Optimal Scheduling Strategy of Demand Response Aggregators Considering Customers' Complicated Response Characteristic

Demand response (DR) aggregators are mandatory to schedule their DR resources (e.g., customers responsiveness) to meet the requirement of the DR capacity bidding curve (DRCBC) which is referred to as the DR capacity that should be offered by DR aggregators at each time slot in the operation day. Otherwise, they will be punished by the market operator and suffer from the financial loss. Due to the complicated response characteristics of residential customers, the aggregator may not satisfy the DRCBC requirement perfectly. To this end, a methodology is proposed to help aggregators schedule the responsiveness of customers in an optimal way aiming at maximizing their profits. Firstly, based on the historical demand response data, customers' typical response curves are obtained through the clustering method, and the probability distribution function is used to describe the customers' response probability. Secondly, an optimal scheduling strategy model is established for a DR aggregator who manages different types of residential customers and energy storage (ES) units, which is formulated as a mixed-integer linear programming problem and can be solved by a commercial solver. Case studies demonstrate that the optimal scheduling model could satisfy the DRCBC requirement perfectly and help the DR aggregator obtain higher profit.