博弈论视角下智能电网需求响应研究综述

doi:10.3969/j.issn.2097-0706.2025.04.001

摘要/Abstract

摘要：

随着智能电网的快速发展与电力体制改革的深化，需求侧用户作为购电与售电双重身份的主体，如何在开放式电力市场中有效参与并实现多方共赢，成为当前研究的热点与难点。从博弈论视角出发，综合梳理并分析了智能电网需求响应（DR）的主要理论方法与实践应用。总结了电力需求侧的典型博弈模型及其分类，包括静态博弈、动态博弈、演化博弈与合作博弈等；探讨了在分布式能源管理、虚拟电厂与微电网环境下，博弈论用于DR优化、收益分配以及用户行为建模等方面的实现路径；针对不同模型在处理多主体决策、复杂网络结构与信息不对称等问题时的适用性和不足进行了深入剖析。综述表明，博弈论在多主体决策场景下具备出色的灵活性与适应性，尤其在应对用户负荷转移、可再生能源波动和价格激励设计等方面具有显著优势。随着电力市场与智能电网规模的不断扩大，动态博弈模型的计算成本、多代理系统中的协同机制设计以及信息不对称导致的策略不确定性问题依然突出。通过引入大数据与人工智能技术，可望进一步提升博弈模型在高维度、不完全信息及实时响应环境中的可行性与效率。总体而言，博弈论为解决智能电网需求侧响应中的多主体交互优化问题提供了重要理论支撑和技术手段。今后的研究可进一步拓展混合博弈模型，结合区块链等新兴技术完善用户数据共享与结算机制，推进多能耦合与跨学科协同调度，为电网安全、经济与低碳运行提供更加全面而高效的解决方案。

关键词: 博弈论, 智能电网, 需求响应, 电力市场, 分布式能源, 虚拟电厂, 大数据, 人工智能, 区块链, 多能耦合

Abstract:

With the rapid development of smart grids and the deepening of power system reforms， demand-side users， as both buyers and sellers of electricity， face challenges in effectively participating in an open electricity market while achieving a win-win situation for all parties. This has emerged as a research hotspot and challenge. From the perspective of game theory， the main theoretical methods and practical applications of demand response（DR） in smart grids are reviewed and analyzed systematically. The typical game models on the electricity demand side are summarized， including static， dynamic， evolutionary， and cooperative games， and the application of game theory in DR optimization， profit distribution， and user behavior modeling in the context of distributed energy resource management， virtual power plants， and microgrids is explored. Additionally， the applicability and limitations of these models in addressing issues such as multi-agent decision-making， complex network structures， and information asymmetry are analyzed. The review indicates that game theory exhibits excellent flexibility and adaptability in the scenarios of multi-agent decision-making， particularly showing distinct advantages in addressing user load shifting， renewable energy fluctuations， and price incentive design. However， as the scale of the electricity market and smart grids continues to expand， three critical challenges remain： computational cost of dynamic game models， design of coordination mechanisms in multi-agent systems， and strategic uncertainties caused by information asymmetry. Integrating big data and artificial intelligence technologies can further enhance the feasibility and efficiency of game models in high-dimensional， incomplete information， and real-time response environments. Overall， game theory provides important theoretical support and technical solutions for optimizing multi-agent interactions in smart grid demand response. Future research can further explore hybrid game models and integrate emerging technologies like blockchain to improve user data sharing and settlement mechanisms. This can promote multi-energy coupling and interdisciplinary coordinated scheduling， thereby providing more comprehensive and efficient solutions for achieving safe， cost-effective， and low-carbon power grid operation.

Key words: game theory, smart grid, demand response, electricity market, distributed energy resources, virtual power plant, big data, artificial intelligence, blockchain, multi-energy coupling

中图分类号:

TK01：TM76

程乐峰, 刘奕杭, 邹涛. 博弈论视角下智能电网需求响应研究综述[J]. 综合智慧能源, 2025, 47(4): 1-22.

CHENG Lefeng, LIU Yihang, ZOU Tao. Review of demand response in smart grids from the perspective of game theory[J]. Integrated Intelligent Energy, 2025, 47(4): 1-22.

图/表 3

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