Forecast uncertainties real-time data-driven compensation scheme for optimal storage control
This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementati...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co. Ltd.
2025-03-01
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| Series: | Data Science and Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764924000353 |
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| author | Arbel Yaniv Yuval Beck |
| author_facet | Arbel Yaniv Yuval Beck |
| author_sort | Arbel Yaniv |
| collection | DOAJ |
| description | This study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system. |
| format | Article |
| id | doaj-art-751e1c24715642a59cba99ad10129434 |
| institution | Kabale University |
| issn | 2666-7649 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj-art-751e1c24715642a59cba99ad101294342025-02-07T04:48:25ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492025-03-01815971Forecast uncertainties real-time data-driven compensation scheme for optimal storage controlArbel Yaniv0Yuval Beck1Corresponding author.; Department of Physical Electronics, School of Electrical Engineering, Tel Aviv University, Tel Aviv, 6997801, IsraelDepartment of Physical Electronics, School of Electrical Engineering, Tel Aviv University, Tel Aviv, 6997801, IsraelThis study introduces a real-time data-driven battery management scheme designed to address uncertainties in load and generation forecasts, which are integral to an optimal energy storage control system. By expanding on an existing algorithm, this study resolves issues discovered during implementation and addresses previously overlooked concerns, resulting in significant enhancements in both performance and reliability. The refined real-time control scheme is integrated with a day-ahead optimization engine and forecast model, which is utilized for illustrative simulations to highlight its potential efficacy on a real site. Furthermore, a comprehensive comparison with the original formulation was conducted to cover all possible scenarios. This analysis validated the operational effectiveness of the scheme and provided a detailed evaluation of the improvements and expected behavior of the control system. Incorrect or improper adjustments to mitigate forecast uncertainties can result in suboptimal energy management, significant financial losses and penalties, and potential contract violations. The revised algorithm optimizes the operation of the battery system in real time and safeguards its state of health by limiting the charging/discharging cycles and enforcing adherence to contractual agreements. These advancements yield a reliable and efficient real-time correction algorithm for optimal site management, designed as an independent white box that can be integrated with any day-ahead optimization control system.http://www.sciencedirect.com/science/article/pii/S2666764924000353Storage optimal schedulingReal-time storage controlPV-plus-storage managementForecast uncertainty compensation |
| spellingShingle | Arbel Yaniv Yuval Beck Forecast uncertainties real-time data-driven compensation scheme for optimal storage control Data Science and Management Storage optimal scheduling Real-time storage control PV-plus-storage management Forecast uncertainty compensation |
| title | Forecast uncertainties real-time data-driven compensation scheme for optimal storage control |
| title_full | Forecast uncertainties real-time data-driven compensation scheme for optimal storage control |
| title_fullStr | Forecast uncertainties real-time data-driven compensation scheme for optimal storage control |
| title_full_unstemmed | Forecast uncertainties real-time data-driven compensation scheme for optimal storage control |
| title_short | Forecast uncertainties real-time data-driven compensation scheme for optimal storage control |
| title_sort | forecast uncertainties real time data driven compensation scheme for optimal storage control |
| topic | Storage optimal scheduling Real-time storage control PV-plus-storage management Forecast uncertainty compensation |
| url | http://www.sciencedirect.com/science/article/pii/S2666764924000353 |
| work_keys_str_mv | AT arbelyaniv forecastuncertaintiesrealtimedatadrivencompensationschemeforoptimalstoragecontrol AT yuvalbeck forecastuncertaintiesrealtimedatadrivencompensationschemeforoptimalstoragecontrol |