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A Simple Opposition-based Greedy Heuristic Search for Dynamic Economic Thermal Power Dispatch

机译：基于简单反对派的贪婪启发式搜索，用于动态经济火力发电调度

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This article proposes a simple opposition-based greedy heuristic search to solve a dynamic thermal power dispatch problem as a non-linear constrained optimization problem in the constrained search space. Opposition-based learning is applied at two stages. First, an initial population is generated to select good candidates by extensively exploring the search space. Second, it is implemented for migration to maintain diversity in the set of feasible solutions. The proposed method applies a mutation strategy by perturbing the genes heuristically and seeking a better one, which introduces parallelism and makes the algorithm greedy for a better solution. The greediness and randomness pulls the algorithm toward a global solution. Acceleration of the algorithm is independent of any parameter tuning. Feasible solutions are achieved heuristically by modifying the generation schedules within operating generation limits. Opposition-based greedy heuristic search has been implemented to analyze dynamic economic thermal power dispatch problems considering ramp-rate limits, prohibited operating zones, valve-point-loading effects, and transmission losses encountered in realistic power system operation. The validity of the proposed method is demonstrated on medium and large power systems. Opposition-based greedy heuristic search emerges as competitive with existing solution techniques. A Wilcoxon signed-rank test also proves the supremacy of opposition-based greedy heuristic search.
机译：本文提出了一种简单的基于对立的贪婪启发式搜索，以解决动态热电分配问题，将其作为约束搜索空间中的非线性约束优化问题。基于对立的学习分为两个阶段。首先，通过广泛探索搜索空间来生成初始种群以选择好的候选人。其次，实现迁移以在一组可行解决方案中保持多样性。所提出的方法通过启发式地干扰基因并寻求更好的方法来应用突变策略，这引入了并行性并使算法贪婪地寻求更好的解决方案。贪婪和随机性将算法推向了全局解决方案。算法的加速与任何参数调整无关。通过在运行发电极限内修改发电计划，可以启发式地获得可行的解决方案。已实施基于对立的贪婪启发式搜索，以分析考虑实际运行的电力系统中的斜率限制，禁止的工作区域，阀点负载效应和传输损耗的动态经济火力发电调度问题。在大中型电力系统上证明了该方法的有效性。基于对立的贪婪启发式搜索与现有解决方案技术相比具有竞争优势。 Wilcoxon秩和检验也证明了基于反对派的贪婪启发式搜索的至高无上性。

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