An Energy Management System to Optimize the Participation in the Day Ahead and Ancillary Service Markets

This paper presents the evolution of an Energy Management System developed by University of Genoa aimed at minimizing the operation costs of plants and Microgrids (MGs). In the updated version, the Ancillary Service Market (ASM) and Day Ahead Market (DAM) mechanisms are modelled to include the possibility of trading in these markets. Since the Transmission System Operator (TSO) rejection or acceptance in the ASM cannot be forecasted, a statistical approach is proposed. Specifically, the optimization process is divided in two steps: initially the optimal dispatching program is computed, identifying DAM offers and ASM bid and offers that can be proposed. Then, a Monte Carlo method is implemented: by receiving a user defined acceptance rate, the TSO decisions are simulated by extracting sets of awarded proposals from a Probability Density Function (PDF). For each extraction, the second step of the optimization re-dispatches the units based on the bidding program. A PDF of the revenues/costs is the final outcome. Thereafter, the statistical moments of the resulting PDF can be analysed to estimate the profitability of the participation to the markets. A test performed on a real plant over one year demonstrates that longtime horizons may be simulated within reasonable computational time allowing to maintain a high level of details to model devices and markets.