Volume 20: Sustainable Energy Solutions for a Post-COVID Recovery towards a Better Future: Part III

Optimal Operation of Virtual Power Plants Bidding in the Day-Ahead and Ancillary Services Markets Andrea Fusco, Domenico Gioffrè, Alessandro Francesco Castelli, Cristian Bovo, Emanuele Martelli

Abstract

This work presents a multi-stage stochastic Mixed Integer Linear Program with binary recourse for optimizing the day-ahead unit commitment of power plants and virtual power plants operating in the day ahead and balancing markets. Scenarios are characterized by profiles representing the expected maximum quantities of energy/bids accepted by the balancing market, and photovoltaic panels generation for each hour of the day. Since the deterministic equivalent MILP model cannot be solved in a practical computation time (> 24 hours), a novel decomposition is developed. Results show how the proposed decomposition approach provides close-to-optimal solutions in much shorter computational time (<20 minutes).

Keywords Multi-stage stochastic programming, unit commitment, Virtual Power Plant, balancing markets