Assessing the Feasibility of Game-Theory-Based Demand Response Management by Practical Implementation

Yu, Mengmeng; Zhang, Xiongfeng; Jiang, Junhui; Lee, Chang Dae; Hong, Seung Ho; Wang, Kai; Xu, Aidong

doi:10.1109/access.2021.3049768

Cited by 8 publications

(4 citation statements)

References 26 publications

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“…In the SG approach for small-and middle-sized users, the master-slave relation among game players is largely clear. That is, energy supply side (i.e., energy retailer [89], utility company [90], and power grid [91]) or energy management side (i.e., energy management center [92] and load aggregator [93]) participates in the game as leader, while the energy demand side (i.e., household appliances and EVs) is in the follower position. For the follower, the optimization problem generally aims to minimize/maximize energy cost/utility, whereas for the leader, the optimization goals have diversified forms, which can be divided into profit-oriented and social welfare-oriented forms.…”

Section: ) Stackelberg 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: ) Stackelberg 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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“…Two utility functions are modeled to meet the power supply and demand objectives. According to [37], DR is a challenging decision-making process since multiple entities must interact. Therefore, the game theory method is appealing due to its capacity to handle complicated decision-making issues.…”

Section: B Literature Reviewmentioning

confidence: 99%

“…In [31]- [36], the DR concept in the electricity market is formulated to increase the adoption to reality of MGs' operating conditions. In [37], the DR method is joined to a real-time pricing strategy under general conditions.…”

Section: B Literature Reviewmentioning

confidence: 99%

Game Theory-Based Bidding Strategy in the Three-Level Optimal Operation of an Aggregated Microgrid in an Oligopoly Market

et al. 2022

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This paper proposes a new framework for the optimal operation of a microgrid aggregator (MGA) that participates in an oligopoly electricity market. This aggregator obtains an optimal bidding (power and price) strategy for a multigrid (MG) system, i.e., a community MG. Consequently, the granted quantity, i.e., power in the electricity market, is deployed to optimally schedule the MG's resources to meet demand. As such, as per three-level optimization, the independent system operator (ISO) clears the market with the goal of maximizing the social welfare in the first stage and determining the hourly market price as well as players' credited power. In the second-level optimization process, the players select the optimal coefficient supply function equilibrium according to the power granted from the market. In third-level optimization, an optimal scheduling for MGs' resources and demand would be obtained according to the won power in the market to maximize the aggregator profit. In addition, a price-taker MGA is simulated for comparison with the price-maker MGA to highlight the advantage of the proposed technique. Furthermore, a bidding strategy based on game theory is proposed to obtain the optimal price and power of the oligopoly market players and maximize all players' profits. Finally, a test system including three generators is created to evaluate the performance of the devised bidding strategy. The results show that the proposed bidding strategy can optimally calculate the focal point of the Nash equilibrium (NE) in the oligopoly electricity market.INDEX TERMS game theory, microgrid operations, oligopoly, optimization, bidding I. INTRODUCTION A. OPERATION OF THE MICROGRID

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“…To test the balanced coordination between the community-integrated energy system and electric vehicle charging stations and the effective role of flexible demand response and RES, a real-time case study in North China has been considered. Stackelberg game-based DR models have been a great source of benefits for providing power system stability to both consumers and the main grid [24]- [26]. From [8]- [13] to [27]- [29], it has been observed that the research interest was on the robust solution and lack of focus on the opportunistic solution.…”

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confidence: 99%

A Smart Power System Operation Using Sympathetic Impact of IGDT and Smart Demand Response With the High Penetration of RES

2022

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The enhanced penetration of available renewable energy sources (RES) is preferred overutilizing the maximum cost budget for the conventional power system operation. Severe uncertainty and power generation and load demand balance are the pre and post-challenges of RES penetration respectively. Penetration of RES can be made effective by modeling the RES uncertainty with a computationally efficient technique and controlling the load demand smartly. In this paper for the smooth and stable penetration of RES, the uncertainty of RES is modeled using the sympathetic impact of information gap decision theory (SI-IGDT) to deal with minimum possible uncertainty. Smart demand response (SDR) is modeled using a virtual layer as a smart demand response operator (SDO) between the main grid and consumers for the post-challenge of RES penetration. The SDO categorizes consumers into virtual prosumer (VP), real prosumer seller (RPS), and real prosumer buyer (RPB) using a power flow conditional algorithm (PFCA). The uncertainty of RES is subsequently optimized and implemented using the firefly optimization algorithm (FOA) and the power flow algorithm (PFA). To achieve technical and economic benefits for the main grid and all consumers, a Stackelberg game is formulated using PFCA and multi-objective FOA (MFOA). MATLAB is used for the implementation of the algorithms and the test system. Simulation results show that the maximum available RES power is penetrated up to 300 %, and load demand reduction is observed up to 62% which ultimately reduces the power flow loss by 70%.INDEX TERMS Firefly algorithm, Information gap decision theory, Renewable energy sources, Smart demand response, Stackelberg game.

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Assessing the Feasibility of Game-Theory-Based Demand Response Management by Practical Implementation

Cited by 8 publications

References 26 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

Game Theory-Based Bidding Strategy in the Three-Level Optimal Operation of an Aggregated Microgrid in an Oligopoly Market

A Smart Power System Operation Using Sympathetic Impact of IGDT and Smart Demand Response With the High Penetration of RES

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