Optimal bidding strategy for demand response aggregator in day-ahead markets via stochastic programming and robust optimization

Abstract

This paper evaluates the optimal bidding strategy for demand response (DR) aggregator in day-ahead (DA) markets. Because of constraint of minimum power quantity requirement, small-sized customers have to become indirect participants of electricity markets via the DR aggregator, who could offer various contracts accessing customers' demand reduction capacity in advance. In day-ahead markets, DR aggregator schedules those contracts and submits accumulated DR offers to the system operator. The objective is to maximize the profit of the DR aggregator. The key element affecting the bidding decision and aggregator's profit is the uncertain hourly DA prices. The stochastic programming adopts scenario-based approach for helping the profit-seeking DR aggregator control uncertainties. Robust optimization employs forecast values with bounded price intervals to address uncertainties while adjusting the robustness of the solution flexibly. Both scenarios can be modelled as mixed-integer linear programming (MILP) problems which could be solved by available solvers.