View in EDS

Computational Game-Theoretic Models for Adaptive Urban Energy Systems: A Comprehensive Review of Algorithms, Strategies, and Engineering Applications.

Saved in:
Bibliographic Details
Title: Computational Game-Theoretic Models for Adaptive Urban Energy Systems: A Comprehensive Review of Algorithms, Strategies, and Engineering Applications.
Authors: Cheng, Lefeng1 (AUTHOR) chenglefeng@gzhu.edu.cn, Li, Manling1 (AUTHOR) limanling20@e.gzhu.edu.cn, Tan, Can1 (AUTHOR) tancan@e.gzhu.edu.cn, Huang, Pengrong1 (AUTHOR) hpr@e.gzhu.edu.cn, Zhang, Mengya1 (AUTHOR) zhangmengya@e.gzhu.edu.cn, Sun, Runbao1 (AUTHOR) Sunrunbao@e.gzhu.edu.cn
Source: Archives of Computational Methods in Engineering. Mar2026, Vol. 33 Issue 2, p2037-2114. 78p.
Subjects: Game theory, Smart power grids, Energy demand management, Multiagent systems, Clean energy, Reinforcement learning, Electric vehicle charging stations
Abstract: The rapid proliferation of smart grid infrastructures in urban environments has brought significant challenges to sustainable energy management, demand response optimization, and grid resilience due to the dynamic and uncertain behavior of electricity users. Game theory provides a rigorous and versatile framework for modeling the strategic interactions among power users, offering critical insights into user behavior in areas such as dynamic pricing, load management, and demand-side participation. This comprehensive review synthesizes state-of-the-art advancements in the application of game-theoretic models—including static, dynamic, repeated, and evolutionary games—to urban energy systems. It emphasizes how these models address the complexities of individual and collective user behavior, offering solutions for long-term strategy optimization, risk aversion, and multi-objective decision-making under uncertainty. By integrating advanced computational techniques such as reinforcement learning and multi-agent systems, game-theoretic approaches enable the development of adaptive and intelligent mechanisms for resilient energy management. This paper highlights practical case studies in demand response, distributed energy management, and electric vehicle charging, showcasing the transformative potential of game theory to optimize behavior and enhance system stability within sustainable urban grids. Furthermore, it identifies critical gaps in existing research, particularly the need to incorporate socio-economic factors and personalized behavior models into game-theoretic frameworks to better address the evolving demands of smart cities. This review advances the discourse on sustainable urban energy by proposing forward-looking strategies that integrate behavioral modeling with intelligent algorithms. It provides a robust theoretical foundation and actionable insights for researchers, policymakers, and practitioners, offering a pathway to design adaptive, resilient, and sustainable smart grids. By aligning with the critical challenges of urban energy systems and exploring the frontiers of game-theoretic innovation, this study establishes a blueprint for future research in sustainable cities and intelligent urban energy management. [ABSTRACT FROM AUTHOR]
Copyright of Archives of Computational Methods in Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:The rapid proliferation of smart grid infrastructures in urban environments has brought significant challenges to sustainable energy management, demand response optimization, and grid resilience due to the dynamic and uncertain behavior of electricity users. Game theory provides a rigorous and versatile framework for modeling the strategic interactions among power users, offering critical insights into user behavior in areas such as dynamic pricing, load management, and demand-side participation. This comprehensive review synthesizes state-of-the-art advancements in the application of game-theoretic models—including static, dynamic, repeated, and evolutionary games—to urban energy systems. It emphasizes how these models address the complexities of individual and collective user behavior, offering solutions for long-term strategy optimization, risk aversion, and multi-objective decision-making under uncertainty. By integrating advanced computational techniques such as reinforcement learning and multi-agent systems, game-theoretic approaches enable the development of adaptive and intelligent mechanisms for resilient energy management. This paper highlights practical case studies in demand response, distributed energy management, and electric vehicle charging, showcasing the transformative potential of game theory to optimize behavior and enhance system stability within sustainable urban grids. Furthermore, it identifies critical gaps in existing research, particularly the need to incorporate socio-economic factors and personalized behavior models into game-theoretic frameworks to better address the evolving demands of smart cities. This review advances the discourse on sustainable urban energy by proposing forward-looking strategies that integrate behavioral modeling with intelligent algorithms. It provides a robust theoretical foundation and actionable insights for researchers, policymakers, and practitioners, offering a pathway to design adaptive, resilient, and sustainable smart grids. By aligning with the critical challenges of urban energy systems and exploring the frontiers of game-theoretic innovation, this study establishes a blueprint for future research in sustainable cities and intelligent urban energy management. [ABSTRACT FROM AUTHOR]
ISSN:11343060
DOI:10.1007/s11831-025-10364-y