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Peer-reviewedCC BYGreen Energy and Environmental Technology

Simulation-Based Supervisory Monitoring of Photovoltaic Mismatch in a Hybrid Greenhouse Microgrid

Abstract

Hybrid greenhouse microgrids require reliable supervisory information because photovoltaic generation can be degraded by partial shading while critical agricultural loads remain dependent on the available renewable supply. This work presents a MATLAB/Simulink system-level simulation framework for a hybrid greenhouse microgrid integrating photovoltaic generation, wind generation, battery storage, grid support, and representative greenhouse loads. A photovoltaic mismatch-awareness layer based on the EMC indicator, corresponding mathematically to the mean squared error, compares synchronized nominal and disturbed photovoltaic power trajectories using a five-sample moving window. The quantitative evaluation includes an explicit 24-h no-shading baseline together with mild, moderate, and severe imposed shading conditions. The median EMC increased from 6.77×10 3 W 2 under nominal operation to 1.62×10 4 , 8.01×10 4 , and 1.81×10 5 W 2 under mild, moderate, and severe shading, respectively. A comparison with a five-sample windowed mean absolute error (MAE w ) showed that the squared-error formulation provided greater relative separation between the evaluated mismatch-severity conditions; for example, the severe-to-mild median ratio was 11.14 for EMC and 3.52 for MAE w . The results support EMC as a compact supervisory mismatch-awareness signal within the evaluated simulation, while the observed distributions remain scenario-dependent. The framework does not implement closed-loop load scheduling, battery dispatch, grid-support control, or experimental hardware validation.

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BibTeX
@article{mendez2026simulationbased,
  title = {{Simulation-Based Supervisory Monitoring of Photovoltaic Mismatch in a Hybrid Greenhouse Microgrid}},
  author = {Victor Eliasib Perez Mendez and Jérôme Bosche and David Lara Alabazares and Abdelhamid Rabhi and Maria Teresa Meza Caiceros},
  journal = {Green Energy and Environmental Technology},
  year = {2026},
  month = oct,
  doi = {10.5772/geet.20260118},
  url = {https://u-picardie.hal.science/hal-05848147v1},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Simulation-Based Supervisory Monitoring of Photovoltaic Mismatch in a Hybrid Greenhouse Microgrid
AU  - Victor Eliasib Perez Mendez
AU  - Jérôme Bosche
AU  - David Lara Alabazares
AU  - Abdelhamid Rabhi
AU  - Maria Teresa Meza Caiceros
PY  - 2026
DA  - 2026/10/29
JO  - Green Energy and Environmental Technology
DO  - 10.5772/geet.20260118
UR  - https://u-picardie.hal.science/hal-05848147v1
AB  - Hybrid greenhouse microgrids require reliable supervisory information because photovoltaic generation can be degraded by partial shading while critical agricultural loads remain dependent on the available renewable supply. This work presents a MATLAB/Simulink system-level simulation framework for a hybrid greenhouse microgrid integrating photovoltaic generation, wind generation, battery storage, grid support, and representative greenhouse loads. A photovoltaic mismatch-awareness layer based on the EMC indicator, corresponding mathematically to the mean squared error, compares synchronized nominal and disturbed photovoltaic power trajectories using a five-sample moving window. The quantitative evaluation includes an explicit 24-h no-shading baseline together with mild, moderate, and severe imposed shading conditions. The median EMC increased from 6.77×10 3 W 2 under nominal operation to 1.62×10 4 , 8.01×10 4 , and 1.81×10 5 W 2 under mild, moderate, and severe shading, respectively. A comparison with a five-sample windowed mean absolute error (MAE w ) showed that the squared-error formulation provided greater relative separation between the evaluated mismatch-severity conditions; for example, the severe-to-mild median ratio was 11.14 for EMC and 3.52 for MAE w . The results support EMC as a compact supervisory mismatch-awareness signal within the evaluated simulation, while the observed distributions remain scenario-dependent. The framework does not implement closed-loop load scheduling, battery dispatch, grid-support control, or experimental hardware validation.
SN  - 2754-6314
SN  - 2754-6314
ER  -