Hybrid Microgrid Many-Objective Sizing Optimization With Fuzzy Decision
Bin Cao, Weinan Dong, Zhihan Lv, Yu Gu, Surjit Singh, Pawan Kumar
Abstract
The economics, reliability, and carbon efficiency of hybrid microgrid systems (HMSs) are often in conflict; hence, a reasonable design for the sizing of the initial microgrid is important. In this article, we propose an improved two-archive many-objective evolutionary algorithm (TA-MaEA) based on fuzzy decision to solve the sizing optimization problem for HMSs. For the HMS simulated in this article, costs, loss of power supply probability, pollutant emissions, and power balance are considered as objective functions. For the proposed algorithm, we employ two archives with different diversity selection strategies to balance convergence and diversity in the high-dimensional objective space. In addition, a fuzzy decision making method is proposed to further help decision makers obtain a solution from the Pareto front that optimally balances the objectives. The effectiveness of the proposed algorithm in solving the HMS sizing optimization problem is investigated for the case of Yanbu, Saudi Arabia. The experimental results show that, compared with the two-archive evolutionary algorithm for constrained many-objective optimization (C-TAEA), the clustering-based adaptive many-objective evolutionary algorithm (CA-MOEA), and the improved decomposition-based evolutionary algorithm (I-DBEA), the proposed algorithm can reduce the system costs by 7%, 13%, and 21%, respectively.