Aggregator's scheduling and offering strategy for renewable integration based on information gap decision theory. (November 2022)

• Record Type: Journal Article
• Authors: Li, Jiamei; Ai, Qian; Chen, Minyu; Huang, Kaiyi
• Is Part Of: Energy reports. Volume 8(2022)Supplement 10
• Page Start: 163
• Page End: 171
• Publication Date: 2022-11
• Subjects: Renewable integration -- Peak regulation -- Step offering curve -- Information gap decision theory -- Aggregator; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2022.05.131
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Due to the increasing penetration of renewable energy, the problem of curtailing wind and photovoltaic is becoming more and more prominent. Peak regulation market is established for renewable integration in China. Owing to the flexibility in demand side, the aggregator is able to participant in the peak regulation market. For this purpose, this paper aims to derive an optimal scheduling and offering strategy for the aggregator in the peak regulation market. The proposed strategy applies information gap decision theory (IGDT) to deal with the uncertainty of the market clearing price. First, the aggregator's deterministic strategy is given which considers two types of load: temperature control load (TCL) and electric vehicle (EV). And based on IGDT, the robust strategy and the opportunistic strategy are presented. Then, in order to further reduce the profit loss caused by the forecasted error of the market clearing price, the step offering curve is developed. Finally, the results of case studies validate the effectiveness of the proposed strategy.