An enhanced multi‐objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power

• Main Authors: Yingjie Bai, Xuedong Wu, Aiming Xia
• Format: Article
• Language: English
• Published: Wiley 2021-03-01
• Series: Energy Science & Engineering
• Subjects: dynamic environmental economic dispatch; enhanced multi‐objective differential evolution algorithm; selection strategies; wind power
• Online Access: https://doi.org/10.1002/ese3.827

Abstract Dynamic environmental economic dispatch (DEED) with wind power is an important extension of the classical environmental economic dispatch (EED) problem, which could provide reasonable scheduling scheme to minimize the pollution emission and economic cost at the same time. In this study, the combined dynamic scheduling of thermal power and wind power is carried out with pollutant emission and economic cost as optimization objectives; meanwhile, the valve‐point effect, power balance, ramp rate, and other constraints are taken into consideration. In order to solve the DEED problem, an enhanced multi‐objective differential evolution algorithm (EMODE) is proposed, which adopts the superiority of feasible solution (SF) and nondominated sorting (NDS) two selection strategies to improve the optimization effect. The suggested algorithm combines the total constraint violation and penalty function to deal with various constraints, due to different constraint techniques could be effective during different stages of searching process, and this method could ensure that each individual in the Pareto front (PF) is feasible. The results show that the proposed algorithm can deal with DEED problem with wind power effectively, and provide better dynamic scheduling scheme for power system.