Critical load restoration in coupled power distribution and traffic networks considering spatio-temporal scheduling of electric vehicles. (October 2022)

• Record Type: Journal Article
• Authors: Su, Su; Wei, Cunhao; Li, Zening; Xia, Mingchao; Chen, Qifang
• Is Part Of: International journal of electrical power & energy systems. Volume 141(2022)
• Publication Date: 2022-10
• Subjects: Critical load restoration -- Coupled power distribution and traffic networks -- Electric vehicle (EV) -- Route planning -- Spatio-temporal scheduling; 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.2022.108180
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A critical load restoration strategy is proposed, considering spatio-temporal scheduling of EVs. The coupling of power distribution network and traffic network is considered. The deterministic transformation of chance constraints is used to handle the uncertainties of traffic network and EV initial SOC. The scheduling results under different strategies, confidence levels of chance constraints, and penalty factors are investigated. Abstract: In order to recover the power supply of critical loads after an accidental blackout, a critical load restoration strategy in coupled power distribution and traffic networks considering spatio-temporal scheduling of electric vehicles (EVs) is proposed in this paper. First, an EV route planning model is established based on the traffic network. And a multi-period coordination load restoration model is constructed containing distributed generators and EV charging stations. Next, simultaneously considered as constraints, the traffic network and power distribution network are coupled and the route planning model is integrated into the load restoration model to realize the spatio-temporal scheduling of EVs. The load recovery, the power distribution network economy, and EV driving cost are taken into consideration at the same time. Then, piecewise linearization, second-order conic relaxation (SOCR), and deterministic transformation of chance constraints are leveraged to reformulate the model. Finally, comparative analysis of the scheduling results in specific faulty scenarios is conducted on different strategies, confidence levels of chance constraints, and penalty factors. Numerical studies have proven the effectiveness and superiority of the proposed strategy, and the accuracy is verified as well.