Sustainable microgrid design with peer-to-peer energy trading involving government subsidies and uncertainties

Abstract

Sustainable microgrid is a feasible approach to handle environmental impacts and satisfy customer demand due to its economic, environmental, and social benefits. Due to the high initial investment cost, the installation rate of microgrids has been limited. Various studies investigated the influence of government subsidies on sustainable microgrid design to increase the installation rate. However, the effect of government subsidies, financial factors, and elasticity coefficient of demand under peer-to-peer energy trading has not been explored in the current sustainable microgrids design literature. To overcome this limitation, this paper investigates the sustainable microgrid design problem to simultaneously consider the effect of government subsidies, peer-to-peer energy trading, time value of money, elasticity coefficient of demand, and uncertainties. The objectives are to maximize total profit and minimize the total environmental cost to satisfy electric energy demand. Fuzzy multi-objective programming is applied to determine the optimal decisions on the number, location, type of renewable energy, the capacity of renewable distributed generation sources, electricity flows, price for selling electricity to demand areas and P2P energy trading, and government subsidy rates. A genetic algorithm and its hybrid versions to include tabu search and simulated annealing are then used to solve the proposed model. Numerical experiments used to evaluate the performance of the proposed model and algorithms show that the proposed genetic algorithm is most effective in maximizing total profit and minimizing environmental cost. Computational results demonstrate that on average, the inclusion of the peer-to-peer trading and government subsidies in the proposed model increases total profit by 13.23% and reduces total environmental cost by 6.29%.