基于自适应精英变异优化算法的微电网群调度研究

打开文本图片集

中图分类号：TM732 文献标志码：A

Abstract：Under the dual-carbon goals，economic-optimal scheduling of microgrid clusters with high distributed energy penetration involves nonlinear optimization problems.Traditional algorithms are prone to local optima，while existing intelligent algorithms still have room forimprovement in convergence speed and optimization accuracy.This paper proposes an adaptive elite mutation optimization algorithm（AEMOA）.It employsadaptive weightstobalanceglobal explorationand local exploitation dynamicall，utilizeselite opposition-based learning to expand the search space and improve optimization breadth，and enhances global escape capabilityand local fine-tuning accuracy through multi-scale collaboration of Cauchy and Gaussian mutations.The algorithm is applied to a 96-period scheduling model of microgrid clusters for simulation and validation.Acomparative analysis with classcal algorithms such as the genetic algorithm（GA）and simulated annealing （SA） demonstrates that the proposed AEMOA achieves improvements in both convergence speed and optimization accuracy.

Key words： microgrid cluster;optimal scheduling;adaptive elite mutation optimization algorithm； 96- hour period

在“双碳”目标引领与能源转型加速推进的背景下，分布式能源渗透率持续攀升，微电网群凭借其在整合新能源资源、提升供电可靠性与灵活性方面的核心优势，已成为新型电力系统的关键组成部分1。（剩余9022字）