Demand-side management and its impact on utility and consumers through a game theoretic approach

Mishra, Mrityunjay Kumar; Murari, Krishna; Parida, S. K.

doi:10.1016/j.ijepes.2022.107995

Cited by 15 publications

(2 citation statements)

References 28 publications

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“…Except for energy economy, P2G scenarios with NG can also take operational systems into consideration, such as bus voltage, overall system loss, and system reliability [43]. It is workable to focus on analyzing DR's impact on operation parameters through the NG approach, including peak-to-average ratio, bus voltage, and total system loss [44].…”

Section: ) Non-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: ) Non-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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“…The task of EHS is to generate all possible attack signatures for the training of SIDS. To do so, the EHS uses the Particle Swarm Optimization (PSO) algorithm [42] with a game theoretic approach [43]. It plays repeated games with the other modules of PRDFS.…”

Section: Ehsmentioning

confidence: 99%

A Resilient Cyber-Physical Demand Forecasting System for Critical Infrastructures against Stealthy False Data Injection Attacks

et al. 2022

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The safe and efficient function of critical national infrastructure (CNI) relies on the accurate demand forecast. Cyber-physical system-based demand forecasting systems (CDFS), typically found in CNI (such as energy, water, and transport), are highly vulnerable to being compromised under false data injection attacks (FDIAs). The problem is that the majority of existing CDFS employ anomaly-based intrusion detection systems (AIDS) to combat FDIAs. Since the distribution of demand time series keeps changing naturally with time, AIDS treat a major change in the distribution as an attack, but this approach is not effective against colluding FDIAs. To overcome this problem, we propose a novel resilient CDFS called PRDFS (Proposed Resilient Demand Forecasting System). The primary novelty of PRDFS is that it uses signature-based intrusion detection systems (SIDS) that automatically generate attack signatures through the game-theoretic approach for the early detection of malicious nodes. We simulate the performance of PRDFS under colluding FDIA on High Performance Computing (HPC). The simulation results show that the demand forecasting resilience of PRDFS never goes below 80%, regardless of the percentage of malicious nodes. In contrast, the resilience of the benchmark system decreases sharply from about 99% to less than 30%, over the simulation period as the percentage of malicious nodes increases.

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Strategic interaction among distribution network operator and residential end-users via distribution use of system charges in demand-side management environment

Mishra,

Al-Sumaiti,

Murari

et al. 2024

Applied Energy

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Demand-side management and its impact on utility and consumers through a game theoretic approach

Cited by 15 publications

References 28 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

A Resilient Cyber-Physical Demand Forecasting System for Critical Infrastructures against Stealthy False Data Injection Attacks

Strategic interaction among distribution network operator and residential end-users via distribution use of system charges in demand-side management environment

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