Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation

Cristina Ventura (), Giuseppe Marco Tina and Santi Agatino Rizzo
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Cristina Ventura: Department of Electric, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Giuseppe Marco Tina: Department of Electric, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Santi Agatino Rizzo: Department of Electric, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy

Energies, 2025, vol. 18, issue 15, 1-24

Abstract: In the Italian intra-day electricity market (MI-XBID), where energy positions can be adjusted up to one hour before delivery, imbalance charges due to forecast errors from non-programmable renewable sources represent a critical issue. This work focuses on photovoltaic (PV) systems, whose production variability makes them particularly sensitive to forecast accuracy. To address these challenges, a comprehensive methodology for assessing and mitigating imbalance penalties by integrating a short-term PV forecasting model with a battery energy storage system is proposed. Unlike conventional approaches that focus exclusively on improving statistical accuracy, this study emphasizes the economic and regulatory impact of forecast errors under the current Italian imbalance settlement framework. A hybrid physical-artificial neural network is developed to forecast PV power one hour in advance, combining historical production data and clear-sky irradiance estimates. The resulting imbalances are analyzed using regulatory tolerance thresholds. Simulation results show that, by adopting a control strategy aimed at maintaining the battery’s state of charge around 50%, imbalance penalties can be completely eliminated using a storage system sized for just over 2 equivalent hours of storage capacity. The methodology provides a practical tool for market participants to quantify the benefits of storage integration and can be generalized to other electricity markets where tolerance bands for imbalances are applied.

Keywords: intra-day market; photovoltaic; forecasting; hybrid artificial neural network; storage system (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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