A two-stage modeling framework for the optimal utilization of load flexibility in energy and reserve markets

Abstract

Facilitating new businesses for retailers to unlock Demand Response (DR) services in electricity markets is key to enabling deep decarbonization. These new businesses will create additional revenue streams for retailers as well as enhance system flexibility. However, it will require retailers to appropriately manage their contracted flexible loads to be able to offer multiple services to the system. Therefore, this work proposes a two-stage modeling framework to determine the optimal operation of a retailer that provides DR services in energy and reserve markets. In the first stage, a data-driven estimation method based on Inverse Optimization (IO) is used to characterize consumer behavior for future operating scenarios. The results are inputted into the second stage, where the model optimizes retailers operations by formulating a bilevel optimization problem between the retailer and the reserve market in which the retailer is also constrained by loads optimization problems. Through several case studies based on the Chilean electricity market, we demonstrate the economic benefits of a multi-service approach by comparing it against the current approach of an actual retailer that only participates in the energy market. Additionally, we also demonstrate the benefits of enabling DR in reserve markets by quantifying reserve cost savings.