Learning the Reluctance of Demand-Side Resources From Equilibrium in Price-Based Demand Response

IF 8.6 1区工程技术 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Smart Grid Pub Date : 2025-02-27 DOI:10.1109/TSG.2025.3546799

Xiaotian Sun;Haipeng Xie;Dawei Qiu;Yunpeng Xiao;Goran Strbac;Zhaohong Bie

引用次数: 0

Abstract

The reluctance of demand-side resources (DSRs) in demand response (DR) is not directly accessible, yet, significantly impacts the DR performance. This work aims to estimate DR reluctance from observed DR equilibrium outcomes by inverse variational inequality (VI). First, the definition and properties of DR reluctance are introduced. Then, the equivalent generalized Nash equilibrium condition in DR is derived by strong duality. Based on inverse VI technique, a data-driven linear-programming (LP) for learning DR reluctance is formulated. Finally, the proposed method is validated through a toy example and larger-scale cases, showing its effectiveness and scalability.

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从基于价格的需求响应均衡中学习需求侧资源的不情愿

需求侧资源在需求响应中的不情愿性是不可直接获取的，但对需求响应的绩效影响很大。本文旨在通过逆变分不等式（VI）从观察到的DR平衡结果估计DR磁阻。首先，介绍了DR磁阻的定义和性质。在此基础上，利用强对偶性导出了DR中的等价广义纳什均衡条件。基于逆VI技术，提出了一种数据驱动线性规划的磁阻学习方法。最后，通过一个小示例和更大规模的案例验证了该方法的有效性和可扩展性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Transactions on Smart Grid ENGINEERING, ELECTRICAL & ELECTRONIC-

CiteScore

22.10

自引率

9.40%

发文量

526

审稿时长

6 months

期刊介绍： The IEEE Transactions on Smart Grid is a multidisciplinary journal that focuses on research and development in the field of smart grid technology. It covers various aspects of the smart grid, including energy networks, prosumers (consumers who also produce energy), electric transportation, distributed energy resources, and communications. The journal also addresses the integration of microgrids and active distribution networks with transmission systems. It publishes original research on smart grid theories and principles, including technologies and systems for demand response, Advance Metering Infrastructure, cyber-physical systems, multi-energy systems, transactive energy, data analytics, and electric vehicle integration. Additionally, the journal considers surveys of existing work on the smart grid that propose new perspectives on the history and future of intelligent and active grids.