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School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China
Prof. Dr. Pei Zhang
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Prof. Dr. Anbo Meng
School of Automation, Guangdong University of Technology, Guangzhou 510006, China
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Game Theory and Artificial Intelligence Methods in Sustainable and Renewable Energy Power Systems

Abstract submission deadline
31 August 2025
Manuscript submission deadline
31 October 2025
Viewed by
2865

Topic Information

Dear Colleagues,

The intersection of sustainable and renewable energy systems with advanced computational technologies represents a dynamic frontier in engineering research. The pressing global need for sustainable energy solutions has driven rapid developments in both renewable energy technologies and their management systems. Game theory, especially evolutionary game theory, along with Artificial Intelligence (AI) methods such as machine learning, are proving essential in optimizing and managing the complex dynamics of power systems. These methodologies facilitate strategic decision-making and efficient resource allocation, which are essential for enhancing the reliability and sustainability of power systems. This Topic seeks to explore the confluence of game theory and AI in the design, operation, and optimization of sustainable and renewable energy systems, highlighting significant advances and applications that could shape the future of energy management. Publishing this Topic will provide a platform for researchers and practitioners to showcase novel methodologies and case studies which integrate game theory and AI in renewable energy systems. It will also foster multidisciplinary collaborations and inspire innovative solutions, which are critical for advancing the field of sustainable energy. Topics of Interest Submissions are invited on topics including, but not limited to:

  • evolutionary game theory applications in energy systems
  • AI-driven optimization of grid operations
  • machine learning for predictive maintenance in renewable systems
  • cooperative game strategies for load balancing in smart grids
  • AI models for forecasting energy production from renewable sources
  • decision-support systems for energy resource management
  • game theory in market-based energy allocation mechanisms
  • reinforcement learning for energy storage management
  • multi-agent systems in smart grid environments
  • game-theoretic approaches to demand response programs
  • pricing models in electricity markets using game theory
  • security strategies in energy systems via game theory
  • sustainability models using AI in energy sector
  • integration of renewable energy sources using AI and game theory
  • robustness and resilience of energy systems using AI techniques

Dr. Lefeng Cheng
Prof. Dr. Pei Zhang
Prof. Dr. Anbo Meng
Topic Editors

Keywords

  • game theory
  • artificial intelligence
  • machine learning
  • renewable energy systems
  • sustainable energy
  • smart grids
  • energy optimization
  • decision support systems
  • energy market
  • multi-agent systems

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
AI
ai 		3.1 7.2 2020 18.9 Days CHF 1600 Submit
Energies
energies 		3.0 6.2 2008 16.8 Days CHF 2600 Submit
Entropy
entropy 		2.1 4.9 1999 22.3 Days CHF 2600 Submit
Sustainability
sustainability 		3.3 6.8 2009 19.7 Days CHF 2400 Submit

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Published Papers (2 papers)

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53 pages, 4632 KiB  
Review
Game-Theoretic Approaches for Power-Generation Companies’ Decision-Making in the Emerging Green Certificate Market
by Lefeng Cheng, Mengya Zhang, Pengrong Huang and Wentian Lu
Sustainability 2025, 17(1), 71; https://doi.org/10.3390/su17010071 - 26 Dec 2024
Cited by 4 | Viewed by 1442
Abstract
This study examines the decision-making optimization of Power-Generation Enterprises (PGEs) in the green certificate market, with a focus on balancing bidding strategies and carbon-reduction targets. Given the increasing complexity of the green certificate market, the research employs Bayesian games, evolutionary games, and Stackelberg [...] Read more.
This study examines the decision-making optimization of Power-Generation Enterprises (PGEs) in the green certificate market, with a focus on balancing bidding strategies and carbon-reduction targets. Given the increasing complexity of the green certificate market, the research employs Bayesian games, evolutionary games, and Stackelberg games to systematically analyze the strategic behavior of PGEs and their interactions within the market framework. The findings demonstrate that game theory facilitates cost structure optimization and enhances adaptability to market dynamics under policy-driven incentives and penalties. Additionally, the study explores the integration of stochastic modeling and machine learning techniques to address market uncertainties. These results provide theoretical support for policymakers in designing efficient green electricity market regulations and offer strategic insights for PGEs aligning with carbon neutrality objectives. This work bridges theoretical modeling and practical application, contributing to the advancement of sustainable energy policies and the development of green electricity markets. Full article
(This article belongs to the Topic Game Theory and Artificial Intelligence Methods in Sustainable and Renewable Energy Power Systems)
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15 pages, 4570 KiB  
Article
Mutual Influences Among the Electricity Market, Carbon Emission Market, and Renewable Energy Certificate Market
by Hongbo Zou, Yuhong Luo, Fushuan Wen, Jiehao Chen, Jinlong Yang and Changhua Yang
Energies 2024, 17(23), 6139; https://doi.org/10.3390/en17236139 - 5 Dec 2024
Viewed by 696
Abstract
With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly [...] Read more.
With the advancement and development of the electricity market (EM), carbon emission market (CEM), and renewable energy certificate market (RECM), promoting the integration and growth of the EM alongside carbon emission trading, renewable energy certificate trading, and other related markets is becoming increasingly important for high-quality development of the power industry. Analyzing the intrinsic connections among these three types of markets can facilitate their coordinated development. In this study, we selected monthly data on European Union (EU) carbon emission futures, French electricity trading prices, and the price of Guarantees of Origin (GO) in France from March 2019 to March 2024 and utilized the Bayesian time-varying stochastic volatility vector autoregression model (TVP-SV-VAR) with time-varying parameters to effectively capture the dynamic changes among the three markets and to analyze the relationships and characteristics of the EM, CEM, and RECM across different historical contexts. Simulation results showed that the influences of the EM and CEM on the RECM were relatively low, with more pronounced short-term effects and relatively stable medium- and long-term effects. In contrast, the influences of the CEM and RECM on the EM were significant, with more pronounced short-term effects and stable medium- and long-term effects. The influences of the EM and RECM on the CEM were significant in the short term. Full article
(This article belongs to the Topic Game Theory and Artificial Intelligence Methods in Sustainable and Renewable Energy Power Systems)
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