Comparative study of metaheuristic algorithms for optimal sizing of standalone microgrids in a remote area community

Abstract

This paper evaluates the performance and suitability of four different metaheuristic algorithms for optimal sizing of standalone microgrids in remote area. The studied metaheuristic algorithms are particle swarm optimization, differential evolution, water cycle algorithm and grey wolf optimization. These algorithms are applied to optimize the capacity of diesel generator, fuel tank, solar photovoltaic, wind turbine, and battery energy storage in four different AC-coupled standalone microgrids for a remote area community in South Australia. The objective function is selected as the net present value of electricity over a 20-year lifetime. The optimisation study is conducted based on the real data of annual load consumption, ambient temperature, solar insolation, and wind speed of the site. Capital, replacement, and maintenance costs of components in Australian market are incorporated for the economic analysis. An operating power reserve is maintained based on the static and dynamic reserve concepts. Uncertainty analysis based on 10-year real data of renewable energies and load consumption is conducted. Sensitivity analysis is provided for variations of the battery price and capacity. The performance of the applied algorithms is evaluated by comparing the economic and operational results, as well as the computational time and optimization convergence. It is found that differential evolution algorithm is unreliable for optimal sizing problem of the studied standalone microgrids.