Optimal design of hybrid renewable-energy microgrid system: a techno–economic–environment–social–reliability perspective

The main objective of this paper is to select the optimal model of a hybrid renewable-energy microgrid (MG) system for a village in India. The MG comprises solar photovoltaic (PV) modules, a wind turbine generator, a biomass generator, a battery bank, a diesel generator and an electric vehicle. The optimal model selection is based on technical, economic, environmental, social and reliability parameters. A novel spoonbill swarm optimization algorithm is proposed to select the best hybrid MG system. The optimization results are compared with particle swarm optimization, the genetic algorithm and the grasshopper optimization algorithm. The number or size of components of the optimized MG system is 215 PV modules, 92 kW of wind turbine generation, 25 kW of biomass generation, 267 batteries, 22 kW of electric vehicles and 30 kW of diesel generation. The optimized system was selected based on technical factors such as renewable dispersion (93.5%), the duty factor (5.85) and excess energy (15 975 kWh/year) as well as economic considerations including the net present cost (Rs. 34 686 622) and the cost of energy (9.3 Rs./kWh). Furthermore, environmental factors such as carbon emissions (396 348 kg/year) and atmospheric particulate matter (22.686 kg/year); social factors such as the human progress index (0.68411), the employment generation factor (0.0389) and local employment generation (15.64643); and reliability parameters including loss of power supply probability (0.01%) and availability index (99.99%) were considered during the selection process. The spoonbill swarm optimization algorithm has reduced the convergence time by 1.2 times and decreased the number of iterations by 0.83 times compared with other algorithms. The performance of the MG system is validated in the MATLAB® environment. The results show that the MG system is the optimal system considering technical, economic, environmental, social and reliability parameters. Additionally, the spoonbill swarm optimization algorithm is found to be more efficient than the other algorithms in terms of iteration time and convergence time.