基于gpu的功率流方法:径向配电网重构问题的多目标功率优化模型

Hiba Yahyaoui, A. Dekdouk, S. Krichen
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引用次数: 0

摘要

本文讨论了配电网重构问题和潮流法。所研究的DNRP在电网的标准配置上运行。处理的主要目标是最大限度地减少功率损失,开关操作的数量和母线电压与其额定值的偏差。提出了一种基于贪婪迭代局部搜索的元启发式方法来解决DNRP问题。标准系统上的基准测试平台很好地说明了使用GrILS解决DNRP背后的动机。此外,所提出的方法和功率流方法在GPU架构上实现。对于大规模总线系统,GPU的实现在时间消耗方面显示了它对CPU的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
GPU-Based Power Flow Method a Multi-Objective Power Optimization Model for Reconfiguration Problem in Radial Distribution Networks
This article addresses the distribution network reconfiguration problem (DNRP) and the power flow method. The studied DNRP operates on standard configurations of electrical networks. The main objectives handled are the minimization of power loss, the number of switching operations and the deviations of bus voltages from their rated values. Metaheuristic approaches based on Greedy Iterated Local Search where proposed to solve the DNRP. A benchmarking testbed on standard systems well illustrates the incentive behind using GrILS for solving the DNRP. In addition, the proposed approaches and the power flow method where implemented on GPU architecture. The GPU implementation shows its effectiveness against the CPU in terms of time consuming specially for large-scale bus systems.
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