A Classification Algorithm for Revenue Range Estimation in Ancillary Service Markets

Alice La Fata (), Giulio Caprara, Riccardo Barilli and Renato Procopio
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Alice La Fata: Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, 16145 Genoa, Italy
Giulio Caprara: Nadar Italy SpA, Viale Monza 259, 20126 Milan, Italy
Riccardo Barilli: Nadar Italy SpA, Viale Monza 259, 20126 Milan, Italy
Renato Procopio: Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, 16145 Genoa, Italy

Energies, 2025, vol. 18, issue 19, 1-20

Abstract: In the last decades, the introduction of intermittent renewable energy sources has transformed the operation of power systems. In this framework, ancillary service markets (ASMs) play an important role, due to their contribution in supporting system operators to balance demand and supply and managing real-time contingencies. Usually, ASMs require that energy is committed before actual participation, hence scheduling systems of plants and microgrids are required to compute the dispatching program and bidding strategy before needs of the market are revealed. Since possible ASM requirements are given as input to scheduling systems, the chance of accessing accurate estimates may be helpful to define reliable dispatching programs and effective bidding strategies. Within this context, this paper proposes a methodology to estimate the revenue range of energy exchange proposals in the ASM. To this end, the possible revenues are discretized into ranges and a classification pattern recognition algorithm is implemented. Modeling is performed using extreme gradient boosting. Input data to be fed to the algorithm are selected because of relationships with the production unit making the proposal, with the location and temporal indication, with the grid power dispatch and with the market regulations. Different tests are set up using historical data referred to the Italian ASM. Results show that the model can appropriately estimate rejection and the revenue range of awarded bids and offers, respectively, in more than 82% and 70% of cases.

Keywords: ancillary service market; bidding strategy; pattern recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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