期刊
ENERGY
卷 248, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2022.123558
关键词
Backtracking search optimization algorithm; Dynamic economic dispatch; Valve-point effects
资金
- State Key Laboratory of Biogeology and Enviromental Geology (China University of Geo-sciences) [GBL21801]
- National Nature Science Founda-tion of China [61972136]
In this paper, an adaptive backtracking search optimization algorithm (DABSA) is proposed for solving the dynamic economic dispatch problem with valve-point effect (DED_vpe). DABSA utilizes a dual-learning strategy and an adaptive parameter control mechanism to improve solution accuracy and overcome premature convergence. Experimental results demonstrate that DABSA is competitive in terms of low fuel costs and high robustness.
Dynamic economic dispatch with valve-point effect (DED_vpe) is a dynamic nonlinear high-dimensional optimization problem with non-smooth and non-convex characteristics. Meta-heuristic methods have become the mainstream for solving the DED_vpe problem. However, most of these methods only focus on minimizing the generation costs and ignore the algorithmic robustness. In this paper, an adaptive backtracking search optimization algorithm with a dual-learning strategy (DABSA) is proposed for solving the DED_vpe problem. In DABSA, a dual-learning strategy (DL) based on the current and historical optimal individuals is developed to update each individual. This updating strategy helps DABSA improve solution accuracy and overcome premature convergence. In addition, an adaptive parameter control mechanism (APC), which can automatically adjust parameter 'mixrate' value according to the current iteration number, is presented. To handle the system constraints, a 'repair thorn penalty' constraints handling approach is employed to lead non-feasible solutions towards the feasible region quickly. The performance of DABSA is assessed by testing on four DED problems containing 5, 10 and 30 units. The experimental results show that DABSA is very competitive compared with reported representative methods in yielding low fuel costs along with high robustness.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据