使用一对一优化算法优化大规模电力系统的可再生能源集成经济负荷调度

Vu Uyen Phuong Nguyen, Hanh Hoang Minh, Trung Thang Nguyen
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引用次数: 0

摘要

本研究介绍了一种名为 "一对一优化算法(OOBO)"的新型元启发式算法在解决可再生能源综合经济负荷调度问题(RI-ELD)时的应用,该算法同时考虑了风力发电厂和太阳能发电厂。整个研究的重点是最大限度地降低所有火力发电厂(TEPPs)的燃料总支出(OEF)。所考虑的电力系统由 20 个具有不同工作限制的 TEPP 组成。在 2500、2600 和 2700 兆瓦三种负荷需求水平情况下,应用 OOBO 解决给定问题。OOBO 在这三种情况下取得的结果与其他元启发式算法 Coati optimization algorithm(COA)在最佳 OEF(Bst.OEF)、平均 OEF(Aver.OEF)、最大 OEF(Max.OEF)等四个方面进行了比较。OOBO 不仅在所有比较方面都优于 COA,而且在所有三种负载需求情况下都能以更快的速度收敛到最佳 OEF 值。此外,在情况 2 和情况 3 中,无论负载需求如何增加,OOBO 都比 COA 显示出惊人的稳定性。通过观察这些结果,OOBO 不愧为解决大规模、高复杂度 RI-ELD 问题的高效搜索工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal renewable-integrated economic load dispatch for a large-scale power system using One-to-One Optimization Algorithm
This study presents the application of a new meta-heuristic algorithm called One-to-One optimization algorithm (OOBO) for solving the renewable-integrated economic load dispatch problem (RI-ELD) with consideration of both wind and solar power plants. The whole study focuses on minimizing the overall expenses of fuel (OEF) for all thermal electric power plants (TEPPs). The considered power system consists of twenty TEPPs with different working limits. OOBO is applied to solve the given problem in three cases of load demand level, including 2500, 2600, and 2700 MW. The results achieved by OOBO in the three cases are compared with other meta-heuristic algorithms called Coati optimization algorithm (COA) in the four aspects, such as Best OEF (Bst.OEF), Average OEF (Aver.OEF), Maximum OEF (Max.OEF). OOBO not only outperforms COA in all comparison aspects but also provides faster convergence speed to the optimal values of OEF at all three cases of load demand. Moreover, OOBO shows its surprising stability over COA regardless of the increase of load demand in Case 2 and Case 3. By observing these results, OOBO deserved the highly effective search tool for solving the large-scale and highly complex RI-ELD problem. 
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