A Game-Theoretic Framework for Multiperiod-Multicompany Demand Response Management in the Smart Grid

By utilizing tools from the game theory, we develop a novel multiperiod-multicompany demand response framework considering the interactions between companies (sellers of energy) and their consumers (buyers of energy). We model the interactions in terms of a Stackelberg game, where companies set thei...

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Vydané v:IEEE transactions on control systems technology Ročník 29; číslo 3; s. 1019 - 1034
Hlavní autori: Alshehri, Khaled, Liu, Ji, Chen, Xudong, Basar, Tamer
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Closed form solutions
Companies
Consumers
Demand side management
Demand-side management (DSM)
Distributed algorithms
Energy consumption
Energy management
Equilibrium
Exact solutions
Game theory
Games
Incentives
Load management
power system economics
Smart grid
Smart grids
Subdivisions
Uniqueness
Utilities
ISSN:1063-6536, 1558-0865
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Shrnutí:By utilizing tools from the game theory, we develop a novel multiperiod-multicompany demand response framework considering the interactions between companies (sellers of energy) and their consumers (buyers of energy). We model the interactions in terms of a Stackelberg game, where companies set their prices and consumers respond by choosing their demands. We show that the underlying game has a unique equilibrium at which the companies maximize their revenues, while the consumers maximize their utilities subject to their local constraints. Closed-form expressions are provided for the optimal strategies of all players. Based on these solutions, a power-allocation game has been formulated, which is shown to admit a unique pure-strategy Nash equilibrium, for which closed-form expressions are also provided. This equilibrium is found under the assumption that companies can freely allocate their power across the time horizon, but we also demonstrate that it is possible to relax this assumption. We further provide a fast distributed algorithm for the computation of all optimal strategies using only local information. We also study the effect of variations in the number of periods (subdivisions of the time horizon) and the number of consumers. As a consequence, we are able to find an appropriate company-to-consumer ratio when the number of consumers participating in demand response exceeds some threshold. Furthermore, we show, both analytically and numerically, that the multiperiod scheme provides incentives for energy consumers to participate in demand response compared with the single-period framework studied by Maharjan et al. In our framework, we provide a condition for the minimum budget consumers need and carry out case studies using real-life data to demonstrate the benefits of the approach, which show potential savings of up to 30% and equilibrium prices that have low volatility.
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ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2020.2989272