Adaptive Online Optimization for Microgrids with Renewable Energy Sources
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918231560683520 |
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| author | van Weerelt, Wouter J. A. Fontan, Angela Bastianello, Nicola |
| author_facet | van Weerelt, Wouter J. A. Fontan, Angela Bastianello, Nicola |
| contents | In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as a constrained, online optimization problem. The proposed algorithm is characterized by a control-based design that applies the internal model principle, and a system identification routine tasked with identifying such internal model. In addition, in order to ensure the constraints are verified, we integrate a projection onto the constraint set. We showcase promising numerical results for the microgrid use case, highlighting in particular the enhanced adaptability of the proposed algorithm to changes in the internal model. The performance of the proposed algorithm is shown to outperform state-of-the-art alternative in the long-term, ensuring efficient management of the grid. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04778 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Online Optimization for Microgrids with Renewable Energy Sources van Weerelt, Wouter J. A. Fontan, Angela Bastianello, Nicola Optimization and Control Systems and Control In this paper we propose a novel adaptive online optimization algorithm tailored to the management of microgrids with high renewable energy penetration, which can be formulated as a constrained, online optimization problem. The proposed algorithm is characterized by a control-based design that applies the internal model principle, and a system identification routine tasked with identifying such internal model. In addition, in order to ensure the constraints are verified, we integrate a projection onto the constraint set. We showcase promising numerical results for the microgrid use case, highlighting in particular the enhanced adaptability of the proposed algorithm to changes in the internal model. The performance of the proposed algorithm is shown to outperform state-of-the-art alternative in the long-term, ensuring efficient management of the grid. |
| title | Adaptive Online Optimization for Microgrids with Renewable Energy Sources |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2512.04778 |