当前位置：
X-MOL 学术
›
Energy Sci. Eng.
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
An enhanced multi‐objective differential evolution algorithm for dynamic environmental economic dispatch of power system with wind power
Energy Science & Engineering ( IF 3.5 ) Pub Date : 2020-11-04 , DOI: 10.1002/ese3.827 Yingjie Bai 1 , Xuedong Wu 1 , Aiming Xia 1
Energy Science & Engineering ( IF 3.5 ) Pub Date : 2020-11-04 , DOI: 10.1002/ese3.827 Yingjie Bai 1 , Xuedong Wu 1 , Aiming Xia 1
Affiliation
|
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.
中文翻译：
风力发电系统动态环境经济调度的改进多目标差分进化算法
风力发电动态环境经济调度（DEED）是经典环境经济调度（EED）问题的重要扩展，它可以提供合理的调度方案，以同时减少污染排放和经济成本。本文以污染物排放和经济成本为优化目标，进行了火电和风电联合动态调度。同时，还考虑了阀点效应，功率平衡，斜率和其他约束条件。为了解决DEED问题，提出了一种改进的多目标差分进化算法（EMODE），该算法利用可行解（SF）和非支配排序（NDS）两种选择策略的优势来提高优化效果。所提出的算法结合了总约束违规和惩罚函数来处理各种约束，因为不同的约束技术可能在搜索过程的不同阶段有效，并且该方法可以确保Pareto前沿（PF）中的每个个体都是可行的。结果表明，所提出的算法可以有效地解决风电的DEED问题，并为电力系统提供更好的动态调度方案。
更新日期：2020-11-04
中文翻译：
风力发电系统动态环境经济调度的改进多目标差分进化算法
风力发电动态环境经济调度（DEED）是经典环境经济调度（EED）问题的重要扩展，它可以提供合理的调度方案，以同时减少污染排放和经济成本。本文以污染物排放和经济成本为优化目标，进行了火电和风电联合动态调度。同时，还考虑了阀点效应，功率平衡，斜率和其他约束条件。为了解决DEED问题，提出了一种改进的多目标差分进化算法（EMODE），该算法利用可行解（SF）和非支配排序（NDS）两种选择策略的优势来提高优化效果。所提出的算法结合了总约束违规和惩罚函数来处理各种约束，因为不同的约束技术可能在搜索过程的不同阶段有效，并且该方法可以确保Pareto前沿（PF）中的每个个体都是可行的。结果表明，所提出的算法可以有效地解决风电的DEED问题，并为电力系统提供更好的动态调度方案。
京公网安备 11010802027423号