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Grid-connected microgrid: design and feasibility analysis for a local community in Bangladesh

Grid-connected microgrid: design and feasibility analysis for a local community in Bangladesh Abstract Global demand for electricity is growing significantly in developing nations. Renewable energy accounts for barely 3% of total energy consumption in Bangladesh. Sources of renewable energy, e.g. solar, are increasingly being acknowledged as viable supply-side choices for microgrids. This article presents a grid-connected microgrid design based on meteorological data for a local community situated in Mohammadpur, Dhaka. This study presents a feasible design of a system that gives the lowest cost of energy production and emissions that is evaluated using software named Hybrid Optimization Multiple Energy Resources (HOMER Pro). Comparison and assessment of the net present cost, cost of energy, operating cost and environmental emission for five different feasible microgrids are analysed concerning real-time data. Also, a suitable case is sorted out and proposed for the local community for electrification. The proposed case offers a $0.0442/kWh cost of energy, which is ~32% cheaper than the current rate with a 57.5% renewable fraction and a payback period of 16.86 years. People of this local community will have access to considerably more clean energy at a lower price by this study; also this design could sell the excess energy to the grid to avoid frequent electricity outages. Open in new tabDownload slide renewable energy, communities, microgrid, community energy, photovoltaic, batteries, emission, cost of energy Introduction Electrical energy is vital for the socio-economic growth of a nation. Rapid urbanization and population growth have increased per-capita power consumption; as a result, the installed capacity must be expanded at the same rate across the world [1]. In Bangladesh, ~75% of rural people do not have access to electricity. An efficient power source is required for the economic progress of the nation as well as for the delivery of critical communal services such as health and education. Access to sustainable and regular power can help to support income-spawning activity, particularly irrigation for agriculture, which accounts for 16% of the gross domestic product (GDP) of the country. The government of Bangladesh has taken numerous footsteps to alleviate the electricity issue in rural areas by utilizing renewable energy sources, notably solar photovoltaic (PV) [2]. However, the most significant limitations of this electrification are: affordability for the lower demographic; limitations in using the energy for productive purposes because the systems frequently suffer from excess capacity as a result of being over-sized to ensure high reliability; and the systems are not flexible in terms of usage and payment methods. Furthermore, a field-based study revealed many flaws, most of which occurred during the application stage, such as improper installation, poor-quality components and the absence of quality control systems [3]. Bangladesh is a South Asian country between latitudes 20.30° to 26.38° N and longitudes 88.04° to 92.44° E. Bangladesh receives sufficient sun irradiation for the production of solar power. The majority of electricity is generated utilizing fossil fuels and natural gas. However, the Bangladeshi government is actively working to increase the quantity of renewable-energy-based generation. The annual solar irradiation, which is ~1700 kWh/m2, is sufficient to provide the needed electricity from solar PV in Bangladesh. As a result, Bangladesh may shift its generating system to one based on renewable energy, reducing its reliance on conventional power generation. In remote regions where traditional grid access is unavailable, a microgrid (MG) system or a renewable-energy-based hybrid system can be used to electrify the area [4]. Microgrids are often made up of low-voltage distribution systems with distributed energy resources as well as storage devices and flexible loads. These systems can be operated in both grid-connected (on-grid) and off-grid (island) modes [5]. The grid-connected approach is merely viable when the typical grid is accessible and the concentrated areas’ unique use requires power. If the microgrid-generated power is more than the demand, the additional power is supplied to the conventional grid; if the microgrid-generated power is insufficient for the area beneath it, the microgrid acts as a load on the conventional system. When supply from the grid is insufficient or the connection from the grid is unavailable in the area, the island mode is considered. Microgrids can be employed in the national grid, i.e. grid-connected microgrids. Off-grid microgrids primarily provide access to power for those who reside in places where a grid expansion is not feasible in terms of time and expense. As a result, the impact of off-grid microgrids is assessed not only by the decrease in power costs in rural and isolated locations but also by the amount to which quality of life improves [6–8]. Modelling and optimization of an urban community microgrid, i.e. housing estates, residential complexes and educational institutions, were examined by Raji and Luta [9]. Distributed energy resources utilized in the research include photovoltaic cells, diesel generators (DGs) and electrical loads classified as critical and noncritical. Sardi et al. [10] proposed a context for establishing virtual microgrids (VMs) based on community energy storage (CES) in home networks using PV modules and explained the financial viability and working feasibility of VMs. The proposed model produced revenue of nearly 35% of the overall financial benefit of CES and successfully helped customers to reduce their energy costs [11]. From a techno-economic standpoint, Johannsen et al. [12] explored why tiny wind turbines (WTs) have been mostly excluded from Kenyan mini-grids. The techno-economic model was used to backtest the viability of a hybrid PV/wind system, and a conceptual framework was built and deployed to analyse the condition of the Kenyan mini-grid industry and categorize discovered constraints. Consequently, the examination of current research and institutional assessment changes affecting community microgrid expansion were discussed [13]. The authors proposed a 20-MVA microgrid for the recently approved All India Institute of Medical Sciences healthcare centre in Madurai with 750 beds. It was ecologically friendly by preventing annual emissions of 27 362 kg of sulphur dioxide, 12 838 kg of nitrogen oxides and 6261.1 tonnes of carbon dioxide as contrasted to power provided directly from the utility grid [14]. In addition, a systematic literature review (SLR) of the study was conducted on designs and energy-management methods for microgrids in concerning aspects. The results of this SLR showed a wide range of techniques and diverse backgrounds, and SLR recommended that future studies include the uncertainty element of energy management rather than relying only on historical data [15]. Corsetti et al. [16] presented an energy-management model for a multi-energy community scheme in which diverse technologies meet power and thermal consumption requirements and system flexibility, i.e. extra generation and extra absorption, which was sold as a service to maintain the power grid and expand revenue. They concluded that these systems might retain large margins in multi-energy community systems to assure their reliability. The optimum energy management of community microgrids (CMGs) was defined [17], considering distributed energy supplies as well as thermal and electrical needs in CMGs. As a result of the widespread adoption of distributed energy resources, end users in the urban community microgrid system have evolved from conventional clients to prosumers capable of both energy generation and consumption. Wang et al. [18] proposed a new distributed peer-to-peer (P2P) energy transaction mechanism based on the double auction marketplace. Also, the theoretical framework for community microgrid development based on new institutional economics principles was proposed. Researchers [19] explored the hurdles to and motives for PV adoption in metropolitan areas of Nigeria. According to the findings [20], the primary impediments were high capital expenses and a lack of investment. MG was linked to the grid and provided electricity to a domestic neighbourhood, the trade-off between two competing goals was examined and the best solution was found [20]. Sanjel and Baral [21] used analytical modelling to undertake energy planning, analysing electrified choices based on life-cycle cost and economic distance limit; each energy system under different load situations was compared to find the best solution. Kakran and Chanana [22] carried out research on microgrid components for electric energy and believed that technical microgrid solutions had been thoroughly established; however, a lack of monetary and commercial considerations was preventing their implementation. An efficient method was proposed [23] for discovering such communities of microgrids while considering both their geo-locations and net energy throughout for any period. The practicality of a PV pumped hydropower-producing scheme employed in rural residential houses with wells was demonstrated [24]. To identify the best techno-economic performance of this design in several prefecture-level cities in Shaanxi Province, the evolutionary algorithm was used to enhance the size of the water tank, the number of solar arrays and the height of the water head. Nevertheless, Xie et al. [25] studied the perspective of community microgrids, in which fellows of a community may trade energy and services deprived of passing via the traditional channels of the public electrical grid. Day-ahead arrangement of battery energy while accounting for the costs of deterioration caused by charging–discharging cycles and replacement costs concerning the depth of charge were simulated and applied to the impartial function to estimate the real operating expenses of the system [26]. The authors created a model of a wind-turbine system and a solar cell to assess their potential to supply electrical energy. To confirm the values acquired from optimization, the colonial-competition method was tested in terms of the convergence process and estimating the overall investment cost using a genetic algorithm and particle swarm optimization (PSO) [27]. Techno-economic evaluation of a hybrid renewable-energy system (HRES) with PV, battery energy-storage system (BESS) and WT linked with the utility grid for an electrical load demand of a small town was proposed [28]. The cost of energy (COE) was reduced by the net-metering feature, which made the total system feasible and sustainable. At BMS College of Engineering in Bengaluru, India, researchers [29] sought to optimize the design of a community-level microgrid for the available load. Battery storage, WTs, diesel generator and solar PV were all part of the proposed microgrid. Hafez and Bhattacharya [30] created an incentive-based demand-side management strategy, as well as evaluated its possible implications on stand-alone microgrid (SAMG) planning. Each system in the literature review was designed with different load types, consumers, resources and locations. Based on their intended purpose, each system cannot be compared properly with another one. Results (system sizing, COE) from each system are suitable considering the system structure. However, Table 1 shows the key findings of the systems listed to provide a generalized idea. Table 1: Short comparison of the literature reviews System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 Open in new tab Table 1: Short comparison of the literature reviews System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 Open in new tab In this article, a grid-connected microgrid is designed to analyse cases obtained from HOMER [33] and a suitable case is proposed for an urban area in Mohammadpur, Dhaka-1207, Bangladesh. The objective of the research work is to provide significantly more clean energy at a cheaper cost to the people of the community. Real-time data have been taken from the survey following the simulation performed in HOMER. Also, surplus energy is sold to reduce the power/electricity deficit of the grid. Off-grid and on-grid modes are also assessed, considering feasible renewable resources for the selected community. Furthermore, renewable energy resources scopes will be explored in this research for the densely populated urban area. The local community of Mohammadpur is taken into consideration as it is one of the rising areas where people ponder to live. Real-time data obtained from the community people are further used for the simulation in HOMER Pro. Several microgrid approaches are analysed and, among them, the best combination is proposed for the community considering the COE and greenhouse gas (GHG) emissions. However, no previous study on microgrid design for the urban community was evident for the concerned area, i.e. Mohammadpur, Dhaka-1207. As a result, the designed grid-connected microgrid is a case study considering location, natural resources and load profiles. The organization of the paper is as follows. Section 1 explores the mathematical formulation used for modelling, and Section 2 contains simulation findings, including a full sensitivity analysis. Finally, conclusions are given in Section 3. 1 Methodology HRES is a system that combines several (two or more) electricity-generating alternatives. Renewable energy sources, fossil-fuel-based energy sources or an arrangement of the two is possible. Hybrid systems are typically used to supplement a single renewable-energy-based producing choice such as sole wind or solar, as these are intermittent sources, as well as to improve resource usage and minimize net cost [34]. Fig. 1 illustrates the block schematic of the planned HRES for the community. Fig. 1: Open in new tabDownload slide Block diagram of the system. 1.1 HOMER HOMER is the global standard in microgrid design and optimization software, based on decades of listening to the demands of customers all over the world and expertise in developing and installing microgrids and distributed power systems that can contain a mix of renewable energy sources, storage and fossil-based generation [33]. HOMER is a simulation program created by the National Renewable Energy Laboratory (NREL) in the USA to aid in the arrangement and construction of renewable-energy-based microgrids. HOMER is used to simulate the physical performance of an energy supply system as well as its lifespan cost, which is the total of capital and operational expenditures during its life. HOMER may also be used to analyse choices such as distributed generation units and stand-alone, off-grid and grid-connected supply systems for distant places, as well as other design possibilities. HOMER is intended to address the issues associated with microgrid analysis and design, such as the huge number of design possibilities and the doubt about important factors such as load increase and future fuel costs. The three main functions achieved by HOMER are simulation, optimization and sensitivity analysis [35]. Net present cost (NPC) and COE are the two main economic elements that are determined by the total annualized cost of the system. As a result, HOMER must compute the annualized costs of the system, which are the annualized costs of the components minus any miscellaneous costs [36]. NPC in HOMER sorts the lifespan cost of the system and includes all system costs, such as replacement cost, capital cost, operation and maintenance cost, fuel consumption cost and other expenses, such as credits caused by pollution emissions and grid cost. The difference between the nominal interest rate and the inflation rate equals the real interest rate, which must be taken into consideration. Furthermore, expenses into the system in terms of perpetual dollars are considered. To simulate using HOMER, various factors such as component pricing, load demand, renewable resource data such as solar irradiation, component specs and so on must be entered into the software as input parameters [37]. From utility-scale and distributed generation to stand-alone microgrids, HOMER maximizes the value of your hybrid power system precisely, which makes HOMER better than PV*SOL and PVsyst [38]. Equations 1 and 2 are used to calculate the total NPC and COE: CNPC=CaCRF(i,Np)(1) Cost of Energy=CatEp+Egs+Ed (2) where CNPC represents the NPC, Ca represents the total cost (annualized), CRF is the capital recovery factor is constant which is dependent on i (annual real interest rate in percent) and Np (project lifetime). However, CRF is constant here as it is dependent on i and Np. For each project, i and Np are constant as different projects have different project lifetime and annual real interest rates. Cat represents the annual cost, Ep represents the primary load, Egs represents the sold energy to grid (yearly) and Ed represents the deferrable load. 1.2 Site location Mohammadpur is a sub-district in Dhaka District, Dhaka Division, Bangladesh, and is located 4 km south of Dhaka North City Corporation [31]. Mohammadpur was primarily intended as a moderate-density residential neighbourhood for middle-income residents. This region has been transformed into a small metropolis of apartment complexes because of massive urbanization and has a constantly growing population made up primarily of native Bangladeshi, with vast and expanding informal settlements. The coordinates are 90.36 E, 23.76 N in a residential community and the electrical demand is met by a three-phase 440-V distribution line from the power grid. Fig. 2 illustrates the site location. Fig. 2: Open in new tabDownload slide Site location. 1.3 Load profile The most significant aspect of simulation and optimization is the load profile of each location. Some sites, such as education centres, hotels, hospitals and industrial towns, contain real-world load-consumption statistics that may be used in the simulation. As time-series data, these real-world data are loaded into HOMER. However, in some regions, particularly distant and rural locations, where real load-consumption statistics are unavailable, the load profile should be projected with consideration for the specifications of the area. These data sets are inputted into HOMER as a daily profile and HOMER uses them in power balance constraint calculations. The community comprises 22 buildings consisting of 264 apartments that are primarily utilized for residential purposes. As a result, the peak demand of the site changes substantially between winter and summer, as well as weekdays and weekends. Despite its small size, the peak load is 366.53 kW with a consumption of 1739.74 kWh/day because of the dwellings feature of the significant loads. Table 2 shows the considered annual load profile of the community. Fig. 3 shows the hourly load profile for the community; underlying data can be found in the online Supplementary Data. Table 2: Monthly consumption Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Open in new tab Table 2: Monthly consumption Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Open in new tab Fig. 3: Open in new tabDownload slide Hourly load profile of the community. 1.4 Resources Renewable resources data, such as solar irradiation statistics with a clearness index for specific areas, are necessary for the simulation in HOMER. The sun irradiation for the suggested site was obtained from the surface meteorological and solar energy database of NASA [39]. Fig. 4 depicts the total amount of yearly sun irradiation for the suggested location with a clearness index. For the site under consideration, an annual average irradiation of 4.86 kWh/m2/day is utilized. The condition of the sky technique can anticipate when the sun will shine for solar resources. Equations (3) and (4) are used to calculate the association between the relative sunlight duration and the condition of the sky: Fig. 4: Open in new tabDownload slide Variation in solar irradiation and the clearness index of the site. NbN=a1+b2+c3n123(3) N=[arcos(−sinφsinΔ+cos85cosΔcosφ)]7.5 (4) where Nb represents the hours of sunshine (bright), N represents the month over the representative day for which the Campbell–Stokes sunlight recorder remains sensitive, φ represents the latitude, δ represents the declination, n1 represents the number of days that remain clear in a month, n2 represents the number of mixed days in a month, n3 represents the number of overcast days in a month and n123 = n1+ n2+ n3η, which represents the total number of days in the month that is under consideration, where a, b and c all are meteorological components. The Angstrom equation can be used from sunshine to guess the global radiation as shown in Equation (5): XX0=y+znN (5) where, X/X0 represents the ratio of monthly averaged daily globally to monthly averaged daily solar radiation on a horizon surface. HOMER uses a three-step procedure to determine the WT’s power output at each time step. First, HOMER computes the wind speed at the turbine’s hub height. The turbine’s power output is then calculated at that wind speed and standard air density. Finally, HOMER modifies the power output value to account for the real air density. HOMER calculates the wind speed at the hub height of the WT at each time step using the inputs that are entered on the Wind Resource page and the Wind Shear entry [33]. If a logarithmic law is used, HOMER estimates the hub height wind speed using Equation (6): Uh=Ua(lnZhZ1) (lnZaZ1)−1(6) where Uh represents the wind speed at the WT’s hub height (m/s), Ua represents the wind speed at anemometer height (m/s), zh represents the wind-turbine hub height (m), za represents the height of the anemometer (m), z1 represents the length of surface roughness (m) and ln(..)represents the natural logarithm. If the power law is used, HOMER estimates the hub height wind speed using Equation (7): Uh=Ua(Za)−α(Zh)α(7) where α represents the law exponent of the power. Fig. 5 presents the average wind speed of the location. 1.5 Photovoltaic cell A PV unit is made up of numerous solar cells linked in parallel and series to meet the current and voltage needs of the system. The PV unit output voltage is determined by the series connection of the cells, whereas the PV unit output current is determined by the parallel connection of the cells. Where z is the thermal voltage, Rsh and Rs are the shunts and series resistances, and Id, Ip and I are the currents in the diode when it is directly polarized, the current generated by the solar cell and the current at the terminal of the solar cell, respectively. Fig. 5: Open in new tabDownload slide Average wind speed. The electrical equal circuit of a solar cell is depicted in Fig. 6 as a conventional single-diode model [28]. The connection between the parameters and variables of a solar cell is represented by Equation (8): Fig. 6: Open in new tabDownload slide Single-diode model. I=Ip− Id [e(V+IRsz)−1]−V+IRsRsh(8) 1.6 WT The ratio of the turbine speed, which is identified as a proportion of the speed of the turbine rotor to the wind speed, has a significant impact on the power of mechanical output for the wind speed. The ideal proportion of the speed of the turbine achieves the highest turbine energy-conversion efficiency at a certain wind speed. When wind speed changes, the speed of the rotor minimizes the ideal proportion of the speed and harvests the most energy from existing wind sources. The non-linear expression yields the expression of aerodynamic power (Paero) generated from a WT shown in Equation (9) [33]: Paero=0.5ρπr2v3CP(λ,β)(9) Here, r represents the radius of the rotor, the wind speed is v and a generic equation has been used to model Cp(λ, β) is calculated using Equation (10), where Cp (power coefficient) is the function of both TSR (λ) and the blade angle of inclination (β): CP(λ,β)= sin [π(λ−3)15.0.3β](0.44−0.0167β)−β(λ−3)0.00184(10) Here, the wind-turbine angle of inclination of the blades is β and λ denotes the ratio of the turbine speed, which is calculated using Equation (11): λ=ωtrv−1 (11) Again, the rotation speed of the blades is ωt. λ o is calculated using Equation (12): λo=cos−1[(0.3β−15)0.00184β(0.44−0.167β)π] [(0.3β−15)π] (12) As a result, the maximum power of the wind Pwind may be calculated using Equation (13): Pwind=0.5ρπr2v3CP(λo,β) (13) 1.7 Inverter The electricity generated by the PV panel is direct current (DC); however, because the load requires alternating current (AC), the generated DC power must be converted to AC. The inverter is used to change DC electricity into AC power at a consistent frequency using integrated maximum power point tracking. The quantity of power that the inverter can change is determined by the inverter rating. Pi is calculated using Equation (14): Pi=PpEi(14) where Pp represents the power of the output of the PV system and Ei represents the inverter efficiency found using Equation (15): Pp=PsNS(15) Ps represents the power generated by a single PV cell and Ns represents the number of PV panels. The size of an inverter in an integrated grid system is determined by two factors: grid selling capacity and local load demand. Pm is calculated using Equation (16): Pm=PL+Pg(16) where PL represents the maximum load demand and Pg represents the maximum amount of power delivered to the grid [36]. 1.8 Energy-storage system Equations (17) and (18) are the representations of battery energy levels for charging/discharging cycles. For charging the battery: B(t)=B(t−1)+tPcpEc(17) For discharging the battery: B(t)=B(t−1)+tPdpEd(18) where Pcp represents the power of charging at time t for the battery, Pdp represents the power that is discharging at time t, B(t) represents the energy level, t represents the time interval, and Ec and Ed represent the charging and discharging efficiency [37]. 1.9 Utility grid The system may be operated in two modes: grid-connected mode and off-grid mode. If the electricity generated by the hybrid energy system exceeds the load needs in the grid-integrated mode, the extra power is transferred to the utility grid. Es represents the electrical energy-storage system, Lr represents the demand of total load, the running hour is t, the sun hour is ts and Ep represents the output power from PV at a particular period. Eie represents the energy that is exported or imported from the grid; if Eie is positive (+ve), surplus energy is sold to the grid; if Eie is negative (–ve), energy is purchased from the grid [33]. Eie is computed using Equation (19): Eie=∑NtS=n0Ep(tS)+Es−Lrt(19) 1.10Component configuration and prices Prices of the components were selected carefully to reduce the cost for this article. Table 3 shows the configuration of the component and prices. Table 3: Specification of the components [32,40,41]. PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years The rate definition for the system is a 0.0750 $/kWh price followed by a sell-back price of 0.0690 $/kWh [42]. Open in new tab Table 3: Specification of the components [32,40,41]. PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years The rate definition for the system is a 0.0750 $/kWh price followed by a sell-back price of 0.0690 $/kWh [42]. Open in new tab 2 Results and discussion HOMER Pro was used to simulate the designed microgrid to assess its operational and economic features. Because of its simpler, non-derivative optimization, HOMER Pro can run ample simulations in a few seconds. In this context, 56 potential cases were evaluated while considering various system designs to compute the option with the lowest NPC at the start of the project. Thirty-six of the entire number of simulated solutions were determined to be feasible; viable is defined as a solution capable of meeting the objectives, implying that 20 possibilities were discarded owing to limitations (due to capacity shortage). HOMER Pro removes all infeasible alternatives (because of a shortage of power sources, inverters or other factors) and ranks viable choices by total NPC. An hourly time-series simulation for every conceivable system design has been examined over a 25-year plan horizon. Four alternative schemes were considered to find the most advantageous approach for microgrid planning, as shown in Table 4. Table 4: Cases considered No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) Open in new tab Table 4: Cases considered No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) Open in new tab Case-1 with an NPC of $868 818 was the most practical, optimal and cost-effective combination for the simulation location. COE was determined at $0.0442/kWh. The proportion of renewable energy in the generated energy was 57.5%. PV array of a total of 450 kW was selected for this case. On the other hand, Case-2 with higher NPC and slightly higher COE was found after simulation. NPC for this is $1.02 million, the COE is $0.0517/kWh and the proportion of renewable energy is 57.4%. A storage system with a total capacity of 211 kWh comprising lead-acid battery packs was chosen for this case. NPC for Case-3 ($917 608) is somewhat higher than that for Case-2 and COE for Case-3 (~$0.0771/kWh) is considerably higher than those of Case-1 and Case-2. As there are no PV and WTs, renewable energy was not generated then. After integrating WTs with Case-2, i.e. Case-4, NPC is $1.17 million and COE is $0.0585/kWh. The WT was installed in 15 buildings and the remaining 7 buildings were not suitable for installing WTs according to the orientation. Thus, renewable-energy generation from Case-4 was 59.8%. Nevertheless, after separating the grid from previous combinations, an off-grid microgrid was formed. Case-5 was designed with PV, inverter and lead-acid batteries; 530 kW of PV and 16 batteries were installed for each building. NPC for Case-4 is $4.39 million and COE is $0.462/kWh. Again, for Case-6, WT, PV, inverter and lead-acid batteries were installed. NPC of $4.52 million and COE of $0.609/kWh are found. For off-grid cases, NPC and COE are much higher than the on-grid cases. Components are sized to be larger than in on-grid systems and the amount of surplus electricity sold is also less. Also, unmet load and capacity shortage are larger compared to on-grid cases after PV and battery are sized at maximum capacity. Thus, off-grid systems are not feasible for the community. Table 5 illustrates the sizing parameters and Table 6 illustrates the obtained financial result in detail in contrast to the simulation. Table 5: Sizing parameters Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Open in new tab Table 5: Sizing parameters Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Open in new tab Table 6: Financial result Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Open in new tab Table 6: Financial result Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Open in new tab HOMER also calculates the quantity of various GHGs that the suggested design will release. The grid emission factor that is keyed in HOMER is 0.67 tCO2/MWh [43]. According to the results, the system will emit carbon dioxide, sulphur dioxide and nitrogen oxide. Case-1 emits 281.47 tCO2/year and Case-3 peaks the GHG emission at 400.88 tCO2/year. Case-4 emits the least GHG in off-grid conditions because of having both WTs and PV. Case-5 and Case-6 do not emit GHG as the grid is not connected. The proposed case offers a 29.8% reduction in GHG emissions. Fig. 7 shows the emission of GHGs for every case. Fig. 7: Open in new tabDownload slide GHG emissions for the considered cases. HOMER also estimates NPC of the proposed design; Case-1 has NPC of $868 818 according to simulation results. Comparatively, the NPC of Case-1 is lower than that of the other cases considered. Photovoltaic generation of 450 kW is connected to the grid, but the other cases have a lead-acid battery (without the base case), the addition of the components affecting NPC. Off-grid cases have a significantly higher NPC as PV and battery were sized at the highest capacity. Fig. 8 illustrates the NPC of the cases. From the obtained result of the simulation, the proposed case possesses the most suitable COE for the area considered. COE is obtained at $0.0442/kWh, whereas for Case-2, COE is $0.0517/kWh because of the addition of the lead-acid battery. In the base case, electricity is directly purchased from the grid but in the proposed case, electricity is generated from both PV and the grid. COE of Case-5 and Case-6 is the highest among the cases, which is $0.465/kWh. COE concerning the cases is shown in Fig. 9. The annualized value of all expenditures and revenues, excluding initial capital costs, is the operating cost. The operating cost is only $22 884 in the proposed case. The proposed case has a lower operating cost, whereas the rest of the cases have a comparatively higher operating cost. Fig. 10 illustrates the graph of operating costs for considered cases; 4741 kWh/year of capacity shortage along with a slight unmet load of 0.375% was observed in the proposed case. Fig. 8: Open in new tabDownload slide NPC of the cases. Fig. 9: Open in new tabDownload slide COE of the cases. Fig. 10: Open in new tabDownload slide Operating cost of the cases. Case-5 has the highest unmet load and lowest capacity shortage, i.e. 3.35% and 17 259 kWh/year. Fig. 11 illustrates unmet load and capacity shortage together against cases. While designing system configurations, sensitivity analysis takes into account the unpredictability of input parameters. The average daily load, inflation rate and grid failure (per year) are taken into account in this research. With the increase in the load-scale average, NPC and COE are increasing linearly. As the load is increasing, the system tends to purchase more electricity than normal, which results in increasing NPC and COE. Fig. 11: Open in new tabDownload slide Capacity shortage and unmet load of different cases. Since the chosen community is in a well-established area, the possibilities of major renovation are very low. So, the proposed system will be adequate for the community. Concern with the increasing demand in the future can be dealt with, with room for capacity upgrades. If not, then the renewable fraction will eventually decrease. Fig. 12 shows the change in NPC and COE with the change in average daily load. Fig. 12: Open in new tabDownload slide Sensitivity output for loaded scaled average. If the inflation rate increases and exceeds the nominal discount, the rate of real interest is negative. With the increase in the inflation rate, NPC will increase and the capital recovery factor will decrease, resulting in a total annual cost decrease. Moreover, COE is the annualized cost-inverse multiplication of the total energy served, so as annualized cost or total energy served increases, COE increases. Dependency on inflation and change in financial parameters is shown in Fig. 13. An inflation clause should be considered while financing such a project. As the grid-failure frequency increases, the system is inadequate to supply demanded electricity, which increases the unmet load and capacity shortage. Fig. 13: Open in new tabDownload slide Sensitivity output for the inflation rate. For our designed case, the unmet load and capacity shortage are increasing slightly. Fig. 14 shows the change in unmet load and capacity shortage concerning grid-failure frequency. As the overall generating capacity of the country is progressively increasing, the load-shedding duration and frequency are reducing gradually. Such issues will not be a concern for the proposed system. Fig. 14: Open in new tabDownload slide Sensitivity output for grid failure per year. Bangladesh has achieved substantial socio-economic development in recent years, including improved life expectancy, per-capita income, poverty reduction and literacy rate, among other things. In the previous 10 years, the average GDP growth rate has grown by >6.7%, up from 3.7% in the 1970s. Bangladesh currently hopes to achieve sustainable economic growth through creating more employment; enhancing the quality of building energy, health and education, and transportation infrastructure; implementing energy conservation and efficiency measures; and guaranteeing good governance [44]. Despite being a developing county, the mass population in the community has less knowledge regarding modern technologies and always tends to save money for their future generations. So, the proposed Case-1, i.e. PV–inverter–grid, is a feasible case despite having an unmet load and capacity shortage. Case-4, i.e. PV–inverter–battery–grid, is more likely to be ignored by the community people because of high NPC though it has less unmet load and capacity shortage than Case-1. Concerning the payback period, only Case-1 has a payback period of 16.86 years after the system’s initial set-up. In other cases, no payback is observed, i.e. the capital is not recovered for this instance. 3 Conclusion In this article, a microgrid approach for a community in Mohammadpur is presented along with the feasibility. This approach is an effective way to mitigate frequent load-shedding problems and usage of sustainable energy broadly for a community is promoted. The combination of the proposed case is of PV–inverter–grid; the case is picked from cases that were obtained from HOMER Pro. Also, the massive use of fossil fuel is eliminated as a sustainable source is used to convert electricity directly. Electricity is supplied at a rate of $0.0442/kWh, which is ~32% cheaper than the current electricity rate in Bangladesh. The fraction of renewable energy is 57.5% and a payback period of 16.86 years was also observed. Furthermore, the suggested system emits fewer GHGs than a traditional fossil-fuel-based power station. The proposed design in this research is capable of meeting the demand of the investigated site, which has a connected load of 1739.74 kWh/day and a peak load of 366.53 kW. This study provides residents of this town with significantly more clean energy at a reduced cost, as well as the ability to sell extra energy to the grid to avoid frequent power outages. The stiffness of the suggested design is obtained from the obtained result and feasibility is assessed according to the mindset of the people in the community. After integrating the microgrid, stability analysis could be done for enhancing the design in the future. LiFePO4 batteries would be used instead of lead-acid batteries; also, dual-axis WTs could be used to improve the designed microgrid for the community. A P2P energy distribution structure could also be used with the designed microgrid. Acknowledgements The authors would like to express their appreciation to the ‘Energy and Technology Research Division’ of the ‘Advanced Bioinformatics, Computational Biology, and Data Science Laboratory, Bangladesh (ABCD Laboratory, Bangladesh)’, Chittagong (Chattogram), Bangladesh for assistance and encouragement. This study has required a considerable amount of assistance and direction, and the authors are very grateful to ABCD Laboratory, Bangladesh for providing them with limitless guidance during their research effort. Conflict of interest statement None declared. References [1] Kaur M , Dhundhara S, Verma YP, et al. Techno-economic analysis of photovoltaic-biomass-based microgrid system for reliable rural electrification . International Transactions on Electrical Energy Systems , 2020 , 30 : e12347 . Google Scholar Crossref Search ADS WorldCat [2] Taheruzzaman M , Janik P. Electric energy access in Bangladesh . 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Energy Strategy Reviews , 2020 , 32 : 100566 . 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

Grid-connected microgrid: design and feasibility analysis for a local community in Bangladesh

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Oxford University Press
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Copyright © 2022 National Institute of Clean-and-Low-Carbon Energy
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2515-4230
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2515-396X
DOI
10.1093/ce/zkac022
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Abstract

Abstract Global demand for electricity is growing significantly in developing nations. Renewable energy accounts for barely 3% of total energy consumption in Bangladesh. Sources of renewable energy, e.g. solar, are increasingly being acknowledged as viable supply-side choices for microgrids. This article presents a grid-connected microgrid design based on meteorological data for a local community situated in Mohammadpur, Dhaka. This study presents a feasible design of a system that gives the lowest cost of energy production and emissions that is evaluated using software named Hybrid Optimization Multiple Energy Resources (HOMER Pro). Comparison and assessment of the net present cost, cost of energy, operating cost and environmental emission for five different feasible microgrids are analysed concerning real-time data. Also, a suitable case is sorted out and proposed for the local community for electrification. The proposed case offers a $0.0442/kWh cost of energy, which is ~32% cheaper than the current rate with a 57.5% renewable fraction and a payback period of 16.86 years. People of this local community will have access to considerably more clean energy at a lower price by this study; also this design could sell the excess energy to the grid to avoid frequent electricity outages. Open in new tabDownload slide renewable energy, communities, microgrid, community energy, photovoltaic, batteries, emission, cost of energy Introduction Electrical energy is vital for the socio-economic growth of a nation. Rapid urbanization and population growth have increased per-capita power consumption; as a result, the installed capacity must be expanded at the same rate across the world [1]. In Bangladesh, ~75% of rural people do not have access to electricity. An efficient power source is required for the economic progress of the nation as well as for the delivery of critical communal services such as health and education. Access to sustainable and regular power can help to support income-spawning activity, particularly irrigation for agriculture, which accounts for 16% of the gross domestic product (GDP) of the country. The government of Bangladesh has taken numerous footsteps to alleviate the electricity issue in rural areas by utilizing renewable energy sources, notably solar photovoltaic (PV) [2]. However, the most significant limitations of this electrification are: affordability for the lower demographic; limitations in using the energy for productive purposes because the systems frequently suffer from excess capacity as a result of being over-sized to ensure high reliability; and the systems are not flexible in terms of usage and payment methods. Furthermore, a field-based study revealed many flaws, most of which occurred during the application stage, such as improper installation, poor-quality components and the absence of quality control systems [3]. Bangladesh is a South Asian country between latitudes 20.30° to 26.38° N and longitudes 88.04° to 92.44° E. Bangladesh receives sufficient sun irradiation for the production of solar power. The majority of electricity is generated utilizing fossil fuels and natural gas. However, the Bangladeshi government is actively working to increase the quantity of renewable-energy-based generation. The annual solar irradiation, which is ~1700 kWh/m2, is sufficient to provide the needed electricity from solar PV in Bangladesh. As a result, Bangladesh may shift its generating system to one based on renewable energy, reducing its reliance on conventional power generation. In remote regions where traditional grid access is unavailable, a microgrid (MG) system or a renewable-energy-based hybrid system can be used to electrify the area [4]. Microgrids are often made up of low-voltage distribution systems with distributed energy resources as well as storage devices and flexible loads. These systems can be operated in both grid-connected (on-grid) and off-grid (island) modes [5]. The grid-connected approach is merely viable when the typical grid is accessible and the concentrated areas’ unique use requires power. If the microgrid-generated power is more than the demand, the additional power is supplied to the conventional grid; if the microgrid-generated power is insufficient for the area beneath it, the microgrid acts as a load on the conventional system. When supply from the grid is insufficient or the connection from the grid is unavailable in the area, the island mode is considered. Microgrids can be employed in the national grid, i.e. grid-connected microgrids. Off-grid microgrids primarily provide access to power for those who reside in places where a grid expansion is not feasible in terms of time and expense. As a result, the impact of off-grid microgrids is assessed not only by the decrease in power costs in rural and isolated locations but also by the amount to which quality of life improves [6–8]. Modelling and optimization of an urban community microgrid, i.e. housing estates, residential complexes and educational institutions, were examined by Raji and Luta [9]. Distributed energy resources utilized in the research include photovoltaic cells, diesel generators (DGs) and electrical loads classified as critical and noncritical. Sardi et al. [10] proposed a context for establishing virtual microgrids (VMs) based on community energy storage (CES) in home networks using PV modules and explained the financial viability and working feasibility of VMs. The proposed model produced revenue of nearly 35% of the overall financial benefit of CES and successfully helped customers to reduce their energy costs [11]. From a techno-economic standpoint, Johannsen et al. [12] explored why tiny wind turbines (WTs) have been mostly excluded from Kenyan mini-grids. The techno-economic model was used to backtest the viability of a hybrid PV/wind system, and a conceptual framework was built and deployed to analyse the condition of the Kenyan mini-grid industry and categorize discovered constraints. Consequently, the examination of current research and institutional assessment changes affecting community microgrid expansion were discussed [13]. The authors proposed a 20-MVA microgrid for the recently approved All India Institute of Medical Sciences healthcare centre in Madurai with 750 beds. It was ecologically friendly by preventing annual emissions of 27 362 kg of sulphur dioxide, 12 838 kg of nitrogen oxides and 6261.1 tonnes of carbon dioxide as contrasted to power provided directly from the utility grid [14]. In addition, a systematic literature review (SLR) of the study was conducted on designs and energy-management methods for microgrids in concerning aspects. The results of this SLR showed a wide range of techniques and diverse backgrounds, and SLR recommended that future studies include the uncertainty element of energy management rather than relying only on historical data [15]. Corsetti et al. [16] presented an energy-management model for a multi-energy community scheme in which diverse technologies meet power and thermal consumption requirements and system flexibility, i.e. extra generation and extra absorption, which was sold as a service to maintain the power grid and expand revenue. They concluded that these systems might retain large margins in multi-energy community systems to assure their reliability. The optimum energy management of community microgrids (CMGs) was defined [17], considering distributed energy supplies as well as thermal and electrical needs in CMGs. As a result of the widespread adoption of distributed energy resources, end users in the urban community microgrid system have evolved from conventional clients to prosumers capable of both energy generation and consumption. Wang et al. [18] proposed a new distributed peer-to-peer (P2P) energy transaction mechanism based on the double auction marketplace. Also, the theoretical framework for community microgrid development based on new institutional economics principles was proposed. Researchers [19] explored the hurdles to and motives for PV adoption in metropolitan areas of Nigeria. According to the findings [20], the primary impediments were high capital expenses and a lack of investment. MG was linked to the grid and provided electricity to a domestic neighbourhood, the trade-off between two competing goals was examined and the best solution was found [20]. Sanjel and Baral [21] used analytical modelling to undertake energy planning, analysing electrified choices based on life-cycle cost and economic distance limit; each energy system under different load situations was compared to find the best solution. Kakran and Chanana [22] carried out research on microgrid components for electric energy and believed that technical microgrid solutions had been thoroughly established; however, a lack of monetary and commercial considerations was preventing their implementation. An efficient method was proposed [23] for discovering such communities of microgrids while considering both their geo-locations and net energy throughout for any period. The practicality of a PV pumped hydropower-producing scheme employed in rural residential houses with wells was demonstrated [24]. To identify the best techno-economic performance of this design in several prefecture-level cities in Shaanxi Province, the evolutionary algorithm was used to enhance the size of the water tank, the number of solar arrays and the height of the water head. Nevertheless, Xie et al. [25] studied the perspective of community microgrids, in which fellows of a community may trade energy and services deprived of passing via the traditional channels of the public electrical grid. Day-ahead arrangement of battery energy while accounting for the costs of deterioration caused by charging–discharging cycles and replacement costs concerning the depth of charge were simulated and applied to the impartial function to estimate the real operating expenses of the system [26]. The authors created a model of a wind-turbine system and a solar cell to assess their potential to supply electrical energy. To confirm the values acquired from optimization, the colonial-competition method was tested in terms of the convergence process and estimating the overall investment cost using a genetic algorithm and particle swarm optimization (PSO) [27]. Techno-economic evaluation of a hybrid renewable-energy system (HRES) with PV, battery energy-storage system (BESS) and WT linked with the utility grid for an electrical load demand of a small town was proposed [28]. The cost of energy (COE) was reduced by the net-metering feature, which made the total system feasible and sustainable. At BMS College of Engineering in Bengaluru, India, researchers [29] sought to optimize the design of a community-level microgrid for the available load. Battery storage, WTs, diesel generator and solar PV were all part of the proposed microgrid. Hafez and Bhattacharya [30] created an incentive-based demand-side management strategy, as well as evaluated its possible implications on stand-alone microgrid (SAMG) planning. Each system in the literature review was designed with different load types, consumers, resources and locations. Based on their intended purpose, each system cannot be compared properly with another one. Results (system sizing, COE) from each system are suitable considering the system structure. However, Table 1 shows the key findings of the systems listed to provide a generalized idea. Table 1: Short comparison of the literature reviews System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 Open in new tab Table 1: Short comparison of the literature reviews System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 System structure . System type . Category . Renewable fraction (%) . COE ($/kWh) . PV-DG-BESS [9] Off-grid Community – 0.506 PV-DG [12] Off-grid Community 98.90 0.631 PV-DG-WT [12] Off-grid Community 99 0.651 PV-WT-DG-BESS [14] Grid-tied Commercial 25.04 0.075 PV-DG-BESS [14] Grid-tied Commercial 17.36 0.078 PV-BESS [28] Grid-tied Community 64.80 0.045 PV-WT-BESS [28] Grid-tied Community 64.80 0.047 PV-WT-DG [29] Off-grid Community – 0.319 PV-WT [29] Grid-tied Community – 0.053 PV [31] Grid-tied Residential 67.70 0.021 PV-BESS [31] Grid-tied Residential 67.70 0.034 PV-DG-WT-BESS [32] Off-grid Commercial 27.80 0.165 Open in new tab In this article, a grid-connected microgrid is designed to analyse cases obtained from HOMER [33] and a suitable case is proposed for an urban area in Mohammadpur, Dhaka-1207, Bangladesh. The objective of the research work is to provide significantly more clean energy at a cheaper cost to the people of the community. Real-time data have been taken from the survey following the simulation performed in HOMER. Also, surplus energy is sold to reduce the power/electricity deficit of the grid. Off-grid and on-grid modes are also assessed, considering feasible renewable resources for the selected community. Furthermore, renewable energy resources scopes will be explored in this research for the densely populated urban area. The local community of Mohammadpur is taken into consideration as it is one of the rising areas where people ponder to live. Real-time data obtained from the community people are further used for the simulation in HOMER Pro. Several microgrid approaches are analysed and, among them, the best combination is proposed for the community considering the COE and greenhouse gas (GHG) emissions. However, no previous study on microgrid design for the urban community was evident for the concerned area, i.e. Mohammadpur, Dhaka-1207. As a result, the designed grid-connected microgrid is a case study considering location, natural resources and load profiles. The organization of the paper is as follows. Section 1 explores the mathematical formulation used for modelling, and Section 2 contains simulation findings, including a full sensitivity analysis. Finally, conclusions are given in Section 3. 1 Methodology HRES is a system that combines several (two or more) electricity-generating alternatives. Renewable energy sources, fossil-fuel-based energy sources or an arrangement of the two is possible. Hybrid systems are typically used to supplement a single renewable-energy-based producing choice such as sole wind or solar, as these are intermittent sources, as well as to improve resource usage and minimize net cost [34]. Fig. 1 illustrates the block schematic of the planned HRES for the community. Fig. 1: Open in new tabDownload slide Block diagram of the system. 1.1 HOMER HOMER is the global standard in microgrid design and optimization software, based on decades of listening to the demands of customers all over the world and expertise in developing and installing microgrids and distributed power systems that can contain a mix of renewable energy sources, storage and fossil-based generation [33]. HOMER is a simulation program created by the National Renewable Energy Laboratory (NREL) in the USA to aid in the arrangement and construction of renewable-energy-based microgrids. HOMER is used to simulate the physical performance of an energy supply system as well as its lifespan cost, which is the total of capital and operational expenditures during its life. HOMER may also be used to analyse choices such as distributed generation units and stand-alone, off-grid and grid-connected supply systems for distant places, as well as other design possibilities. HOMER is intended to address the issues associated with microgrid analysis and design, such as the huge number of design possibilities and the doubt about important factors such as load increase and future fuel costs. The three main functions achieved by HOMER are simulation, optimization and sensitivity analysis [35]. Net present cost (NPC) and COE are the two main economic elements that are determined by the total annualized cost of the system. As a result, HOMER must compute the annualized costs of the system, which are the annualized costs of the components minus any miscellaneous costs [36]. NPC in HOMER sorts the lifespan cost of the system and includes all system costs, such as replacement cost, capital cost, operation and maintenance cost, fuel consumption cost and other expenses, such as credits caused by pollution emissions and grid cost. The difference between the nominal interest rate and the inflation rate equals the real interest rate, which must be taken into consideration. Furthermore, expenses into the system in terms of perpetual dollars are considered. To simulate using HOMER, various factors such as component pricing, load demand, renewable resource data such as solar irradiation, component specs and so on must be entered into the software as input parameters [37]. From utility-scale and distributed generation to stand-alone microgrids, HOMER maximizes the value of your hybrid power system precisely, which makes HOMER better than PV*SOL and PVsyst [38]. Equations 1 and 2 are used to calculate the total NPC and COE: CNPC=CaCRF(i,Np)(1) Cost of Energy=CatEp+Egs+Ed (2) where CNPC represents the NPC, Ca represents the total cost (annualized), CRF is the capital recovery factor is constant which is dependent on i (annual real interest rate in percent) and Np (project lifetime). However, CRF is constant here as it is dependent on i and Np. For each project, i and Np are constant as different projects have different project lifetime and annual real interest rates. Cat represents the annual cost, Ep represents the primary load, Egs represents the sold energy to grid (yearly) and Ed represents the deferrable load. 1.2 Site location Mohammadpur is a sub-district in Dhaka District, Dhaka Division, Bangladesh, and is located 4 km south of Dhaka North City Corporation [31]. Mohammadpur was primarily intended as a moderate-density residential neighbourhood for middle-income residents. This region has been transformed into a small metropolis of apartment complexes because of massive urbanization and has a constantly growing population made up primarily of native Bangladeshi, with vast and expanding informal settlements. The coordinates are 90.36 E, 23.76 N in a residential community and the electrical demand is met by a three-phase 440-V distribution line from the power grid. Fig. 2 illustrates the site location. Fig. 2: Open in new tabDownload slide Site location. 1.3 Load profile The most significant aspect of simulation and optimization is the load profile of each location. Some sites, such as education centres, hotels, hospitals and industrial towns, contain real-world load-consumption statistics that may be used in the simulation. As time-series data, these real-world data are loaded into HOMER. However, in some regions, particularly distant and rural locations, where real load-consumption statistics are unavailable, the load profile should be projected with consideration for the specifications of the area. These data sets are inputted into HOMER as a daily profile and HOMER uses them in power balance constraint calculations. The community comprises 22 buildings consisting of 264 apartments that are primarily utilized for residential purposes. As a result, the peak demand of the site changes substantially between winter and summer, as well as weekdays and weekends. Despite its small size, the peak load is 366.53 kW with a consumption of 1739.74 kWh/day because of the dwellings feature of the significant loads. Table 2 shows the considered annual load profile of the community. Fig. 3 shows the hourly load profile for the community; underlying data can be found in the online Supplementary Data. Table 2: Monthly consumption Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Open in new tab Table 2: Monthly consumption Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Months . Consumption (kWh) . January 1057.15 February 1102.11 March 1420.02 April 1796.22 May 1845.32 June 1922.15 July 2116.99 August 2563.11 September 2435.20 October 2031.02 November 1403.65 December 1125.26 Open in new tab Fig. 3: Open in new tabDownload slide Hourly load profile of the community. 1.4 Resources Renewable resources data, such as solar irradiation statistics with a clearness index for specific areas, are necessary for the simulation in HOMER. The sun irradiation for the suggested site was obtained from the surface meteorological and solar energy database of NASA [39]. Fig. 4 depicts the total amount of yearly sun irradiation for the suggested location with a clearness index. For the site under consideration, an annual average irradiation of 4.86 kWh/m2/day is utilized. The condition of the sky technique can anticipate when the sun will shine for solar resources. Equations (3) and (4) are used to calculate the association between the relative sunlight duration and the condition of the sky: Fig. 4: Open in new tabDownload slide Variation in solar irradiation and the clearness index of the site. NbN=a1+b2+c3n123(3) N=[arcos(−sinφsinΔ+cos85cosΔcosφ)]7.5 (4) where Nb represents the hours of sunshine (bright), N represents the month over the representative day for which the Campbell–Stokes sunlight recorder remains sensitive, φ represents the latitude, δ represents the declination, n1 represents the number of days that remain clear in a month, n2 represents the number of mixed days in a month, n3 represents the number of overcast days in a month and n123 = n1+ n2+ n3η, which represents the total number of days in the month that is under consideration, where a, b and c all are meteorological components. The Angstrom equation can be used from sunshine to guess the global radiation as shown in Equation (5): XX0=y+znN (5) where, X/X0 represents the ratio of monthly averaged daily globally to monthly averaged daily solar radiation on a horizon surface. HOMER uses a three-step procedure to determine the WT’s power output at each time step. First, HOMER computes the wind speed at the turbine’s hub height. The turbine’s power output is then calculated at that wind speed and standard air density. Finally, HOMER modifies the power output value to account for the real air density. HOMER calculates the wind speed at the hub height of the WT at each time step using the inputs that are entered on the Wind Resource page and the Wind Shear entry [33]. If a logarithmic law is used, HOMER estimates the hub height wind speed using Equation (6): Uh=Ua(lnZhZ1) (lnZaZ1)−1(6) where Uh represents the wind speed at the WT’s hub height (m/s), Ua represents the wind speed at anemometer height (m/s), zh represents the wind-turbine hub height (m), za represents the height of the anemometer (m), z1 represents the length of surface roughness (m) and ln(..)represents the natural logarithm. If the power law is used, HOMER estimates the hub height wind speed using Equation (7): Uh=Ua(Za)−α(Zh)α(7) where α represents the law exponent of the power. Fig. 5 presents the average wind speed of the location. 1.5 Photovoltaic cell A PV unit is made up of numerous solar cells linked in parallel and series to meet the current and voltage needs of the system. The PV unit output voltage is determined by the series connection of the cells, whereas the PV unit output current is determined by the parallel connection of the cells. Where z is the thermal voltage, Rsh and Rs are the shunts and series resistances, and Id, Ip and I are the currents in the diode when it is directly polarized, the current generated by the solar cell and the current at the terminal of the solar cell, respectively. Fig. 5: Open in new tabDownload slide Average wind speed. The electrical equal circuit of a solar cell is depicted in Fig. 6 as a conventional single-diode model [28]. The connection between the parameters and variables of a solar cell is represented by Equation (8): Fig. 6: Open in new tabDownload slide Single-diode model. I=Ip− Id [e(V+IRsz)−1]−V+IRsRsh(8) 1.6 WT The ratio of the turbine speed, which is identified as a proportion of the speed of the turbine rotor to the wind speed, has a significant impact on the power of mechanical output for the wind speed. The ideal proportion of the speed of the turbine achieves the highest turbine energy-conversion efficiency at a certain wind speed. When wind speed changes, the speed of the rotor minimizes the ideal proportion of the speed and harvests the most energy from existing wind sources. The non-linear expression yields the expression of aerodynamic power (Paero) generated from a WT shown in Equation (9) [33]: Paero=0.5ρπr2v3CP(λ,β)(9) Here, r represents the radius of the rotor, the wind speed is v and a generic equation has been used to model Cp(λ, β) is calculated using Equation (10), where Cp (power coefficient) is the function of both TSR (λ) and the blade angle of inclination (β): CP(λ,β)= sin [π(λ−3)15.0.3β](0.44−0.0167β)−β(λ−3)0.00184(10) Here, the wind-turbine angle of inclination of the blades is β and λ denotes the ratio of the turbine speed, which is calculated using Equation (11): λ=ωtrv−1 (11) Again, the rotation speed of the blades is ωt. λ o is calculated using Equation (12): λo=cos−1[(0.3β−15)0.00184β(0.44−0.167β)π] [(0.3β−15)π] (12) As a result, the maximum power of the wind Pwind may be calculated using Equation (13): Pwind=0.5ρπr2v3CP(λo,β) (13) 1.7 Inverter The electricity generated by the PV panel is direct current (DC); however, because the load requires alternating current (AC), the generated DC power must be converted to AC. The inverter is used to change DC electricity into AC power at a consistent frequency using integrated maximum power point tracking. The quantity of power that the inverter can change is determined by the inverter rating. Pi is calculated using Equation (14): Pi=PpEi(14) where Pp represents the power of the output of the PV system and Ei represents the inverter efficiency found using Equation (15): Pp=PsNS(15) Ps represents the power generated by a single PV cell and Ns represents the number of PV panels. The size of an inverter in an integrated grid system is determined by two factors: grid selling capacity and local load demand. Pm is calculated using Equation (16): Pm=PL+Pg(16) where PL represents the maximum load demand and Pg represents the maximum amount of power delivered to the grid [36]. 1.8 Energy-storage system Equations (17) and (18) are the representations of battery energy levels for charging/discharging cycles. For charging the battery: B(t)=B(t−1)+tPcpEc(17) For discharging the battery: B(t)=B(t−1)+tPdpEd(18) where Pcp represents the power of charging at time t for the battery, Pdp represents the power that is discharging at time t, B(t) represents the energy level, t represents the time interval, and Ec and Ed represent the charging and discharging efficiency [37]. 1.9 Utility grid The system may be operated in two modes: grid-connected mode and off-grid mode. If the electricity generated by the hybrid energy system exceeds the load needs in the grid-integrated mode, the extra power is transferred to the utility grid. Es represents the electrical energy-storage system, Lr represents the demand of total load, the running hour is t, the sun hour is ts and Ep represents the output power from PV at a particular period. Eie represents the energy that is exported or imported from the grid; if Eie is positive (+ve), surplus energy is sold to the grid; if Eie is negative (–ve), energy is purchased from the grid [33]. Eie is computed using Equation (19): Eie=∑NtS=n0Ep(tS)+Es−Lrt(19) 1.10Component configuration and prices Prices of the components were selected carefully to reduce the cost for this article. Table 3 shows the configuration of the component and prices. Table 3: Specification of the components [32,40,41]. PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years The rate definition for the system is a 0.0750 $/kWh price followed by a sell-back price of 0.0690 $/kWh [42]. Open in new tab Table 3: Specification of the components [32,40,41]. PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years PV . Specifications . Panel type Flat panel Rated capacity 330 kW Temperature coefficient –0.35 Operating temperature 45°C Capital $345/kWp Replacement $205/kWp Operating cost $9/panel/year Lifetime 25 years Derating factor 85% Wind turbine Rated power 3 kW Maximum output power 3.6 kW Weight 106 kg Cut-in wind speed 2.5 m/s (5.58 mph) Rated wind speed 12 m/s (26.84 mph) Survival wind speed 55 m/s (122.65 mph) Noise level < 45 dB(A) Capital (per unit) $10 400.00 Replacement (per unit) $8898.00 Hub height 9.90 m Lifetime 25 years Battery Nominal voltage 12 V Nominal capacity 1.2 kWh Roundtrip efficiency 80% Maximum charge current 16.3 A Maximum discharge current 24.7 A Capital $194/kWh Operating cost $13/battery/year String size 4 Voltage 48 V Lifetime 7 years Inverter Capital $280 kW Operating cost $4 kW/year Efficiency 90% Lifetime 18 years The rate definition for the system is a 0.0750 $/kWh price followed by a sell-back price of 0.0690 $/kWh [42]. Open in new tab 2 Results and discussion HOMER Pro was used to simulate the designed microgrid to assess its operational and economic features. Because of its simpler, non-derivative optimization, HOMER Pro can run ample simulations in a few seconds. In this context, 56 potential cases were evaluated while considering various system designs to compute the option with the lowest NPC at the start of the project. Thirty-six of the entire number of simulated solutions were determined to be feasible; viable is defined as a solution capable of meeting the objectives, implying that 20 possibilities were discarded owing to limitations (due to capacity shortage). HOMER Pro removes all infeasible alternatives (because of a shortage of power sources, inverters or other factors) and ranks viable choices by total NPC. An hourly time-series simulation for every conceivable system design has been examined over a 25-year plan horizon. Four alternative schemes were considered to find the most advantageous approach for microgrid planning, as shown in Table 4. Table 4: Cases considered No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) Open in new tab Table 4: Cases considered No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) No . Cases . 1 PV–inverter–grid (proposed) 2 PV–inverter–battery–grid 3 Grid (base) 4 PV–wind turbine–inverter–battery–grid 5 PV–inverter–battery (off-grid) 6 PV–wind–inverter–battery (off-grid) Open in new tab Case-1 with an NPC of $868 818 was the most practical, optimal and cost-effective combination for the simulation location. COE was determined at $0.0442/kWh. The proportion of renewable energy in the generated energy was 57.5%. PV array of a total of 450 kW was selected for this case. On the other hand, Case-2 with higher NPC and slightly higher COE was found after simulation. NPC for this is $1.02 million, the COE is $0.0517/kWh and the proportion of renewable energy is 57.4%. A storage system with a total capacity of 211 kWh comprising lead-acid battery packs was chosen for this case. NPC for Case-3 ($917 608) is somewhat higher than that for Case-2 and COE for Case-3 (~$0.0771/kWh) is considerably higher than those of Case-1 and Case-2. As there are no PV and WTs, renewable energy was not generated then. After integrating WTs with Case-2, i.e. Case-4, NPC is $1.17 million and COE is $0.0585/kWh. The WT was installed in 15 buildings and the remaining 7 buildings were not suitable for installing WTs according to the orientation. Thus, renewable-energy generation from Case-4 was 59.8%. Nevertheless, after separating the grid from previous combinations, an off-grid microgrid was formed. Case-5 was designed with PV, inverter and lead-acid batteries; 530 kW of PV and 16 batteries were installed for each building. NPC for Case-4 is $4.39 million and COE is $0.462/kWh. Again, for Case-6, WT, PV, inverter and lead-acid batteries were installed. NPC of $4.52 million and COE of $0.609/kWh are found. For off-grid cases, NPC and COE are much higher than the on-grid cases. Components are sized to be larger than in on-grid systems and the amount of surplus electricity sold is also less. Also, unmet load and capacity shortage are larger compared to on-grid cases after PV and battery are sized at maximum capacity. Thus, off-grid systems are not feasible for the community. Table 5 illustrates the sizing parameters and Table 6 illustrates the obtained financial result in detail in contrast to the simulation. Table 5: Sizing parameters Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Open in new tab Table 5: Sizing parameters Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Cases . PV capacity (kW) . Wind-turbine capacity (kW) . Inverter capacity (kW) . BESS capacity (kWh) . Renewable fraction (%) . Capacity shortage (kWh/year) . Unmet load (%) . PV–inverter–grid (proposed) 450 0 450 0 57.5 4741 0.305 PV–inverter–battery–grid 450 0 450 211 57.4 3212 0.266 Grid (base) 0 0 0 0 0 5240 0.750 PV–wind turbine–inverter–battery–grid 450 45 450 211 59.8 3155 0.254 PV–inverter–battery (off-grid) 530 0 530 5069 100 17 259 3.35 Wind–inverter–battery (off-grid) 530 45 575 5069 100 6249 1.18 Open in new tab Table 6: Financial result Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Open in new tab Table 6: Financial result Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Cases . Capital cost ($) . Net present cost (NPC) ($) . Cost of energy (COE) ($/ kWh) . Operating cost ($/year) . Payback (years) . PV–inverter–grid (proposed) 436 500 868 818 0.0442 22 884 16.68 PV–inverter–battery–grid 470 644 1.02 million 0.0517 28 822 24.75 Grid (base) 0 917 608 0.0771 48 573 N/A PV–wind turbine–inverter–battery–grid 626 644 1.17 million 0.0585 28 694 N/A PV–inverter–battery (off-grid) 1.34 million 4.42 million 0.465 162 771 N/A Wind–inverter–battery (off-grid) 1.52 million 4.62 million 0.465 165 178 N/A Open in new tab HOMER also calculates the quantity of various GHGs that the suggested design will release. The grid emission factor that is keyed in HOMER is 0.67 tCO2/MWh [43]. According to the results, the system will emit carbon dioxide, sulphur dioxide and nitrogen oxide. Case-1 emits 281.47 tCO2/year and Case-3 peaks the GHG emission at 400.88 tCO2/year. Case-4 emits the least GHG in off-grid conditions because of having both WTs and PV. Case-5 and Case-6 do not emit GHG as the grid is not connected. The proposed case offers a 29.8% reduction in GHG emissions. Fig. 7 shows the emission of GHGs for every case. Fig. 7: Open in new tabDownload slide GHG emissions for the considered cases. HOMER also estimates NPC of the proposed design; Case-1 has NPC of $868 818 according to simulation results. Comparatively, the NPC of Case-1 is lower than that of the other cases considered. Photovoltaic generation of 450 kW is connected to the grid, but the other cases have a lead-acid battery (without the base case), the addition of the components affecting NPC. Off-grid cases have a significantly higher NPC as PV and battery were sized at the highest capacity. Fig. 8 illustrates the NPC of the cases. From the obtained result of the simulation, the proposed case possesses the most suitable COE for the area considered. COE is obtained at $0.0442/kWh, whereas for Case-2, COE is $0.0517/kWh because of the addition of the lead-acid battery. In the base case, electricity is directly purchased from the grid but in the proposed case, electricity is generated from both PV and the grid. COE of Case-5 and Case-6 is the highest among the cases, which is $0.465/kWh. COE concerning the cases is shown in Fig. 9. The annualized value of all expenditures and revenues, excluding initial capital costs, is the operating cost. The operating cost is only $22 884 in the proposed case. The proposed case has a lower operating cost, whereas the rest of the cases have a comparatively higher operating cost. Fig. 10 illustrates the graph of operating costs for considered cases; 4741 kWh/year of capacity shortage along with a slight unmet load of 0.375% was observed in the proposed case. Fig. 8: Open in new tabDownload slide NPC of the cases. Fig. 9: Open in new tabDownload slide COE of the cases. Fig. 10: Open in new tabDownload slide Operating cost of the cases. Case-5 has the highest unmet load and lowest capacity shortage, i.e. 3.35% and 17 259 kWh/year. Fig. 11 illustrates unmet load and capacity shortage together against cases. While designing system configurations, sensitivity analysis takes into account the unpredictability of input parameters. The average daily load, inflation rate and grid failure (per year) are taken into account in this research. With the increase in the load-scale average, NPC and COE are increasing linearly. As the load is increasing, the system tends to purchase more electricity than normal, which results in increasing NPC and COE. Fig. 11: Open in new tabDownload slide Capacity shortage and unmet load of different cases. Since the chosen community is in a well-established area, the possibilities of major renovation are very low. So, the proposed system will be adequate for the community. Concern with the increasing demand in the future can be dealt with, with room for capacity upgrades. If not, then the renewable fraction will eventually decrease. Fig. 12 shows the change in NPC and COE with the change in average daily load. Fig. 12: Open in new tabDownload slide Sensitivity output for loaded scaled average. If the inflation rate increases and exceeds the nominal discount, the rate of real interest is negative. With the increase in the inflation rate, NPC will increase and the capital recovery factor will decrease, resulting in a total annual cost decrease. Moreover, COE is the annualized cost-inverse multiplication of the total energy served, so as annualized cost or total energy served increases, COE increases. Dependency on inflation and change in financial parameters is shown in Fig. 13. An inflation clause should be considered while financing such a project. As the grid-failure frequency increases, the system is inadequate to supply demanded electricity, which increases the unmet load and capacity shortage. Fig. 13: Open in new tabDownload slide Sensitivity output for the inflation rate. For our designed case, the unmet load and capacity shortage are increasing slightly. Fig. 14 shows the change in unmet load and capacity shortage concerning grid-failure frequency. As the overall generating capacity of the country is progressively increasing, the load-shedding duration and frequency are reducing gradually. Such issues will not be a concern for the proposed system. Fig. 14: Open in new tabDownload slide Sensitivity output for grid failure per year. Bangladesh has achieved substantial socio-economic development in recent years, including improved life expectancy, per-capita income, poverty reduction and literacy rate, among other things. In the previous 10 years, the average GDP growth rate has grown by >6.7%, up from 3.7% in the 1970s. Bangladesh currently hopes to achieve sustainable economic growth through creating more employment; enhancing the quality of building energy, health and education, and transportation infrastructure; implementing energy conservation and efficiency measures; and guaranteeing good governance [44]. Despite being a developing county, the mass population in the community has less knowledge regarding modern technologies and always tends to save money for their future generations. So, the proposed Case-1, i.e. PV–inverter–grid, is a feasible case despite having an unmet load and capacity shortage. Case-4, i.e. PV–inverter–battery–grid, is more likely to be ignored by the community people because of high NPC though it has less unmet load and capacity shortage than Case-1. Concerning the payback period, only Case-1 has a payback period of 16.86 years after the system’s initial set-up. In other cases, no payback is observed, i.e. the capital is not recovered for this instance. 3 Conclusion In this article, a microgrid approach for a community in Mohammadpur is presented along with the feasibility. This approach is an effective way to mitigate frequent load-shedding problems and usage of sustainable energy broadly for a community is promoted. The combination of the proposed case is of PV–inverter–grid; the case is picked from cases that were obtained from HOMER Pro. Also, the massive use of fossil fuel is eliminated as a sustainable source is used to convert electricity directly. Electricity is supplied at a rate of $0.0442/kWh, which is ~32% cheaper than the current electricity rate in Bangladesh. The fraction of renewable energy is 57.5% and a payback period of 16.86 years was also observed. Furthermore, the suggested system emits fewer GHGs than a traditional fossil-fuel-based power station. The proposed design in this research is capable of meeting the demand of the investigated site, which has a connected load of 1739.74 kWh/day and a peak load of 366.53 kW. This study provides residents of this town with significantly more clean energy at a reduced cost, as well as the ability to sell extra energy to the grid to avoid frequent power outages. The stiffness of the suggested design is obtained from the obtained result and feasibility is assessed according to the mindset of the people in the community. After integrating the microgrid, stability analysis could be done for enhancing the design in the future. LiFePO4 batteries would be used instead of lead-acid batteries; also, dual-axis WTs could be used to improve the designed microgrid for the community. A P2P energy distribution structure could also be used with the designed microgrid. Acknowledgements The authors would like to express their appreciation to the ‘Energy and Technology Research Division’ of the ‘Advanced Bioinformatics, Computational Biology, and Data Science Laboratory, Bangladesh (ABCD Laboratory, Bangladesh)’, Chittagong (Chattogram), Bangladesh for assistance and encouragement. This study has required a considerable amount of assistance and direction, and the authors are very grateful to ABCD Laboratory, Bangladesh for providing them with limitless guidance during their research effort. Conflict of interest statement None declared. References [1] Kaur M , Dhundhara S, Verma YP, et al. Techno-economic analysis of photovoltaic-biomass-based microgrid system for reliable rural electrification . International Transactions on Electrical Energy Systems , 2020 , 30 : e12347 . Google Scholar Crossref Search ADS WorldCat [2] Taheruzzaman M , Janik P. Electric energy access in Bangladesh . 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Energy Strategy Reviews , 2020 , 32 : 100566 . 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

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Clean EnergyOxford University Press

Published: Jun 1, 2022

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