Stacking revenues from energy and reserve markets through the aggregation of flexible demands

Retailers traditionally deploy demand response (DR) mainly to reduce procurement costs in energy markets, overlooking the potential to leverage flexible loads in reserve or flexibility markets. This paper proposes a two-stage, data-driven framework that enables retailers to stack revenues from energy and reserve markets through the aggregation of flexible demands. The first stage employs inverse optimization (IO) to infer flexible load parameters from historical price and consumption data, while the second stage formulates a bilevel model capturing the strategic interaction between the retailer and the system operator. The bilevel problem is reformulated as a Mathematical Program with Equilibrium Constraints (MPEC) and McCormick envelopes. Applied to the Chilean electricity market, results show that multi-market DR participation increases retailer profits fourfold and reduces system reserve procurement costs by 8.91% during the actives hours of the demand response procurement, underscoring DR’s role in enhancing flexibility and delivering system-wide savings.