分布式发电与充电负荷结合对电动汽车计费可靠性的影响分析

Energy Storage Pub Date : 2025-02-10 DOI:10.1002/est2.70138
Khaliq Ahmed, Devkaran Sakravdia, Chandrakant Sharma
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引用次数: 0

摘要

本文研究的是基于可再生能源的分布式发电以及电动汽车(EV)充电负载在配电系统中的渗透。提出了一种将粒子群算法与安第斯秃鹰算法(PSO-ACA)相结合的优化方法,为系统设计提供了一条高效的路径。从最小化功率损耗和最大化可靠性两个方面分析了系统的性能。该研究通过使用基准33总线和69总线测试系统,详细评估了可靠性指标和功耗降低。研究结果表明,对于33母线系统,获得的有功功率损耗减少为64.3 KW,实际功率损耗减少68%,而对于69母线系统,实际功率损耗减少72% (62.8 KW)。这导致了可靠性指标的大幅降低,从而提高了系统的整体性能,因为混合优化技术显著提高了系统的可靠性。这些结果表明,先进的混合优化方法与可靠性分析相结合,在未来提供经济可行、可持续的可再生能源系统方面具有巨大的潜力。这一合作符合促进可持续能源解决方案和建立更具弹性和效率的能源框架的更大目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact Analysis of Combining Distributed Generation With Charging Loads for Electric Vehicles Accounting Reliability

This work is concerned with the penetration of renewable energy-based distributed generation along with electric vehicle (EV) charging loads into power distribution systems. This presents a new optimization procedure integrating Particle Swarm Optimization and the Andean Condor Algorithm (PSO-ACA) into a high-performing route for system design. It analyzes the performance of the system in terms of minimizing the loss of power and maximizing reliability. The study evaluates reliability indices and power loss reductions in detail by utilizing benchmark 33-bus and 69-bus test systems. The findings indicate that for the 33-bus system, the active power loss reduction obtained is 64.3 KW, with real power loss showing a 68% reduction, whereas for the 69-bus system, real power loss decrease is 72% (62.8 KW). This led to a substantial reduction in reliability indices thus enhancing the overall system performance as hybrid optimization techniques improved the reliability of the system remarkably. These results show the immense potential that advanced hybrid optimization approaches combined with reliability analyses have for delivering the economically viable, sustainable renewable energy systems of the future. This collaboration is in line with the larger objectives of promoting sustainable energy solutions and establishing a more resilient and efficient energy framework.

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