A comparison of power plant energy flow prediction using real data applied to linear programming and if-else algorithms

In this paper, the energy flow prediction of a grid-connected Hybrid Microgrid (HMG) is studied. The studied system consists of a 19 kW photovoltaic (PV) array, a 6.5 kW wind turbine (WT), and a 59.32 kWh battery Energy Storage System (ESS). Two energy prediction and optimization algorithms, Linear...

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Bibliographic Details
Published inEnergy reports Vol. 9; pp. 294 - 300
Main Authors Ibrahim, Chaker, Reboul, Jean-Michel, Houivet, David, Gualous, Hamid, Barkad, Hassan Ali
Format Journal Article
LanguageEnglish
Published Elsevier 01.11.2023
Subjects
Energy management systems
Engineering Sciences
Hybrid microgrids
Linear programming (LP)
Renewable energy
Storage systems
Hybrid microgrids Storage systems Energy management systems Renewable energy Linear programming (LP
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ISSN2352-4847
2352-4847
DOI10.1016/j.egyr.2023.09.169

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Summary:In this paper, the energy flow prediction of a grid-connected Hybrid Microgrid (HMG) is studied. The studied system consists of a 19 kW photovoltaic (PV) array, a 6.5 kW wind turbine (WT), and a 59.32 kWh battery Energy Storage System (ESS). Two energy prediction and optimization algorithms, Linear Programming and If-Else (LP-If-Else), are applied to analyze the site's energy flow behavior and accurately predict it one day in advance. Meteorological data collected at the site and real load profiles are used in this study. To highlight the effectiveness of the offline prediction approach, three extreme cases are applied to three energy management strategies and compared. The results of the prediction performed by the two algorithms showed that a single prediction algorithm (LP) is not sufficient to accurately predict the energy flow for the next day.
ISSN:2352-4847
2352-4847
DOI:10.1016/j.egyr.2023.09.169