Information Gap Decision Theory-Based Electricity Purchasing Optimization Strategy For Load Aggregator Considering Demand Response

The gradual improvement of the electricity market and the rapid development of demand response (DR) technology not only make the load aggregator (LA) integrate the demand side resources (DSR) to participate in the market becoming true, but also bring new problems and challenges to LA for formulating electricity purchasing optimization strategy. With the goal of minimizing the operating cost of LA participating in electricity market to purchase electricity and providing auxiliary services in response to the peak-shaving demand planning, a deterministic day-ahead electricity purchasing decision-making model is established. Considering the uncertainty of wind power output and real-time price and the different attitude of LA toward risk caused by uncertainty, we adopt stochastic scenario planning method to deal with the uncertainty of wind power output. And the information gap decision theory (IGDT) is introduced to transform the deterministic model into a day-ahead electricity purchasing decision-making model of LA under two different risk attitudes: risk-averse and risk-seeking. In order to verify the effectiveness of the proposed approach, a case study has been investigated and the day-ahead electricity purchased optimization strategy for LA under different risk attitudes has been obtained. Moreover, the results confirm that participating in DR and improving the reliability of LA response can effectively reduce the operating cost of LA and improve the system stability.