Optimal scheduling of electric vehicles aggregator under market price uncertainty using robust optimization technique. (May 2020)

• Record Type: Journal Article
• Authors: Cao, Yan; Huang, Liang; Li, Yiqing; Jermsittiparsert, Kittisak; Ahmadi-Nezamabad, Hamed; Nojavan, Sayyad
• Is Part Of: International journal of electrical power & energy systems. Volume 117(2020)
• Publication Date: 2020-05
• Subjects: Electric vehicles aggregator -- Market price uncertainty -- Robust optimization technique -- Charging and discharging schedule of electric vehicles; 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.2019.105628
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: Deterministic based scheduling of electric vehicles (EVs) aggregator is studied. Upstream grid price uncertainty is modeled via robust optimization technique. Robust scheduling of EVs aggregator is obtained via proposed technique. Abstract: Today, the uncertainty of upstream grid price is one of the most important challenging topics for the electric vehicle (EV) aggregators. So, a robust optimization technique is applied in this work to investigate robust scheduling of EV aggregators considering price uncertainty. The proposed EV aggregator participates in market price with the aim of maximizing the profit. In order to model the market price uncertainty with the mentioned technique, the upper and lower amounts of upstream grid prices are used instead of the estimated prices. The output of the proposed algorithm is used to build the various charging and discharging strategies which can be used by the operator to robust scheduling of EV aggregator under upstream grid price uncertainty. With considering the obtained results, it can be shown that the total profit of EV aggregator in optimistic strategy is raised 69.78% in comparison with the deterministic strategy while it is decreased 54.94% in pessimistic cases. It should be noted that the applied technique is formulated as a MIP model which is implemented in GAMS and global optimal result is guaranteed.