Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation
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...
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MDPI AG
2025-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/15/4161 |
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| author | Cristina Ventura Giuseppe Marco Tina Santi Agatino Rizzo |
| author_facet | Cristina Ventura Giuseppe Marco Tina Santi Agatino Rizzo |
| author_sort | Cristina Ventura |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-cf9f746fbfe9413eba23a202dad9e7ed |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-cf9f746fbfe9413eba23a202dad9e7ed2025-08-20T03:02:58ZengMDPI AGEnergies1996-10732025-08-011815416110.3390/en18154161Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic GenerationCristina Ventura0Giuseppe Marco Tina1Santi Agatino Rizzo2Department of Electric, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, ItalyDepartment of Electric, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, ItalyDepartment of Electric, Electronics and Computer Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, ItalyIn 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.https://www.mdpi.com/1996-1073/18/15/4161intra-day marketphotovoltaicforecastinghybrid artificial neural networkstorage system |
| spellingShingle | Cristina Ventura Giuseppe Marco Tina Santi Agatino Rizzo Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation Energies intra-day market photovoltaic forecasting hybrid artificial neural network storage system |
| title | Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation |
| title_full | Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation |
| title_fullStr | Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation |
| title_full_unstemmed | Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation |
| title_short | Imbalance Charge Reduction in the Italian Intra-Day Market Using Short-Term Forecasting of Photovoltaic Generation |
| title_sort | imbalance charge reduction in the italian intra day market using short term forecasting of photovoltaic generation |
| topic | intra-day market photovoltaic forecasting hybrid artificial neural network storage system |
| url | https://www.mdpi.com/1996-1073/18/15/4161 |
| work_keys_str_mv | AT cristinaventura imbalancechargereductionintheitalianintradaymarketusingshorttermforecastingofphotovoltaicgeneration AT giuseppemarcotina imbalancechargereductionintheitalianintradaymarketusingshorttermforecastingofphotovoltaicgeneration AT santiagatinorizzo imbalancechargereductionintheitalianintradaymarketusingshorttermforecastingofphotovoltaicgeneration |