A cooperative demand response strategy based on repeated game and cartel mechanism

Yang, Jie; Ma, Tieding; Ma, Kai; Tian, Zhenhua; Zheng, Qian

doi:10.1016/j.epsr.2021.107475

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…In [86], a cooperative DR strategy is proposed to reduce user energy costs where the non-cooperative behavior in the cooperation was accounted for. Here, a cooperative DR model is formulated by minimizing coalition's total cost, including discomfort cost and energy cost.…”

Section: ) Cooperative Gamementioning

confidence: 99%

Game-Theoretic Applications for Decision-Making Behavior on the Energy Demand Side: a Systematic Review

Ji,

Liu,

Tang

2024

Prot. Control Mod. Power Syst.

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As an essential characteristic of the smart grid, energy demand users are being transformed from passive roles to active decision-makers. To analyze their decision-making behaviors, game theory has been widely applied on the demand side. This paper focuses on the classification and in-depth analysis of recent studies that propose game-theoretic approaches for decision optimization of multiple demand users. This analysis classifies scenarios into various game participant categories, including distributed energy prosumers, small-and middle-sized users, and large energy consumers. The in-depth analysis of each scenario, covering non-cooperative game, cooperative game, Stackelberg game, Bayesian game, and evolutionary game, is conducted by analyzing market operation mechanisms, model assumptions/formulations, and solution methods. Based on a comprehensive review of such studies, it is concluded that game-theoretic applications on the demand side can benefit both the grid and the users, e.g., reductions in the peak-to-average ratios and energy costs of the users. The prospects for the applications of game theory on the demand side are discussed, including application scenarios and methodologies. The overview presented in this paper is expected to support researchers in comprehending typical game-theoretic concepts, keeping with the latest research developments, and identifying new and innovative applications for the energy demand side.

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Section: ) Cooperative Gamementioning

confidence: 99%

Game-Theoretic Applications for Decision-Making Behavior on the Energy Demand Side: a Systematic Review

Ji,

Liu,

Tang

2024

Prot. Control Mod. Power Syst.

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“…In recent years, it has been revealed that game theory may be used in the analysis of energy management and price-based demand response strategies [88]. According to the authors [89], a demand response model built on a game-theoretic framework was introduced.…”

Section: ) Cooperative Game Theorymentioning

confidence: 99%

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

et al. 2023

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In recent years, there has been a significant growth in demand response (DR) as a cost-effective technique of providing flexibility and, as a result, improving the dependability of energy systems. Although the tasks associated with demand side management (DSM) are extremely complex, the use of large-scale data and the frequent requirement for near-real-time decisions mean that Artificial Intelligence (AI) has recently emerged as a key technology for enabling DSM. Optimization algorithm methods can be used to address a variety of problems, including selecting the optimal set of consumers to respond to, learning their attributes and preferences, dynamic pricing, device scheduling, and control, as well as determining the most effective way to incentive and reward participants in DR schemes fairly and effectively. The implementation optimization algorithm needs proper selection to mitigate the cost of energy consumption. Due to that reason, this paper outlines various challenges and opportunities in developing, utilizing, controlling, and scheduling the DR scheme's optimization algorithm. In addition, several issues in applications and advantages of optimization techniques in artificial intelligence approaches are discussed. The importance of implementing demand response mechanisms in developing countries is also presented. In addition, the status of demand response optimization in demandside management solutions is also illustrated congruently.

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“…Interestingly, the demand response (DR) has drawn a significant amount of attention in improving the hosting capacity for EV and renewable energy integration [30], [31]. As an incentive payment to use less electric power, a DR program can smoothen the profile of the system load and thus improve the hosting capacity [32], [33]. However, to the best of our knowledge, there has been no research on the characterization of the feasible region for EV integration coupled with a DR program.…”

Section: Introductionmentioning

confidence: 99%

Hosting Capacity Evaluation Method for Power Distribution Networks Integrated with Electric Vehicles

Dai,

Wang,

Hwang Goh

et al. 2023

Journal of Modern Power Systems and Clean Energy

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The large-scale deployment of electric vehicles (EVs) poses critical challenges to the secure and economic operation of power distribution networks (PDNs). Therefore, a method for evaluating the hosting capacity that enables a PDN to determine the EV chargeable area (EVCA) to satisfy the charging demand and ensure the secure operation is proposed in this paper. Specifically, the distribution system operator (DSO) serves as a public entity to manage the integration of EVs by determining the presence of the charging load in the EVCA. Hence, an EVCA optimization model is formulated on the basis of the coupling effect of the charging nodes to determine the range of the available charging power. In this model, nonlinear power flow equations and operational constraints are considered to maintain the solvability of the power flow of the PDN. Subsequently, a novel multipoint approximation technique is proposed to quickly search for the boundary points of the EVCA. In addition, the impact of the demand response (DR) mechanism on the hosting capacity is explored. The results show that the presence of the DR significantly enlarged the EVCA during peak hours, thus revealing the suitability of the DR mechanism as an important supplement to accommodate the EV charging load. The examined case studies demonstrate the effectiveness of the proposed model and show that the unmanaged allocation of the charging load impedes secure operation. Finally, the proposed method provides a reference for the allocation of the EV charging load and a reduction in the risk of line overloading.

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A cooperative demand response strategy based on repeated game and cartel mechanism

Cited by 6 publications

References 27 publications

Game-Theoretic Applications for Decision-Making Behavior on the Energy Demand Side: a Systematic Review

Game-Theoretic Applications for Decision-Making Behavior on the Energy Demand Side: a Systematic Review

Artificial Intelligence Application in Demand Response: Advantages, Issues, Status, and Challenges

Hosting Capacity Evaluation Method for Power Distribution Networks Integrated with Electric Vehicles

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