Many-Constraint Multi-objective Optimization of Grid-Connected Hybrid Renewable Energy System

Grid-connected hybrid renewable energy system (G-HRES) is demonstrated as effective in making use of renewable energies, e.g., solar, wind. This study proposes a novel multi-objective model and algorithm for optimizing the size of a typical G-HRES that is composed of photovoltaic (PV) panels, wind turbines, battery banks and diesels. Noticeably, the proposed model considers objectives of economy and environment under the premise of satisfying many constraints, and enables a decision maker to optimize both the number and the type of PV panel, wind turbine, battery and diesel generator as well as the PV panel installation angle, the wind turbine installation height. To effectively solve the model, in particular, dealing with many linear constraints, an adaptive multi-stage evolutionary algorithm is proposed. Lastly, a case study is presented to demonstrate the effectiveness and efficiency of the proposed model and algorithm.