Distributionally robust day-ahead scheduling for power-traffic network under a potential game framework. (May 2023)

• Record Type: Journal Article
• Authors: Deng, Haoran; Yang, Bo; Ning, Chao; Chen, Cailian; Guan, Xinping
• Is Part Of: International journal of electrical power & energy systems. Volume 147(2023)
• Publication Date: 2023-05
• Subjects: Power-traffic coupled network -- Day-ahead power scheduling -- Potential game -- Uncertainty -- Distributionally robust optimization (DRO); Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2022.108851
• Languages: English
• ISSNs: 0142-0615

Abstract

Abstract: Widespread utilization of electric vehicles (EVs) incurs more uncertainties and impacts on the scheduling of the power-transportation coupled network. This paper investigates optimal power scheduling for a power-transportation coupled network in the day-ahead energy market considering multiple uncertainties related to photovoltaic (PV) generation and the traffic demand of vehicles. The crux of this problem is to model the coupling relation between the two networks in the day-ahead scheduling stage and consider the intra-day spatial uncertainties of the source and load. Meanwhile, the flexible load with a certain adjustment margin is introduced to ensure the balance of supply and demand of power nodes and consume the renewable energy better. Furthermore, we show the interactions between the power system and EV users from a potential game-theoretic perspective, where the uncertainties are characterized by an ambiguity set. In order to ensure the individual optimality of the two networks in a unified framework in day-ahead power scheduling, a two-stage distributionally robust centralized optimization model is established to carry out the equilibrium of power-transportation coupled network. On this basis, a combination of the duality theory and the Benders decomposition is developed to solve the distributionally robust optimization (DRO) model. Simulations demonstrate that the proposed approach can obtain individual optimal and less conservative strategies. Highlights: Day-ahead power scheduling for power-transportation coupled network with uncertainty. Uncertainties were modeled by moment-based ambiguity set. Reconfiguration method of distributionally robust model based on potential function. The unified optimization framework improved dispatch profit.