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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. [ABSTRACT FROM AUTHOR]

In a typical electricity market, the load aggregator (LA) bids in the wholesale market to purchase electricity and meet the expected demand of its customers in the retail market. However, considering the uncertainty of the wholesale market prices, the LA has to undertake all the risks arising from the price volatility in the wholesale market, which may make the LA suffer from financial loss under some scenarios such as price spikes. To this end, first, this article proposes an optimal bidding strategy model for the LA that implements the demand response program (DRP), which enables the LA to reduce the risk of financial loss caused by price volatility. The bidding model is a mixed integer linear programming problem, which can be solved efficiently via a commercial solver. Second, making a rational and quantitative compensation mechanism is significant for the LA to induce its customers to participate in the DRP while there are few studies investigating it, hence, this article designs a quantitative compensation mechanism for the LA. Case studies using a dataset from the Thames valley vision verify the effectiveness of the proposed bidding model. The results confirm that the implementation of DRP not only brings great profits to LA but also benefits the other entities in the electricity market. [ABSTRACT FROM AUTHOR]