Forecast-Based Energy Management for Optimal Energy Dispatch in a Microgrid
This article describes the development of an optimal and predictive energy management system (EMS) for a microgrid with a high photovoltaic (PV) power contribution. The EMS utilizes a predictive long-short-term memory (LSTM) neural network trained on real PV power and consumption data. Optimal EMS d...
| Main Authors: | , |
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| Format: | ARTÍCULO |
| Language: | es_ES |
| Published: |
2024
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| Subjects: | |
| Online Access: | http://dspace.ucuenca.edu.ec/handle/123456789/44241 https://www.scopus.com/record/display.uri?eid=2-s2.0-85183321555&doi=10.3390%2fen17020486&origin=inward&txGid=ba7951cac78e82c760eeb6bdf5ef6fa7 |
| Summary: | This article describes the development of an optimal and predictive energy management system (EMS) for a microgrid with a high photovoltaic (PV) power contribution. The EMS utilizes a predictive long-short-term memory (LSTM) neural network trained on real PV power and consumption data. Optimal EMS decisions focus on managing the state of charge (SoC) of the battery energy storage system (BESS) within defined limits and determining the optimal power contributions from the microgrid components. The simulation utilizes MATLAB R2023a to solve a mixed-integer optimization problem and HOMER Pro 3.14 to simulate the microgrid. The EMS solves this optimization problem for the current sampling time (Formula presented.) and the immediate sampling time (Formula presented.), which implies a prediction of one hour in advance. An upper-layer decision algorithm determines the operating state of the BESS, that is, to charge or discharge the batteries. An economic and technical impact analysis of our approach compared to two EMSs based on a pure economic optimization approach and a peak-shaving algorithm reveals superior BESS integration, achieving 59% in demand satisfaction without compromising the life of the equipment, avoiding inexpedient power delivery, and preventing significant increases in operating costs. |
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