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001053059 0247_ $$2doi$$a10.1109/ICCEP65222.2025.11143761
001053059 037__ $$aFZJ-2026-01399
001053059 1001_ $$0P:(DE-HGF)0$$aZambrano, Holguer Humberto Noriega$$b0$$eCorresponding author
001053059 1112_ $$a2025 International Conference on Clean Electrical Power (ICCEP)$$cVillasimius$$d2025-06-24 - 2025-06-26$$wItaly
001053059 245__ $$aA Forecast-Driven Energy Management Methodology for Microgrids under Seasonal Variability
001053059 260__ $$bIEEE$$c2025
001053059 300__ $$a1108-1114
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001053059 520__ $$aThis paper introduces a dynamic energy management strategy based on model predictive control (MPC) for a microgrid with photovoltaic generation and an energy storage system. The strategy is designed to be able to acclimate to different seasonal situations. Moreover, to model realistic scenarios, some forecasting uncertainties are made current, using noise models for photovoltaic, load and cost variables. The performance of the MPC was also compared with an open-loop controller with pre-known trajectory prediction. Moreover, some week scenarios are carried out using real and processed solar generation data and load profiles from Milan, Italy, considering summer and winter periods. The results show the MPC strategy is able to optimize grid transactions, while the stability of the battery is kept even in hard and noisy scenarios.
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001053059 536__ $$0G:(DE-HGF)POF4-1122$$a1122 - Design, Operation and Digitalization of the Future Energy Grids (POF4-112)$$cPOF4-112$$fPOF IV$$x1
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001053059 7001_ $$0P:(DE-Juel1)179029$$aBenigni, Andrea$$b1$$ufzj
001053059 7001_ $$0P:(DE-HGF)0$$aLazzari, Riccardo$$b2
001053059 7001_ $$0P:(DE-HGF)0$$aPiegari, Luigi$$b3
001053059 773__ $$a10.1109/ICCEP65222.2025.11143761
001053059 8564_ $$uhttps://ieeexplore.ieee.org/document/11143761
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