Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the i...
详细信息
Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the integration of renewable energy sources, which introduce uncertainties and further tighten the constraints. This paper proposes a constrainedmultiobjectiveoptimization evolutionary algorithm (CMOEA) based on a dual-stage approach with a dual-population genetic operator. The proposed CMOEA is applied to a case study of a distribution system reconfiguration problem with severe constraints, uncertainties, and high computational demands, and it shows good performance in finding infeasible solutions with small constraint violations and feasible solutions with small objectives. Copyright (c) 2024 The Authors.
Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the i...
详细信息
Distribution system reconfiguration problems pose significant challenges due to their multiple objectives, high computational complexity, and severe constraints. These challenges become even more pronounced with the integration of renewable energy sources, which introduce uncertainties and further tighten the constraints. This paper proposes a constrainedmultiobjectiveoptimization evolutionary algorithm (CMOEA) based on a dual-stage approach with a dual-population genetic operator. The proposed CMOEA is applied to a case study of a distribution system reconfiguration problem with severe constraints, uncertainties, and high computational demands, and it shows good performance in finding infeasible solutions with small constraint violations and feasible solutions with small objectives.
暂无评论