Data-driven optimal strategy for scheduling the hourly uncertain demand response in day-ahead markets

Demand response (DR) is usually regarded as a valuable balancing and reserve resource that contributes to maintaining the power balance. However, electricity customers can freely decide whether to reduce their electricity consumption or not in the liberalized day-ahead market and therefore DR is difficult to predict. Considering that, this paper investigates a novel tri-level two-stage data-driven / distributionally robust optimization risk-averse and decision-dependence economic dispatch framework to incorporate DR uncertainties into the day-ahead electricity market clearing process. First, DR commitment is made after establishing the decisiondependence relationship between DR commitment and the corresponding dispatching uncertainty. Then, we construct an ambiguity set for the unknown distribution of the DR uncertainty by purely learning from the historical data. Considering the worst-case distribution within the ambiguity set, an optimal strategy is investigated for scheduling the hourly uncertain DR in day-ahead markets. Finally, a decomposition framework embedded with Benders' and Column-and-Constraint generation (CC & G) methods is built for identifying the optimal solution. The effectiveness of the proposed method is investigated through case studies on the IEEE 30 and IEEE 118 test systems.