Pareto Efficient Incentive-based Real-time Pricing Model for Balanced Smart Grids
- Authors
- Seok, H.; Kim, S.
- Issue Date
- 1-Jan-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Demand management; genetic algorithm; Stackelberg game; welfare
- Citation
- IEEE Access, v.10, pp.2766 - 2774
- Journal Title
- IEEE Access
- Volume
- 10
- Start Page
- 2766
- End Page
- 2774
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/24472
- DOI
- 10.1109/ACCESS.2021.3138466
- ISSN
- 2169-3536
- Abstract
- In this study, a Pareto efficient incentive-based real-time pricing model was designed for balanced energy consumption scheduling (ECS) in a smart grid. In this model, the energy consumption of each subscriber is monitored and updated in real-time by an individual smart meter, and a cost-effective ECS is determined. The most recent research has not considered a balanced distribution of costs and profits to the participants. In general, there is a trade-off between service providers and subscribers. A service provider tries to maximize its profit, and a subscriber tends to minimize its cost. Therefore, the well-adjusted cost and profit distribution of a service provider and subscribers is considered by controlling the incentive degree in a Stackelberg game. The multiobjective genetic algorithm is applied to show the Pareto efficient solutions of a service provider and subscribers. Furthermore, welfare is introduced as the third objective in proposing a practical solution. It is used to select one of the multiple Pareto efficient solutions. The results are compared with those of the nonscheduling and day-ahead-scheduling models. Author
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