Optimal cost and feasible design for grid-connected microgrid on campus area using the robust-intelligence method

Abstract

In this paper, a robust optimization and sustainable investigation are undertaken to find a feasible design for a microgrid in a campus area at minimum cost. The campus microgrid needs to be optimized with further investigation, especially to reduce the cost while considering feasibility in ensuring the continuity of energy supply. A modified combination of genetic algorithm and particle swarm optimization (MGAPSO) is applied to minimize the cost while considering the feasibility of a grid-connected photovoltaic/battery/diesel system. Then, a sustainable energy-management system is also defined to analyse the characteristics of the microgrid. The optimization results show that the MGAPSO method produces a better solution with better convergence and lower costs than conventional methods. The MGAPSO optimization reduces the system cost by up to 11.99% compared with the conventional methods. In the rest of the paper, the components that have been optimized are adjusted in a realistic scheme to discuss the energy profile and allocation characteristics. Further investigation has shown that MGAPSO can optimize the campus microgrid to be self-sustained by enhancing renewable-energy utilization.