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首页> 外文期刊>European transactions on electrical power engineering >A new multi-objective hybrid optimization algorithm for wind-thermal dynamic economic emission power dispatch
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A new multi-objective hybrid optimization algorithm for wind-thermal dynamic economic emission power dispatch

机译：一种新型多目标混合优化算法，用于风热动态经济发射电力调度

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摘要

This article presents a new optimization method to solve dynamic economic emission dispatch (DEED) problem incorporating wind power by using a hybrid nature inspired multi-objective algorithm based on equilibrium optimizer (EO) and differential evolution (DE). In the proposed algorithm, the EO with a competitive mechanism and an additional exploration strategy is devised to explore the whole search space, while the DE with a ranking mutation operator and an opposition-based learning strategy (OBL) is suggested to evolve the individuals of the external archive. The Kent chaotic map is adopted to generate a uniformly distributed initial population. The approach based on non-dominated sort and improved crowding distance is utilized to screen equilibrium particles' leaders and to update the external archive. These strategies attempt to obtain a Pareto optimal front with excellent diversity and good convergence. Moreover, a real-time constraints adjustment method and a penalty function method are combined to deal with complex constraints. The simulation results on the test system containing 10 thermal power units and one wind farm indicate that the proposed approach has much better performance than other methods for comparison.
机译：本文介绍了一种新的优化方法，以解决基于均衡优化器（EO）和差分演进（DE）的混合性质启发的多目标算法并通过杂交性质启发的多目标算法解决了一种新的优化方法。在所提出的算法中，设计了具有竞争机制和额外探索策略的EO，探讨整个搜索空间，而使用排名突变运营商和基于反对的学习策略（OBL）的DE建议进化个人外部存档。采用肯特混沌图来产生均匀分布的初始群体。基于非主导排序和改进的拥挤距离的方法用于筛分均衡粒子的领导者并更新外部存档。这些策略试图获得帕累托最优面部，具有出色的多样性和良好的收敛性。此外，将实时约束调整方法和惩罚功能方法组合以处理复杂的约束。仿真结果对包含10个火电机和一个风电场的测试系统，表明所提出的方法具有比其他比较的其他方法更好的性能。

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