Day-ahead Market Optimal Bidding Strategy and Quantitative Compensation Mechanism Design for Load Aggregator Engaging Demand Response

摘要

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, given that the uncertainty of the wholesale market prices (WMPs), the LA has to undertake all the risk caused by the price volatility in the wholesale market, which makes the LA may fall into loss in some cases such as price spike. To this end, firstly, this paper 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 profit loss caused by price volatility. The bidding model is a mixed integer linear programming (MILP) problem, which can be solved efficiently. Secondly, making a rational and quantitative compensation mechanism is significant for the LA to induce its customers to participate in DRP while there are few studies investigating it, hence, this paper designs a quantitative compensation mechanism for the LA. Case studies using a dataset from the Thames valley vision (TVV) verify the effectiveness of the proposed bidding model. Besides, the results show that all entities in the electricity market enable to obtain benefits through the implementation of DRP.