← 返回
风电变流技术 储能系统 ★ 5.0

面向社会福利最大化的输电网络约束下风电集成需求响应两阶段框架

A two-stage wind power integrated demand response framework under constrained transmission network for social welfare maximization

作者 Vikram Singh · Manoj Fozdar · Tawfiq Aljohani · Satyendra Singh · Hasmat Malikd
期刊 Applied Energy
出版日期 2025年1月
卷/期 第 394 卷
技术分类 风电变流技术
技术标签 储能系统
相关度评分 ★★★★★ 5.0 / 5.0
关键词 Social welfare is maximized by congestion management thorough DR.
语言:

中文摘要

摘要 可再生能源(RESs)在现有电力系统中的融合正日益加速。尽管可再生能源的引入通过提供清洁能源有望实现可持续发展,但也给电力系统的可靠与安全运行带来了诸多挑战。这些问题包括线路阻塞、不同的节点电价以及网络损耗增加等。为解决上述问题,本文提出了一种双层优化框架。第一层旨在最大化系统的社会福利(SW),第二层则采用基于分时电价(ToU)的机制,在高峰时段引导负荷转移,从而缓解可能存在的网络阻塞。本文利用k-中心点聚类技术对用电负荷进行划分,以区分高峰、低谷和平段时段。此外,所提出的框架引入了条件风险价值(CVaR)指标以纳入风险控制,为平衡期望社会福利与相关风险提供了有效的解决方案。最后,通过对IEEE 30节点测试系统进行仿真验证,展示了所提模型的测试结果,证明了该方法的有效性与实用性。

English Abstract

Abstract The fusion of renewable energy sources (RESs) in the existing electricity network has been steadily gathering momentum day by day. Though the inclusion of renewables promises a sustainable environment by providing clean energy, it poses multiple challenges for the reliable and safe operation of power systems . Some of these issues include line congestion, different nodal prices, and increased network losses. To resolve these issues, a two-level framework is developed in this work. The first level intends to maximize the system's social welfare (SW), while at the second level, time-of-use (ToU) based tariffs are used to shift the load demand during peak hours, thereby reducing congestion, if present. A k-medoid clustering technique is utilized to differentiate between peak, valley, and flat periods. Further, risk control is incorporated into the proposed framework using the conditional value-at-risk (CVaR) metric, presenting an excellent solution to obtain a trade-off between the expected SW and associated risks. Finally, the proposed model has been tested, and the simulation outcomes are displayed for the IEEE 30-bus test system to validate the efficacy and practicality of the suggested approach.
S

SunView 深度解读

该双层优化框架对阳光电源ST系列储能变流器和PowerTitan系统具有重要应用价值。通过分时电价需求响应和CVaR风险控制,可优化储能系统充放电策略,缓解风电并网引起的线路阻塞和节点电价差异。k-medoid聚类算法可集成至iSolarCloud平台,实现峰谷平时段智能识别,指导储能系统参与电网社会福利最大化调度,提升含高比例新能源电网的安全经济运行水平,为储能PCS的GFM控制策略提供上层优化依据。