Optimal Bidding Strategy of DER Aggregator Considering Dual Uncertainty via Information Gap Decision Theory

• Published in: IEEE transactions on industry applications 2021-01, Vol.57 (1), p.158-169
• Main Authors: Lu, Xiaoxing, Li, Kangping, Wang, Fei, Mi, Zengqiang, Sun, Rongfu, Wang, Xuanyuan, Lai, Jingang
• Format: Article
• Language: English
• Subjects: Batteries; Decision theory; Demand response (DR); distributed energy resources (DER) aggregator; Distributed generation; Energy sources; energy storage; Environmental protection; information gap decision theory (IGDT); Load management; Microgrids; Optimization; Photovoltaic systems; Schedules; Strategy; Sustainable development; Uncertainty; Volatility
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2020.3035553

Distributed energy resources especially wind and photovoltaic power, and demand response are highly valued in recent years for their advantages in environmental protection, sustainable development, and so on. However, their volatility poses double risks to the DER aggregator when formulating a profitable bidding strategy and schedule scheme. To this end, first, this article proposes an information gap decision theory-based optimal bidding strategy to model the dual uncertainties confronted by the DER aggregator without knowing the specific distribution pattern of uncertainties. Second, the DER aggregator is assumed to be risk-averse or opportunity-seeking, and the corresponding strategies could be obtained. The former comes up with a robust strategy under severe uncertain circumstances, and the latter presents a profit-maximization scheme while enduring more risks. The validity of the proposed method is examined using the dataset from the Thames valley vision project; the obtained results demonstrate that proper adjustment on aggregator's bidding strategy could be achieved based on its preference for high-profit or stability, which is also applicable for other market entities.