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@INPROCEEDINGS{Zambrano:1053059,
author = {Zambrano, Holguer Humberto Noriega and Benigni, Andrea and
Lazzari, Riccardo and Piegari, Luigi},
title = {{A} {F}orecast-{D}riven {E}nergy {M}anagement {M}ethodology
for {M}icrogrids under {S}easonal {V}ariability},
publisher = {IEEE},
reportid = {FZJ-2026-01399},
pages = {1108-1114},
year = {2025},
abstract = {This 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.},
month = {Jun},
date = {2025-06-24},
organization = {2025 International Conference on Clean
Electrical Power (ICCEP), Villasimius
(Italy), 24 Jun 2025 - 26 Jun 2025},
cin = {ICE-1},
cid = {I:(DE-Juel1)ICE-1-20170217},
pnm = {1121 - Digitalization and Systems Technology for
Flexibility Solutions (POF4-112) / 1122 - Design, Operation
and Digitalization of the Future Energy Grids (POF4-112) /
1123 - Smart Areas and Research Platforms (POF4-112)},
pid = {G:(DE-HGF)POF4-1121 / G:(DE-HGF)POF4-1122 /
G:(DE-HGF)POF4-1123},
typ = {PUB:(DE-HGF)8},
doi = {10.1109/ICCEP65222.2025.11143761},
url = {https://juser.fz-juelich.de/record/1053059},
}