected RI TRNSYS [48] Grid-connected MGS Grid-connected CoE, emission and TNPC HOMER [49] PV/DG hybrid energy system Off-grid Emission, LPSP and NPC MOCSA [50] PV/battery/DG/hydrogen Off-grid NPC, CoE, RF, emission HOMER [51] PV/battery/DG housing MG Off-grid Emission and TNPC HOMER [52] Architecture . Mode . Analysed metrics . Methodology . Ref. . PV/battery microgrid Grid-connected EG exchange GA [20] MGS-based PV/PHS Grid-connected AEC, PSP and LTM ANNs [21] PV/WT/battery/DG Off-grid LCOE, REFI HOMER [22] PV/battery/DG Off-grid CoE, VD and CE GA [23] PV/DG/battery/EV Grid-connected CE, OE and SS MSOA [24] WT/ESS/TSS/PHEV/CHP/boiler Grid-connected TC, EC and CPS MINLP [25] PV/battery/CHP/MT/FC Grid-connected MGC and EC HVAC and DSM [26] MGS-based PV/ABESS system Grid-connected RI Markov [27] PV/WT/DG/FC/MT/battery Grid-connected IPI, CO2 emission and cost SCP [28] Photovoltaic, storage and BIPV Grid-connected OSC EFMA [29] PV/WT/BESS/TESS/FC/boiler Grid-connected TC, GHE emission and FC PSO [30] SB based CHP/battery/TS Grid-connected OC GAMS, MIP [31] Microgrid-based PV/battery/WT Grid-connected EM MPC [32] PV/battery/DG Off-grid CoE, EE and NPC HOMER [33] PV/WT/battery/DG Grid-connected Reliability, cost and emission TA-MaEA [34] PV/battery/WT/DG/HPS/EV Off-grid CoE, EE, emission, NPC and RF IOM [35] PV/wind/battery Grid-connected Cost HEMPC [36] HES-based PV/battery/DG Off-grid Emission, unmet load and TSC PSO [37] PV/battery Grid-connected NPC, CoE and emission HOMER [38] PV/WT/battery/biodiesel Off-grid OC MILP [39] PV/wind/battery/diesel Off-grid NPC, CoE, emission and power quality HOMER and MATLAB® [40] PV/battery/WT-based HES Off-grid LPSP CSA and GA [41] PV/battery/wind/GT Grid-connected OC MFOA [42] HES-based PV/WT/battery Off-grid CoE, LPSP and OC GA, HOMER [43] PV/battery/FC/HPU Off-grid Control MATLAB® [44] PV/Batterybased HES system Off-grid CoE, LPSP and OC Monte Carlo [45] PVSHP/ESS/VE Grid-connected Voltage and losses MAS [46] HES-based PV/battery/WT Off-grid NPC and CoE MATLAB®, HOMER [47] HES-based PV/battery/WT Grid-connected RI TRNSYS [48] Grid-connected MGS Grid-connected CoE, emission and TNPC HOMER [49] PV/DG hybrid energy system Off-grid Emission, LPSP and NPC MOCSA [50] PV/battery/DG/hydrogen Off-grid NPC, CoE, RF, emission HOMER [51] PV/battery/DG housing MG Off-grid Emission and TNPC HOMER [52] FC, fuel cell; GT, gas turbine; HPU, hydrogen production unit; WT, wind turbine; TSS, thermal storage system; MTs, micro-turbines; PHS, pumped hydro storage; DG, diesel generator; CHP, combined heat and power; EC, economic cost; CoE, cost of energy; LCOE, levelized cost of energy; MGC, MG cost; NPC, net present cost; OC, operation cost; LTM, lifetime benefits; AEC, annual electricity cost; EG, energy generation; EE, excess energy; OE, operation efficiency; OSC, optimized self-consumption; REFI, renewable-energy friendliness index; RI, reliability indices; SS, system security; IPI, independence performance index; LPSP, loss of power supply probability; VD, voltage deviation; ANNs, artificial neural networks; CSA, crow search algorithm; EFMA, energy flow management algorithm; GA, genetic algorithm; HEMPC, hybrid economic model predictive control; IOM, iterative optimization method; MAS, multi-agent system; MFOA, moth flame optimization algorithm; MILP, mixed-integer linear programming; MINLP, mixed-integer non-linear programming; MOCSA, multi-objective crow search algorithm; PSO, particle swarm optimization; SCP, stochastic compromise programming; TA-MaEA, two-archive many-objective evolutionary algorithm; GAMS, general algebraic modelling system; HOMER, hybrid optimization of multiple energy resources; MATLAB®, Matrix laboratory; TRNSYS, transient energy system simulation program; HES, hybrid energy system. Open in new tab Table 1: Literature research paper on microgrid Architecture . Mode . Analysed metrics . Methodology . Ref. . PV/battery microgrid Grid-connected EG exchange GA [20] MGS-based PV/PHS Grid-connected AEC, PSP and LTM ANNs [21] PV/WT/battery/DG Off-grid LCOE, REFI HOMER [22] PV/battery/DG Off-grid CoE, VD and CE GA [23] PV/DG/battery/EV Grid-connected CE, OE and SS MSOA [24] WT/ESS/TSS/PHEV/CHP/boiler Grid-connected TC, EC and CPS MINLP [25] PV/battery/CHP/MT/FC Grid-connected MGC and EC HVAC and DSM [26] MGS-based PV/ABESS system Grid-connected RI Markov [27] PV/WT/DG/FC/MT/battery Grid-connected IPI, CO2 emission and cost SCP [28] Photovoltaic, storage and BIPV Grid-connected OSC EFMA [29] PV/WT/BESS/TESS/FC/boiler Grid-connected TC, GHE emission and FC PSO [30] SB based CHP/battery/TS Grid-connected OC GAMS, MIP [31] Microgrid-based PV/battery/WT Grid-connected EM MPC [32] PV/battery/DG Off-grid CoE, EE and NPC HOMER [33] PV/WT/battery/DG Grid-connected Reliability, cost and emission TA-MaEA [34] PV/battery/WT/DG/HPS/EV Off-grid CoE, EE, emission, NPC and RF IOM [35] PV/wind/battery Grid-connected Cost HEMPC [36] HES-based PV/battery/DG Off-grid Emission, unmet load and TSC PSO [37] PV/battery Grid-connected NPC, CoE and emission HOMER [38] PV/WT/battery/biodiesel Off-grid OC MILP [39] PV/wind/battery/diesel Off-grid NPC, CoE, emission and power quality HOMER and MATLAB® [40] PV/battery/WT-based HES Off-grid LPSP CSA and GA [41] PV/battery/wind/GT Grid-connected OC MFOA [42] HES-based PV/WT/battery Off-grid CoE, LPSP and OC GA, HOMER [43] PV/battery/FC/HPU Off-grid Control MATLAB® [44] PV/Batterybased HES system Off-grid CoE, LPSP and OC Monte Carlo [45] PVSHP/ESS/VE Grid-connected Voltage and losses MAS [46] HES-based PV/battery/WT Off-grid NPC and CoE MATLAB®, HOMER [47] HES-based PV/battery/WT Grid-connected RI TRNSYS [48] Grid-connected MGS Grid-connected CoE, emission and TNPC HOMER [49] PV/DG hybrid energy system Off-grid Emission, LPSP and NPC MOCSA [50] PV/battery/DG/hydrogen Off-grid NPC, CoE, RF, emission HOMER [51] PV/battery/DG housing MG Off-grid Emission and TNPC HOMER [52] Architecture . Mode . Analysed metrics . Methodology . Ref. . PV/battery microgrid Grid-connected EG exchange GA [20] MGS-based PV/PHS Grid-connected AEC, PSP and LTM ANNs [21] PV/WT/battery/DG Off-grid LCOE, REFI HOMER [22] PV/battery/DG Off-grid CoE, VD and CE GA [23] PV/DG/battery/EV Grid-connected CE, OE and SS MSOA [24] WT/ESS/TSS/PHEV/CHP/boiler Grid-connected TC, EC and CPS MINLP [25] PV/battery/CHP/MT/FC Grid-connected MGC and EC HVAC and DSM [26] MGS-based PV/ABESS system Grid-connected RI Markov [27] PV/WT/DG/FC/MT/battery Grid-connected IPI, CO2 emission and cost SCP [28] Photovoltaic, storage and BIPV Grid-connected OSC EFMA [29] PV/WT/BESS/TESS/FC/boiler Grid-connected TC, GHE emission and FC PSO [30] SB based CHP/battery/TS Grid-connected OC GAMS, MIP [31] Microgrid-based PV/battery/WT Grid-connected EM MPC [32] PV/battery/DG Off-grid CoE, EE and NPC HOMER [33] PV/WT/battery/DG Grid-connected Reliability, cost and emission TA-MaEA [34] PV/battery/WT/DG/HPS/EV Off-grid CoE, EE, emission, NPC and RF IOM [35] PV/wind/battery Grid-connected Cost HEMPC [36] HES-based PV/battery/DG Off-grid Emission, unmet load and TSC PSO [37] PV/battery Grid-connected NPC, CoE and emission HOMER [38] PV/WT/battery/biodiesel Off-grid OC MILP [39] PV/wind/battery/diesel Off-grid NPC, CoE, emission and power quality HOMER and MATLAB® [40] PV/battery/WT-based HES Off-grid LPSP CSA and GA [41] PV/battery/wind/GT Grid-connected OC MFOA [42] HES-based PV/WT/battery Off-grid CoE, LPSP and OC GA, HOMER [43] PV/battery/FC/HPU Off-grid Control MATLAB® [44] PV/Batterybased HES system Off-grid CoE, LPSP and OC Monte Carlo [45] PVSHP/ESS/VE Grid-connected Voltage and losses MAS [46] HES-based PV/battery/WT Off-grid NPC and CoE MATLAB®, HOMER [47] HES-based PV/battery/WT Grid-connected RI TRNSYS [48] Grid-connected MGS Grid-connected CoE, emission and TNPC HOMER [49] PV/DG hybrid energy system Off-grid Emission, LPSP and NPC MOCSA [50] PV/battery/DG/hydrogen Off-grid NPC, CoE, RF, emission HOMER [51] PV/battery/DG housing MG Off-grid Emission and TNPC HOMER [52] FC, fuel cell; GT, gas turbine; HPU, hydrogen production unit; WT, wind turbine; TSS, thermal storage system; MTs, micro-turbines; PHS, pumped hydro storage; DG, diesel generator; CHP, combined heat and power; EC, economic cost; CoE, cost of energy; LCOE, levelized cost of energy; MGC, MG cost; NPC, net present cost; OC, operation cost; LTM, lifetime benefits; AEC, annual electricity cost; EG, energy generation; EE, excess energy; OE, operation efficiency; OSC, optimized self-consumption; REFI, renewable-energy friendliness index; RI, reliability indices; SS, system security; IPI, independence performance index; LPSP, loss of power supply probability; VD, voltage deviation; ANNs, artificial neural networks; CSA, crow search algorithm; EFMA, energy flow management algorithm; GA, genetic algorithm; HEMPC, hybrid economic model predictive control; IOM, iterative optimization method; MAS, multi-agent system; MFOA, moth flame optimization algorithm; MILP, mixed-integer linear programming; MINLP, mixed-integer non-linear programming; MOCSA, multi-objective crow search algorithm; PSO, particle swarm optimization; SCP, stochastic compromise programming; TA-MaEA, two-archive many-objective evolutionary algorithm; GAMS, general algebraic modelling system; HOMER, hybrid optimization of multiple energy resources; MATLAB®, Matrix laboratory; TRNSYS, transient energy system simulation program; HES, hybrid energy system. Open in new tab Table 1 summarizes the various study reports on MGs, the configurations used, the connection modes, the analysed metrics and the MG analysis methodologies. Based on the comprehensive literature listed in Table 1, the following highlights can be summarized: there are a large number of studies on MGs, which show their advantage as a new power generation technology based on RES to reduce CO2 emissions and the problem of climate change; MGs take many configurations based on RE, unconventional sources, different types of storage and different load demands. However, the fully renewable configuration such as PV/battery/EV is little discussed in these research papers; regarding MG analysis methodology, including sizing, operational feasibility, control, energy management and performance analysis, most of the approaches are analysed by simulation with different algorithms and software; however, experimental analysis under real conditions is better, which is missing in most of the papers reviewed; from an application point of view, it has been found that MGs are used in a variety of applications, including village electrification, remote sites, water supply, etc. In contrast, MGs in academic institutions are very limited; regarding the objectives of MG installation, the objective of PV self-consumption maximization is not much mentioned. However, this paper aims to address all of the gaps examined in the literature. It focuses on the experimental operation, power performance analysis and energy management of a hybrid MG fully based on renewable energies installed in outdoor conditions at the University Institute of Technology (IUT) in Mulhouse, France. The main focus is on the experimental analysis of a PV/battery/EV grid-connected MG system taking into account the behaviour of each component of the MG, which is composed of two subsystems with two PV generators of different technologies (an EV, an electrical storage system, energy converters, the main grid and electrical loads). The objective of this MG is to manage the local consumed load by maximizing the PV self-consumption of locally produced energy to reduce dependence on the conventional power grid. The behaviour of the system components over two different days with and without the connection of EV is analysed, including the behaviour of the inverters and batteries, for the two subsystems constituting the entire MG system. PV output power, inverter output power, self-consumed energy and supply power are evaluated and discussed. This paper is structured in six sections, starting with an introduction in Section 1. In Section 2, the approach of PV self-consumption and demand-side management (DSM), as well as ways to increase self-consumption, such as the use of storage batteries, is discussed in detail. Section 3 provides background information on the case study in terms of site location and resources (solar, wind, temperature, etc.). Section 4 describes the hybrid MG PV/battery/EV grid-connected system studied, including its two subsystems and the presentation of component specifications. The modelling of the system components, including the PV array, inverter and batteries, is presented in Section 5. The control strategy and the operation of the investigated hybrid MG are discussed in Section 6. System operation and performance analysis are presented and discussed in Section 7. Finally, the conclusions of this study are summarized in Section 8. 1 PV self-consumption and DSM approaches Solar PV systems have grown significantly over the past decades, mainly due to the decrease in the price of PV solar modules and the total cost of installation. In addition, governments have established support rules and financial incentives such as net metering, feed-in tariffs and net-billing configurations [53, 54]. The concept of PV self-consumption is also one of the applications that is attracting great interest from users and the scientific community. It is defined as that part of the PV electricity production that is directly consumed by the owner of the PV system [55]. Fig. 3 schematically defines the concept of self-consumption by showing the profiles of electricity consumption and on-site PV electricity generation. There are three zones; Zones A and B represent the total net electricity production and load demand, respectively. Zone C shows the PV power used directly by the consumer using the PV system (e.g. a building), which is sometimes referred to as absolute PV self-consumption [56]. However, the most commonly used term is PV self-consumption. It is defined as the share of self-consumed energy in relation to total power production and expressed by Equation (1) as follows: Fig. 3: Open in new tabDownload slide Diagram of the PV self-consumption concept with the area (A + C) is the net daily load, the area (B + C) is the net PV generation and the area (C) is the absolute PV self-consumption [55] (with permission from Elsevier). Self−Consumption=CB+C(1) In addition, self-sufficiency refers to the extent to which on-site energy production is sufficient to meet the client’s energy needs [57]. System self-sufficiency is expressed by Equation (2). More details on these two nomenclatures are given in [55]. Self−sufficiency=CA+C(2) There are different methods and technologies to increase the self-consumption of PV energy. Power and thermal storage are the main ways to make residential and commercial buildings more self-sufficient in the future [58], including residential storage based on stationary batteries used only with the PV system. There are several different battery technologies on the market that are suitable for storing power in homes, such as lithium-ion (Li-ion), nickel–metal hydride, domestic lead–acid, nickel–cadmium (NiCd) and sodium–sulphur (NaS) batteries. Given their high storage efficiency and energy density, lithium-ion batteries are becoming popular and affordable, and offer the greatest potential for future development with their cost continuously decreasing [59, 60]. More detailed information on the different energy-storage systems (ESSs) is available in the literature [61]. According to [54], the integration of battery storage energy systems with residential PV systems can significantly increase the self-consumption and energy self-sufficiency of households. Of course, increasing the self-consumption of the PV system can be beneficial for both utilities and end-users, and can be achieved efficiently by using storage systems such as batteries and flexible load with EVs and heat pumps [62, 63]. PV self-consumption is also increased by the use of battery storage [64–66] and using EV as a storage system [67–69]. PV self-consumption without batteries is discussed in Ref. [4]. Another alternative to battery storage is used to increase the self-consumption of PV systems, namely DSM, which is generally defined by Gellings [70] as the planning and implementation of activities to influence the use of electricity by consumers to change the load curve in terms of magnitude and/or time of energy consumption. The aim is to save on energy consumption and costs. There are a number of DSM techniques [71–73], the most commonly used being peak clipping, load shifting, load building, valley filling, strategic conservation and flexible load, as shown in Fig. 4. A more detailed description of this concept and its strategies can be found in the literature [74]. Fig. 4: Open in new tabDownload slide Demand-side management objectives. In addition, DSM techniques can be used separately or in combination with battery storage as a means to increase PV self-consumption as in [75, 76]. Fig. 3 illustrates the technique of load shifting in PV self-consumption. 2 Investigated MG description 2.1 Site location The system under study is located at the IUT in Mulhouse, France, geographically situated at a latitude of 47°43,8′N, a longitude of 7°18,1′E and an altitude of 240 m above sea level. Fig. 5 shows the geographical location of the study site on the map of France. Fig. 5: Open in new tabDownload slide Geographical location of the study site. 2.2 Local resources assessment The electricity production of the PV system is mainly influenced by meteorological data, i.e. solar radiation and ambient temperature. Therefore, the assessment of resource data was necessary for the modelling of the PV system. Data in terms of solar radiation, ambient temperature and wind speed in this study were collected for evaluation and analysis, and were obtained from the NASA database website using the site coordinates via the HOMER software [77]. Fig. 6 illustrates the monthly average solar radiation and wind-speed data. In addition, the profiles of ambient temperature and clearness index are shown in Fig. 7. Fig. 6: Open in new tabDownload slide Monthly daily average solar radiation data and wind speed. Fig. 7: Open in new tabDownload slide Monthly daily average ambient temperature and clearness index. According to Fig. 6, the monthly average daily solar irradiation for the site ranges from the lowest irradiation month with 0.98 kWh/m2/day to the highest month of July with 5.75 kWh/m2/day, and the yearly average solar irradiation was estimated at 3.33 kWh/m2/day. However, the highest monthly average wind speed was recorded in January (6.40 m/s) and the lowest in June (3.71 m/s), with an annual average of 4.86 m/s. Based on the results, solar energy at this site is highest in summer and lowest in winter; the opposite is observed for the wind-speed profile. Considering these characteristics, the hybridization of wind and solar PV energy sources in the region can give good results and high efficiency. As shown in Fig. 7, the highest ambient temperature was recorded in July and August with 17.22°C, while the lowest temperature was observed in January and February with –1.89°C and –0.72°C respectively. The clearness index has the minimum, maximum and average annual values of 0.393 in December, 0.52 in August and 0.460, respectively. 3 MG PV/battery/EV grid-connected system 3.1 Overall system description This section describes the MG system under consideration. The overall hybrid MG PV/battery/EV grid-connected energy system in its design is mainly composed of two subsystems: Bike Shelter and UHA Trackers. The entire system is composed of a number of elements, including two PV generators, micro-inverters, two inverters connected to two battery storage units, power grid equipment and a net meter, an EV and some other loads. Note that the methods and equipment used to perform the measurements and the performance analysis of the MG are based on the information and data obtained from the intelligent inverters that have the capacity to give the values of the different analysis parameters (energy production, consumption, efficiency, etc.). In addition, there is an online system designed specifically for the monitoring and control of the various MG components (PV systems, batteries and inverters). The online acquisition unit was set up for real-time monitoring of the system every 5 minutes. The schematic diagram of the entire hybrid MG system under study is shown in Fig. 8. In addition, Fig. 9 illustrates the real photos of the system components. Fig. 8: Open in new tabDownload slide Investigated microgrid PV/battery/EV grid-connected system. Fig. 9: Open in new tabDownload slide Photos of the microgrid components. 3.2 Subsystem 1: Bike Shelter and associated inverter The polycrystalline PV array with a capacity of 2.16 kWp installed in this system consists of eight solar modules with a rated power of 270 W each. The system was installed on the roof of the Bike Shelter at a fixed-tilt angle of 45°C facing south-west. An associated inverter, called a ‘retrofit inverter’, was used in this subsystem to match the electricity from the PV panel and to convert the direct current (DC) produced into AC and then to feed it into the power grid with the same synchronization (50–60 Hz) and voltage (230 V). This retrofit inverter makes it possible to intelligently meet electricity needs by selecting the most economical source between the solar panels, batteries and the power grid. The SOLAX retrofit inverter was used and its specifications are listed in Table 2. Table 2: Technical specifications of the retrofit inverter Parameters . Value . Output AC Nominal AC power (W) 3680 Max. apparent AC power (VA) 3680 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Nominal AC current (A) 16 Max. AC current (A) 16 Input AC Nominal AC power (W) 7680 Max. AC current (A) 16 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Dimensions (W × H × D) (mm) 482 × 464 × 182 Weight (kg) 26.9 Parameters . Value . Output AC Nominal AC power (W) 3680 Max. apparent AC power (VA) 3680 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Nominal AC current (A) 16 Max. AC current (A) 16 Input AC Nominal AC power (W) 7680 Max. AC current (A) 16 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Dimensions (W × H × D) (mm) 482 × 464 × 182 Weight (kg) 26.9 Open in new tab Table 2: Technical specifications of the retrofit inverter Parameters . Value . Output AC Nominal AC power (W) 3680 Max. apparent AC power (VA) 3680 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Nominal AC current (A) 16 Max. AC current (A) 16 Input AC Nominal AC power (W) 7680 Max. AC current (A) 16 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Dimensions (W × H × D) (mm) 482 × 464 × 182 Weight (kg) 26.9 Parameters . Value . Output AC Nominal AC power (W) 3680 Max. apparent AC power (VA) 3680 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Nominal AC current (A) 16 Max. AC current (A) 16 Input AC Nominal AC power (W) 7680 Max. AC current (A) 16 Rated grid voltage (AC voltage range) (V) 230 Rated grid frequency (Hz) 50/60 Dimensions (W × H × D) (mm) 482 × 464 × 182 Weight (kg) 26.9 Open in new tab 3.3 Subsystem 2: UHA Trackers and associated inverter The associated PV system consists of a PV array, a micro-inverter and solar-tracking equipment. The eight PV modules of monocrystalline technology with a nominal output of 230 Wp each, with a total installed power of 2.4 kWp, were mounted on a south-facing metal structure. In addition, horizontal solar-tracking equipment was integrated into the PV system to maximize the efficiency of solar energy conversion. The detailed specifications of the PV modules used in the standard test condition (STC, 1000 W/m2, 25°C) and the main technical parameters of the micro-inverter are summarized in Tables 3 and 4, respectively, in which the maximum power point tracking (MPPT) efficiency and the voltage at the maximum power point (MPP) are given. It should be noted that every two solar PV modules are connected to a micro-inverter, as shown in Fig. 10. Table 3: Detailed technical and electrical specification of PV module Manufacturer . VOLTEC . Cells type Monocrystalline Voltage at max. power, Vpmax 33.4 V Current at max. power, Ipmax 9.0 A Maximum power at STC 300 Wp Short-circuit current 9.5 A Open-circuit voltage 40.4 V Temperature coefficient of Pmax –0.395%/°C Short-circuit current coefficient 0.027%/°C Open-circuit voltage coefficient –0.293%/°C Power tolerance From +0 to +5 Wp Area performance 18.1% Dimensions and quantity/panel 156 × 156 mm/60 cells Weight 18.6 kg Manufacturer . VOLTEC . Cells type Monocrystalline Voltage at max. power, Vpmax 33.4 V Current at max. power, Ipmax 9.0 A Maximum power at STC 300 Wp Short-circuit current 9.5 A Open-circuit voltage 40.4 V Temperature coefficient of Pmax –0.395%/°C Short-circuit current coefficient 0.027%/°C Open-circuit voltage coefficient –0.293%/°C Power tolerance From +0 to +5 Wp Area performance 18.1% Dimensions and quantity/panel 156 × 156 mm/60 cells Weight 18.6 kg Open in new tab Table 3: Detailed technical and electrical specification of PV module Manufacturer . VOLTEC . Cells type Monocrystalline Voltage at max. power, Vpmax 33.4 V Current at max. power, Ipmax 9.0 A Maximum power at STC 300 Wp Short-circuit current 9.5 A Open-circuit voltage 40.4 V Temperature coefficient of Pmax –0.395%/°C Short-circuit current coefficient 0.027%/°C Open-circuit voltage coefficient –0.293%/°C Power tolerance From +0 to +5 Wp Area performance 18.1% Dimensions and quantity/panel 156 × 156 mm/60 cells Weight 18.6 kg Manufacturer . VOLTEC . Cells type Monocrystalline Voltage at max. power, Vpmax 33.4 V Current at max. power, Ipmax 9.0 A Maximum power at STC 300 Wp Short-circuit current 9.5 A Open-circuit voltage 40.4 V Temperature coefficient of Pmax –0.395%/°C Short-circuit current coefficient 0.027%/°C Open-circuit voltage coefficient –0.293%/°C Power tolerance From +0 to +5 Wp Area performance 18.1% Dimensions and quantity/panel 156 × 156 mm/60 cells Weight 18.6 kg Open in new tab Table 4: Technical specifications of the micro-inverter Model . Type YC600 . Input data (DC) Recommended module power (STC) 250–365 Wp/PV modules of 60 and 72 cells Maximum DC input voltage 55 V Maximum DC input current 12 A × 2 Operating voltage range 16–55 V Output data (AC) Maximum output power 600 VA Rated output voltage 230 V Rated output current 2.39 A Nominal frequency 50 Hz Efficiency Maximum efficiency 95.5% Nominal MPPT efficiency 99.5% Model . Type YC600 . Input data (DC) Recommended module power (STC) 250–365 Wp/PV modules of 60 and 72 cells Maximum DC input voltage 55 V Maximum DC input current 12 A × 2 Operating voltage range 16–55 V Output data (AC) Maximum output power 600 VA Rated output voltage 230 V Rated output current 2.39 A Nominal frequency 50 Hz Efficiency Maximum efficiency 95.5% Nominal MPPT efficiency 99.5% Open in new tab Table 4: Technical specifications of the micro-inverter Model . Type YC600 . Input data (DC) Recommended module power (STC) 250–365 Wp/PV modules of 60 and 72 cells Maximum DC input voltage 55 V Maximum DC input current 12 A × 2 Operating voltage range 16–55 V Output data (AC) Maximum output power 600 VA Rated output voltage 230 V Rated output current 2.39 A Nominal frequency 50 Hz Efficiency Maximum efficiency 95.5% Nominal MPPT efficiency 99.5% Model . Type YC600 . Input data (DC) Recommended module power (STC) 250–365 Wp/PV modules of 60 and 72 cells Maximum DC input voltage 55 V Maximum DC input current 12 A × 2 Operating voltage range 16–55 V Output data (AC) Maximum output power 600 VA Rated output voltage 230 V Rated output current 2.39 A Nominal frequency 50 Hz Efficiency Maximum efficiency 95.5% Nominal MPPT efficiency 99.5% Open in new tab Fig. 10: Open in new tabDownload slide PV panel and micro-inverter connection diagram. The associated inverter (hybrid solar inverter) converts the energy from DC to AC and vice versa when charging and discharging the storage batteries. The AC is fed into the public grid or used to power the connected load. The solar hybrid inverter can be adjusted according to the availability of the energy source, either solar (solar PV plus battery) or grid. In this system, the AC output power was recorded every 5 minutes. It is important to note that the parameters of the PV system were not recorded in this inverter and therefore the conversion efficiency was not analysed. The main specifications of the hybrid inverter used are shown in Table 5. Table 5: Technical data of solar hybrid inverter Parameters . Values . DC input Maximum DC voltage (V) 600 Maximum DC current (A) 10 Minimum MPP voltage (V) 125 Maximum MPP voltage (V) 550 Maximum DC power (kW) 5 AC output and AC input Nominal AC voltage (V) 230 Frequency (Hz) 50–60 Nominal AC apparent power (VA) 3680 Maximum AC output/input current (A) 16/16 Maximum efficiency(%) 97.8 Dimensions (L × W × H) (mm) 477 × 460 × 181.5 Weight (kg) 26.9 Parameters . Values . DC input Maximum DC voltage (V) 600 Maximum DC current (A) 10 Minimum MPP voltage (V) 125 Maximum MPP voltage (V) 550 Maximum DC power (kW) 5 AC output and AC input Nominal AC voltage (V) 230 Frequency (Hz) 50–60 Nominal AC apparent power (VA) 3680 Maximum AC output/input current (A) 16/16 Maximum efficiency(%) 97.8 Dimensions (L × W × H) (mm) 477 × 460 × 181.5 Weight (kg) 26.9 Open in new tab Table 5: Technical data of solar hybrid inverter Parameters . Values . DC input Maximum DC voltage (V) 600 Maximum DC current (A) 10 Minimum MPP voltage (V) 125 Maximum MPP voltage (V) 550 Maximum DC power (kW) 5 AC output and AC input Nominal AC voltage (V) 230 Frequency (Hz) 50–60 Nominal AC apparent power (VA) 3680 Maximum AC output/input current (A) 16/16 Maximum efficiency(%) 97.8 Dimensions (L × W × H) (mm) 477 × 460 × 181.5 Weight (kg) 26.9 Parameters . Values . DC input Maximum DC voltage (V) 600 Maximum DC current (A) 10 Minimum MPP voltage (V) 125 Maximum MPP voltage (V) 550 Maximum DC power (kW) 5 AC output and AC input Nominal AC voltage (V) 230 Frequency (Hz) 50–60 Nominal AC apparent power (VA) 3680 Maximum AC output/input current (A) 16/16 Maximum efficiency(%) 97.8 Dimensions (L × W × H) (mm) 477 × 460 × 181.5 Weight (kg) 26.9 Open in new tab 3.4 Energy-storage system battery The ESS is necessary to improve the efficiency and stability of the system and to maximize self-consumption of energy. In this system, batteries, as a second source after PV systems, are used to store the energy to be used when there is no electricity production from the PV system. A lithium battery (Pylontech) with >6000 cycles and a depth of discharge (DoD) of ≤90% was used at this site. Table 6 summarizes the technical and electrical characteristics of the batteries used. Table 6: Main specifications of used batteries Basic parameters . US2000 (VERSION B) . Nominal voltage (V) 48 Nominal capacity (Ah) 50 Discharge voltage (V) 45~54 Charge voltage (V) 52.5~54 Peak discharge power (W) 5 kW @ 1 min Charge temperature 0~50°C Discharge temperature –10~50°C Storage temperature –40~80°C Lifetime +10 years (25°C/77°F) Cycle life >6000 (25°C, 90% DoD) Dimension (mm) 440 × 410 × 89 Weight (kg) 24 Basic parameters . US2000 (VERSION B) . Nominal voltage (V) 48 Nominal capacity (Ah) 50 Discharge voltage (V) 45~54 Charge voltage (V) 52.5~54 Peak discharge power (W) 5 kW @ 1 min Charge temperature 0~50°C Discharge temperature –10~50°C Storage temperature –40~80°C Lifetime +10 years (25°C/77°F) Cycle life >6000 (25°C, 90% DoD) Dimension (mm) 440 × 410 × 89 Weight (kg) 24 Open in new tab Table 6: Main specifications of used batteries Basic parameters . US2000 (VERSION B) . Nominal voltage (V) 48 Nominal capacity (Ah) 50 Discharge voltage (V) 45~54 Charge voltage (V) 52.5~54 Peak discharge power (W) 5 kW @ 1 min Charge temperature 0~50°C Discharge temperature –10~50°C Storage temperature –40~80°C Lifetime +10 years (25°C/77°F) Cycle life >6000 (25°C, 90% DoD) Dimension (mm) 440 × 410 × 89 Weight (kg) 24 Basic parameters . US2000 (VERSION B) . Nominal voltage (V) 48 Nominal capacity (Ah) 50 Discharge voltage (V) 45~54 Charge voltage (V) 52.5~54 Peak discharge power (W) 5 kW @ 1 min Charge temperature 0~50°C Discharge temperature –10~50°C Storage temperature –40~80°C Lifetime +10 years (25°C/77°F) Cycle life >6000 (25°C, 90% DoD) Dimension (mm) 440 × 410 × 89 Weight (kg) 24 Open in new tab 3.5 EV The site was equipped with an EV (Renault TWIZY 45) that can be plugged and recharged at home on a conventional domestic socket or externally on all public charging stations, including old and new generations, using the standard charging cable. The EV has a power and autonomy (range) of 5 hp and 120 km, respectively, with a lithium-ion battery capacity of 6.1 kWh. The studied MG, with all its components, was connected to the power grid. The French AC power grid has the characteristics of a voltage wave delivered of 230 V in the single-phase system and 400 V in the three-phase system, with a constant frequency of 50/60 Hz. 4 System components modelling This section provides the modelling of the system components, including the PV array, battery and converter. 4.1 PV array modelling The PV module is a set of solar cells connected in series and parallel, which have the ability to convert solar irradiation into direct current through the PV phenomenon. The power produced by the solar PV system depends on the solar irradiation and the ambient temperature and can be estimated using Equation (3) [78] as follows: PPV=YPVfPV=GTGT,STC[1+αp(TC−TC,STC)](3) where YPV represents the rated capacity of the PV array under standard test conditions (kW), fPV represents the de-rating factor (%), GT and GT,STC (kW/m2) represent the solar radiation incident on the PV array and the incident radiation under standard test conditions, αP represents the temperature coefficient of power (%/°C), and Tc (°C) and TC,STC represent the temperature of the PV cell and its temperature under STC (25°C), respectively. The module temperature Tc can be calculated using Equation (4) [79]: Tc=Ta+NOCT−200.8∗G(4) where G represents the solar irradiance (kW/m2), Ta represents the ambient temperature (°C) and NOCT represents the nominal operating cell temperature as specified by the manufacturer’s data sheet (45°C). 4.2 Energy-storage battery The ESS is necessary because of the intermittent nature of RES. In this case, batteries are used to store energy for future use when there is little or no PV power generation [80]. At any given time, the battery state of charge (SoC) is given by Equation (5) [81]: SoC(t)=SoC(0)+ηc∑tk=0PCB(k)+ηd∑tk=0PDP(k)(5) where SoC(0) represents the battery state of charge at t = 0, PCB and PDB represent the electrical power charged and discharged from the battery bank, respectively, and ηc and ηd represent the charging and discharge efficiencies, respectively. The constraints for the available battery capacity are expressed by Equation (6) [81]: {Bmin≤SoC≤BmaxBmin(1−DoD)Bmax(6) where Bmin and Bmax represent the minimum and the maximum power capacity of the battery bank, respectively, and DoD represents the depth of discharge. Equation (7) gives the constraint of the discharge power of the battery bank as follows [81, 82]: 0≤PDB(k)≤Pmax(7) where Pmax is the maximum hourly discharging power. 4.3 Converter The converter in this system is used as an inverter to convert the energy from DC to AC (from the PV generator) to power the load and used as a rectifier to convert the energy from its AC form to DC form (during battery charging). The efficiency of the inverter can be expressed using Equation (8) [81] as follows: Pin=PoutηInv(8) The inverter’s input and output power are given by: PInvOut=PInvIn∗ηInv(9) PInvIn=PPV+PDB(10) where Pin represents the DC power input (kW), Pout represents the AC output power (kW) and ηInv represents the inverter efficiency (%). 5 Energy management of MG The hybrid system is operated according to load/PV/battery/grid priority and the inverter was set to solar priority. This means that the load is supplied by solar energy with priority. If there is not enough PV energy, the battery is used to supplement the remaining load. In case there is not enough energy from both the solar PV and the battery storage, the grid feed is automatically activated. In addition, to maximize the self-consumption of the PV system, the inverter is set so that the batteries are only loaded by the solar PV system. Therefore, the control strategy and the operation of the system can be summarized in three cases, which are summarized below: the load (PLoad) and the PV production (PPV) are equal; the PV power generation exceeds the load (PPV > PLoad); the batteries are fully charged (SoC(t) = SoCmax); the battery state of charge is between minimum and maximum (SoCmin < SoC(t) < SoCmax) and the batteries are completely discharged (SoC(t) = SoCmin or SoC(t) < SoCmin); the load is higher than the PV power output (PPV > PLoad); the battery SoC takes one of the cases noted in the second point. It is important to note that the exchange of energy between the system and the power grid at any given time depends on the energy consumption, the PV power output and the battery SoC. Fig. 11 illustrates the control strategy for the MG hybrid system under study. Fig. 11: Open in new tabDownload slide Flowchart of energy management strategy of the studied microgrid. 6 Discussion on system operation and performance analysis 6.1 Global system analysis This study aims to analyse the behaviour of the system and to determine the energy flow and contribution of each component of the system. The system was analysed during 2 days under different conditions, with and without EV connection. The analysis of the performance of the two subsystems is presented in this section for an arbitrary selected day of 20 March 2020. The following parameters and measurements were taken simultaneously, including the output voltage, current and PV array output power, inverters output power, total feed-in energy, battery status, the total cumulative energy consumption and the energy injected into and/or extracted from the power grid. 6.2 Operation analysis of Subsystem 1: Bike Shelter The solar PV system performance in terms of current, voltage and output power was recorded every 5 minutes. Fig. 12 shows the current and voltage output profile of the PV grid associated with Subsystem 1 for a specific day (20 March 2020). Fig. 12: Open in new tabDownload slide Daily output voltage and current of PV array. Fig. 13 shows the daily output power of the PV system, which depends on the weather conditions at the site at a given time during this same arbitrary day. The maximum PV output power of this system (1632 W) was obtained at 1:32 p.m. Fig. 13: Open in new tabDownload slide Daily instantaneous PV power generated. The maximum output power is observed in the half day, which corresponds to the high amount of solar irradiation received in this period. Fig. 14 provides the instantaneous hourly output power of the inverter as of 20 March 2020. Based on Fig. 14, the positive sign indicates the energy that supplies the electricity load and the negative sign indicates the energy that is used to charge the batteries. Fig. 14: Open in new tabDownload slide Daily output power of the inverter. Energy that is not consumed locally is fed into the power grid with the corresponding voltage and frequency (230 V, 50 Hz). Fig. 15 shows the inverter’s feed-in power profile on the selected day of 20 March 2020. Fig. 15: Open in new tabDownload slide Hourly profile of feed-in power of inverter. It is important to note that up to 20 March 2020, the total cumulative system energy consumption recorded by the inverter is 166.51 kWh. On the other hand, the total cumulative energy injected into the grid by this inverter during the entire system operating cycle is estimated at 3466.82 kWh. The total estimated yield of this inverter is 1613.9 kWh. Fig. 16 shows the feed energy profile and the cumulative energy consumption for that day. Fig. 16: Open in new tabDownload slide Cumulative feed-in energy and energy consumption until 20 March 2020. 6.3 Power flow analysis of Subsystem 2: UHA Trackers This section discusses the power performance analysis of the UHA Trackers subsystem. Fig. 17 presents the hourly profile of the hybrid inverter’s input and output power. It is important to note that the positive sign of the inverter output power is the power delivered to the load. Fig. 17: Open in new tabDownload slide Daily feed-in power and hybrid inverter output power. However, the negative sign means that the power is removed from the storage batteries. Therefore, there is no output power because no energy is consumed. The energy that depends on PV production is injected into the power grid when there is no energy consumed locally or when it is greater than the energy demand. Between August 2018 and 20 March 2020, the total cumulative energy production fed into the grid is 5836.58 kWh, with 242.04 kWh representing the cumulative energy consumption (Fig. 18). In addition, the daily and total cumulative yield up to this date (20 March 2020) are 177.5 and 30.2 kWh, respectively. The energy generated by the PV system (2.4 kWp) over its lifetime and the emissions emitted are estimated at 5597.65 kWh and 4.17 tons, respectively. Fig. 18: Open in new tabDownload slide Cumulative feed-in energy and energy consumption. Fig. 19 indicates the AC voltage, frequency and reference voltage (Vref) of the inverter. The daily average value of AC voltage is ~230 V and the frequency is 50 Hz, which is in accordance with French electricity regulations. Fig. 19: Open in new tabDownload slide Inverter output voltage and reference voltage. 6.4 Results of EV connection This section presents the performance analysis and results of the connection of EV for a randomly selected day (16 July 2020). According to the local weather service, the sun on this day was hidden behind clouds and the temperature was 16°C throughout the day. The EV was plugged in at 9 a.m. with 50% of charge. 6.4.1 Subsystem 1: Bike Shelter A number of parameters were applied to analyse the performance of the two subsystems when the EV was connected on that day. These parameters include: feed-in power, AC output power of inverter, daily feed-in energy, energy self-use and daily yield. Fig. 20 shows the hourly variation in the output current and voltage of the 2-kWp PV system (Bike Shelter) on 16 July 2020. Fig. 20: Open in new tabDownload slide Voltage and current output of PV array. Fig. 21 indicates the daily variation in energy yield that was obtained and reported from all systems containing both subsystems on 16 July 2020. Fig. 21: Open in new tabDownload slide Daily power curve of the total system. Fig. 22 shows the instantaneous profile of the PV output power, the AC output power feeding the load that was obtained after the hybrid DC/AC inverter in 5-minute intervals and also the feed-in power curve. The minimum electricity output of the PV system was ~7 W and the maximum power was 774 kW at 12:25 a.m. It is important to note that the EV was connected at 9 a.m. as indicated by the arrow in Fig. 22. It can be seen that the AC output power increases when the EV is connected. The feed-in power takes the negative sign most of the day, meaning that the system draws electricity from the power grid (only a few watts). However, at 9 a.m. with 358 W, 3:40 p.m. with 253 W and 4:30 p.m. with 291 W, the system injects electricity into the power grid, i.e. when the EV is charged. Fig. 22: Open in new tabDownload slide Inverter input power, output and feed-in power. Fig. 23 shows the daily current and voltage profile of the inverter for this selected day (16 July 2020). The cumulative total yield, total feed-in energy and energy consumption are 2648, 5890.11 and 216.17 kWh, respectively. 6.4.2 Subsystem 2: UHA Trackers For the inverter of the UHA Trackers subsystem, Fig. 24 shows the daily output power variation profile. It should be noted that the inverter also intervenes in the EV charging when it is connected at 9:30 a.m. The inverter then charges the batteries when the EV is charged at 11:45 a.m. and the batteries are fully charged at 3:00 p.m. The system takes energy from the power grid but with very small amounts. Even the grid feed-in power is only observed at certain times, such as at 8:55 a.m., 3:40 p.m. and 4:25 p.m., with 424, 243 and 289 W, respectively. The cumulative values of total yield, total feed-in energy and total energy consumption recorded by the hybrid inverter are 237.6, 8261.34 and 291.76 kWh, respectively. 6.4.3 Energy consumption and yield Figs 25–27 show the hourly distribution of the energy consumed and self-consumed (self-use) on 16 July 2020 for the entire system, Subsystem 1 and Subsystem 2, respectively. The energy consumed is the electricity that is used during the operation of the system. However, self-consumed energy is the energy that is used when loads are plugged in such as the EV, electric bike and lighting. The self-consumed energy from 9 a.m. to 2 p.m. is supplied by the two inverters (Fig. 25). For example, the total power at 10:00 a.m. is estimated to be 1.5 kWh, of which 0.3 kWh is supplied by the Subsystem 1 inverter and the remaining 1.2 kWh is supplied by the Subsystem 2 inverter. However, the self-consumed energy between 12:00 p.m. and 2:00 p.m. is only supplied by the Subsystem 2 inverter. The self-consumed energy is 0.5, 0.7 and 0.65 kWh for 12:00 p.m., 13:00 p.m. and 14:00 p.m., respectively. Table 7 lists the daily energy yield, which is the sum of the daily feed-in energy and self-use. The total daily energy yield for the entire system is estimated to be 3 kWh divided into 0.35 kWh of feed-in energy with 11.67% and 2.65 kWh (88.33%) of self-consumed energy. The Bike Shelter subsystem’s daily yield is 2.8 kWh, with 0.18 kWh (6.43%) and 0.62 kWh (93.57%) being the feed-in energy and energy self-use, respectively. Table 8 provides the proportion of energy consumed and self-consumed energy as well. Table 7: Daily energy yield (16 July 2020) Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Feed-in energy 0.35 11.67 0.17 85 0.18 6.43 Self-use 2.65 88.33 0.03 15 2.62 93.57 Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Feed-in energy 0.35 11.67 0.17 85 0.18 6.43 Self-use 2.65 88.33 0.03 15 2.62 93.57 Open in new tab Table 7: Daily energy yield (16 July 2020) Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Feed-in energy 0.35 11.67 0.17 85 0.18 6.43 Self-use 2.65 88.33 0.03 15 2.62 93.57 Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Feed-in energy 0.35 11.67 0.17 85 0.18 6.43 Self-use 2.65 88.33 0.03 15 2.62 93.57 Open in new tab Table 8: Daily energy consumption (16 July 2020) Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Consume energy 1.87 41.37 0.94 96.91 0.93 26.2 Self-use 2.65 58.63 0.03 3.09 2.62 73.8 Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Consume energy 1.87 41.37 0.94 96.91 0.93 26.2 Self-use 2.65 58.63 0.03 3.09 2.62 73.8 Open in new tab Table 8: Daily energy consumption (16 July 2020) Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Consume energy 1.87 41.37 0.94 96.91 0.93 26.2 Self-use 2.65 58.63 0.03 3.09 2.62 73.8 Case . Total system . . UHA Trackers . . Bike Shelter . . Value kWh % kWh % kWh % Consume energy 1.87 41.37 0.94 96.91 0.93 26.2 Self-use 2.65 58.63 0.03 3.09 2.62 73.8 Open in new tab 6.4.4 Battery operation analysis In this study, the behaviour of the storage batteries is also given for both subsystems of the MG. When the load is connected (EV), the PV energy produced is consumed and used directly on-site. If there is EE, it is used to charge the batteries if it is not in its maximum SoC. Otherwise, it is fed into the power grid. On the contrary, if the load is greater than the PV output power, the batteries are discharged to power the load. If both are not sufficient, the energy requirement is taken from the power grid. Fig. 28 shows the behaviour of the batteries and the information about Subsystem 1 on 16 July 2020. It can be seen that when the load is interconnected at 9 a.m., it is not powered by these batteries. However, the batteries are discharged to power the load with the PV system at 9:20 a.m. Then, the batteries start to be charged when there is no load connected (EV fully charged), i.e. at 3:00 p.m. (78%) until 7:27 p.m. with a SoC estimated at 91%. Fig. 29 shows the behaviour and information of the Subsystem 2 batteries. The batteries are discharged when the EV is connected between 9:30 a.m. and 11:40 a.m., reaching a discharge capacity of 69%. They are then recharged by the solar PV system to an estimated maximum load of 91% between 11:40 p.m. and 03:07 p.m. Fig. 23: Open in new tabDownload slide Hourly output voltage and current of the retrofit inverter. Fig. 24: Open in new tabDownload slide Power output and feed-in power of hybrid inverter. Fig. 25: Open in new tabDownload slide Daily energy consumption and self-use for the whole system. Fig. 26: Open in new tabDownload slide Daily energy consumption and self-use for UHA Trackers subsystem. Fig. 27: Open in new tabDownload slide Daily energy consumption and self-use for Bike Shelter subsystem. Fig. 28: Open in new tabDownload slide Daily battery information of Bike Shelter subsystem. Fig. 29: Open in new tabDownload slide Daily battery information of UHA Trackers subsystem. The MG studied is a small-scale system installed at the university campus to achieve a certain energy autonomy and increase the self-consumption. Using this type of system for large-scale applications can offer technical, economic and environmental benefits. All of this is achieved by using different renewable-energy and storage technologies as well as different DSM and dynamic pricing techniques. The objective is to achieve energy autonomy and to consume the energy produced locally (the prosumer and self-consumption concept), especially in off-grid areas and islands, as well as in grid-connected MG systems. On the other hand, the connection to the electrical grid allows the exchange of energy between the electrical grid operator and the consumer (sales and purchase of energy). 7 Conclusion This study investigated the functional analysis and performance evaluation of a hybrid MG-based PV/battery/EV grid-connected system installed on the Mulhouse campus in France. The overall system is composed of two subsystems including two PV generators, two inverters, batteries and an EV. All are connected to the power grid with self-consumption of solar energy or direct feed-in power. The objectives of the system installation were discussed, including the management of the site demand by maximizing the self-consumption of the energy produced. To this end, the behaviour of the system components, in particular inverters and storage batteries, was presented and analysed. The system was examined over two randomly selected days, with and without the EV connection. The parameters analysed were the PV output power, energy efficiency, feed-in power and self-consumed energy. The obtained results show that the energy produced by Subsystem 2 during its lifetime and the emissions emitted are respectively estimated at 5597.65 kWh and 4.17 tons. In addition, the total cumulative energy injected into the grid during the entire system operating cycle is estimated to be 3466.82 and 5836.58 kWh for Subsystems 1 and 2, respectively. Based on the results and the analysis of the performance of the studied MG it can be concluded that the MG is a means of integration and use of decentralized PV RE. The MG can contribute to increasing the self-consumption of PV energy and reducing the load of the conventional power grid. 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