Building Day-Ahead Market Bidding and Offering Curves of a Virtual Power Plant Participating in a Manual Frequency Restoration Reserve-Based Demand Response Program

This paper proposes a risk-averse three-stage stochastic programming approach to develop the day-ahead market bidding and offering curves of a virtual power plant participating in a manual frequency restoration reserve-based demand response program. This participation requires that the virtual power plant should comply with the requests from the system operator to decrease its energy consumption from the grid by a specified capacity for a given time. The day-ahead market scheduling strategy of the virtual power plant is, therefore, determined considering the participation in the demand response program and the operation of the assets under its control, i.e., conventional and renewable generating units, a battery, inelastic and flexible demands, and an electrolyzer. Scenarios are used to model the uncertainty in the day-ahead electricity market prices and the activations of the demand response program. The performance of the proposed model is analyzed under a realistic case study based on the Spanish power system. Results show that providing the manual frequency restoration reserve-based demand response program leads to increases in the net day-ahead market participation costs and the rescheduling costs of the virtual power plant due to the program’s activations. Nevertheless, these increases are negligible in comparison with the remunerations obtained. Furthermore, it is concluded through sensitivity analyses that the power assigned in the demand response program and the risk-aversion level significantly influence the operation of the virtual power plant. Lastly, computational times lower than 1 h are obtained, which motivates extending the proposed approach to medium- and long-term models in future works.