Sizing a Hybrid Renewable Energy System by a Coevolutionary Multiobjective Optimization Algorithm

Hybrid renewable energy system (HRES) arises regularly in real life. By optimizing the capacity and running status of microgrid (MG), HRES can decrease cost improve efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which usually solved by linear method. However, as more devices are added into MG, mathematical model refers to nonlinear, traditional method incapable solve. To address this issue, we first proposed HRES. Then, coevolutionary multiobjective algorithm, termed CMOEA-c, handle nonlinear part constraints. considering constraints objective values simultaneously, CMOEA-c easily jump out local optimal solution obtain satisfactory results. Experimental results show that, compared other state-of-the-art methods, algorithm competitive solving problems.