使用混合优化方法重新配置基于配电网的风能资源配置(考虑时变负荷

IF 1.5 Q4 ENERGY & FUELS
Mohammad Kazeminejad, Mozhdeh Karamifard, Ali Sheibani
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引用次数: 0

摘要

本研究提出了一种方法,通过使用灰狼优化遗传算法(HGWOGA),在考虑波动负载需求的情况下,整合风力涡轮机分配,优化径向配电网络。该方法旨在降低网络的能源损耗成本。通过结合遗传算法技术,该方法提高了灰狼优化器的效率,加快了收敛速度,避免了局部最优。该策略确定了网络的开放线路以及风力涡轮机的位置和容量,同时遵守辐射性和运行限制。它将负荷水平分为住宅、商业和工业,对各种情况下的能源损耗及其成本影响进行了全面分析,包括恒定负荷和动态负荷。研究表明,管理随时间变化的需求可以更准确地描述网络面临的挑战,从而在不同需求阶段进行有效的重新配置。此外,HGWOGA 还证明了其高效地找到全局最优的能力,即使在人口规模缩小的情况下也是如此--这是灰狼优化器无法单独实现的。对比分析表明,HGWOGA 在抑制网络能源损耗成本方面的效果优于以往的方法。通过同时应用网络重新配置和风力涡轮机分配(而非仅仅重新配置网络),该方法显著降低了电能损耗,减少了损耗成本,并改善了电压曲线。这种协同策略充分利用了风力涡轮机在网络中的动态分配,优化了能量流和配电效率,因此比传统的网络重新配置方法有了很大改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reconfiguration of distribution network-based wind energy resource allocation considering time-varying load using hybrid optimization method
This study proposed a method for optimizing a radial distribution network by integrating wind turbine allocation, considering fluctuating load demands, through the use of a hybrid Grey Wolf Optimizer-Genetic Algorithm (HGWOGA). This approach aims to decrease the network’s energy loss costs. By incorporating genetic algorithm techniques, the method enhances the Grey Wolf Optimizer’s efficiency, speeding up convergence and avoiding local optima. The strategy determines the network’s open lines and the placement and capacity of wind turbines, adhering to radiality and operational constraints. It categorizes load levels into residential, commercial, and industrial, providing a comprehensive analysis of energy losses and their cost implications under various scenarios, including constant and dynamic loads. The study suggests that managing time-varying demand offers a more accurate depiction of network challenges, enabling effective reconfiguration throughout different demand phases. Moreover, HGWOGA demonstrates its ability to find the global optimum efficiently, even with reduced population sizes—a feat not achievable with the Grey Wolf Optimizer alone. Comparative analyses reveal HGWOGA’s effectiveness in curbing network energy loss costs better than previous methodologies. By simultaneously applying network reconfiguration and wind turbine allocation, as opposed to merely reconfiguring the network, this approach notably reduces power loss, diminishes the cost of losses, and enhances the voltage profile. This synergistic strategy leverages the dynamic allocation of wind turbines within the network, optimizing energy flow and distribution efficiency, thereby offering a substantial improvement over conventional network reconfiguration methods.
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来源期刊
Wind Engineering
Wind Engineering ENERGY & FUELS-
CiteScore
4.00
自引率
13.30%
发文量
81
期刊介绍: Having been in continuous publication since 1977, Wind Engineering is the oldest and most authoritative English language journal devoted entirely to the technology of wind energy. Under the direction of a distinguished editor and editorial board, Wind Engineering appears bimonthly with fully refereed contributions from active figures in the field, book notices, and summaries of the more interesting papers from other sources. Papers are published in Wind Engineering on: the aerodynamics of rotors and blades; machine subsystems and components; design; test programmes; power generation and transmission; measuring and recording techniques; installations and applications; and economic, environmental and legal aspects.
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