Microgrid operation multi-objective optimization based on hybrid evolution algorithm with α-constraint dominant sorting

In order to reduce the operational cost and pollution emission, a multi-objective optimization model is built for microgrid and a hybrid evolution algorithm with α-constraint dominant sorting is proposed to solve the model, which applies the α-constraint dominant sorting mechanism to treat all constraints as the α-constraint levelness and takes the levelness as the evolutionary selection index to quickly transform all individuals into the feasible solution, significantly improving the constraint processing efficiency. A hybrid multi-objective evolution algorithm with non-dominated sorting is proposed to effectively combine the advantages of the DEA(Differential Evolution Algorithm) and EDA(Estimation of Distribution Algorithm) for overcoming the defects of low species diversity and premature convergence of single algorithm. The similarity sorting method is adopted to approach the ideal solution for realizing the multi-attribute decision and obtaining the optimal compromise solution. Case study for a microgrid shows that the proposed algorithm is effective and feasible. Electric Power Automation Equipment Press