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Mathematics
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Artificial Intelligence and Game Theory
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Artificial Intelligence and Game Theory

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 997

Special Issue Editors

Dr. Lefeng Cheng

E-Mail Website
Guest Editor
School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China
Interests: artificial intelligence; game theory; smart grid; integrated energy systems; decision optimization theory
Dr. Wentian Lu

E-Mail Website
Guest Editor
School of Mechanical and Electric Engineering, Guangzhou University, Guangzhou 510006, China
Interests: artificial intelligence; game theory; smart grid; integrated energy systems; decision optimization theory

Special Issue Information

Dear Colleagues,

At present, various countries have formulated a new generation of artificial intelligence development plans to seize the opportunity for a new round of technological changes. Based on this, vigorously developing the application of a new generation of artificial intelligence in smart grids and integrated energy systems will change the entire traditional energy utilization model and further promote the intelligent development of the system. In addition to the above fields, this Special Issue aims to collect research on a new generation of artificial intelligence and game theory-related methods in important engineering fields such as electrical, electronic, mechanical, and computer science. 

Dr. Lefeng Cheng
Dr. Wentian Lu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • game theory
  • artificial intelligence
  • power system
  • sustainable and renewable energy
  • engineering optimization decision

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Published Papers (2 papers)

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Research

49 pages, 11591 KiB  
Article
Spontaneous Formation of Evolutionary Game Strategies for Long-Term Carbon Emission Reduction Based on Low-Carbon Trading Mechanism
by Zhanggen Zhu, Lefeng Cheng and Teng Shen
Mathematics 2024, 12(19), 3109; https://doi.org/10.3390/math12193109 - 4 Oct 2024
Viewed by 253
Abstract
In the context of increasing global efforts to mitigate climate change, effective carbon emission reduction is a pressing issue. Governments and power companies are key stakeholders in implementing low-carbon strategies, but their interactions require careful management to ensure optimal outcomes for both economic [...] Read more.
In the context of increasing global efforts to mitigate climate change, effective carbon emission reduction is a pressing issue. Governments and power companies are key stakeholders in implementing low-carbon strategies, but their interactions require careful management to ensure optimal outcomes for both economic development and environmental protection. This paper addresses this real-world challenge by utilizing evolutionary game theory (EGT) to model the strategic interactions between these stakeholders under a low-carbon trading mechanism. Unlike classical game theory, which assumes complete rationality and perfect information, EGT allows for bounded rationality and learning over time, making it particularly suitable for modeling long-term interactions in complex systems like carbon markets. This study builds an evolutionary game model between the government and power companies to explore how different strategies in carbon emission reduction evolve over time. Using payoff matrices and replicator dynamics equations, we determine the evolutionarily stable equilibrium (ESE) points and analyze their stability through dynamic simulations. The findings show that in the absence of a third-party regulator, neither party achieves an ideal ESE. To address this, a third-party regulatory body is introduced into the model, leading to the formulation of a tripartite evolutionary game. The results highlight the importance of regulatory oversight in achieving stable and optimal low-carbon strategies. This paper offers practical policy recommendations based on the simulation outcomes, providing a robust theoretical framework for government intervention in carbon markets and guiding enterprises towards sustainable practices. Full article
(This article belongs to the Special Issue Artificial Intelligence and Game Theory)
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16 pages, 15904 KiB  
Article
Decentralized Stochastic Recursive Gradient Method for Fully Decentralized OPF in Multi-Area Power Systems
by Umair Hussan, Huaizhi Wang, Muhammad Ahsan Ayub, Hamna Rasheed, Muhammad Asghar Majeed, Jianchun Peng and Hui Jiang
Mathematics 2024, 12(19), 3064; https://doi.org/10.3390/math12193064 - 30 Sep 2024
Viewed by 338
Abstract
This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem [...] Read more.
This paper addresses the critical challenge of optimizing power flow in multi-area power systems while maintaining information privacy and decentralized control. The main objective is to develop a novel decentralized stochastic recursive gradient (DSRG) method for solving the optimal power flow (OPF) problem in a fully decentralized manner. Unlike traditional centralized approaches, which require extensive data sharing and centralized control, the DSRG method ensures that each area within the power system can make independent decisions based on local information while still achieving global optimization. Numerical simulations are conducted using MATLAB (Version 1.0.0.1) to evaluate the performance of the DSRG method on a 3-area, 9-bus test system. The results demonstrate that the DSRG method converges significantly faster than other decentralized OPF methods, reducing the overall computation time while maintaining cost efficiency and system stability. These findings highlight the DSRG method’s potential to significantly enhance the efficiency and scalability of decentralized OPF in modern power systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Game Theory)
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