A novel multi-objective evolutionary algorithm for hybrid renewable energy system design

As a result of the fossil fuel energy crisis and the focus on environmental protection, renewable energy is favoured throughout the world. Due to the unstable and unpredictable nature of renewable energy, a hybrid renewable energy system (HRES) that integrates traditional fossil fuel and renewable energy is a promising solution to overcome this challenge. In this paper, a novel multi-objective evolutionary algorithm with a diversity-maintained mechanism (MOEA-DM) is applied to the design of a multi-objective HRES. We propose a special environmental selection strategy to enhance the diversity of solutions, considering the discrete optimization of the HRES design. In the experiments, a stand-alone hybrid system including photovoltaic (PV) panels, wind power generators, batteries and diesel generators is applied to find the optimal combination of components with a set of nondominated solutions. The effectiveness, superiority and generalizability of the proposed algorithm are validated through a comparison with state-of-the-art algorithms.