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Forecast-Based Energy Management for Optimal Energy Dispatch in a Microgrid

Author

Listed:
  • Francisco Durán

    (Department of Electrical Engineering, Electronics, and Telecommunications, Universidad de Cuenca, Avenue 12 de Abril, Cuenca 010101, Ecuador)

  • Wilson Pavón

    (Engineering Department, Universidad Politécnica Salesiana, Quito 170146, Ecuador)

  • Luis Ismael Minchala

    (Department of Electrical Engineering, Electronics, and Telecommunications, Universidad de Cuenca, Avenue 12 de Abril, Cuenca 010101, Ecuador)

Abstract

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 ( t ) and the immediate sampling time ( t + 1 ) , 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.

Suggested Citation

  • Francisco Durán & Wilson Pavón & Luis Ismael Minchala, 2024. "Forecast-Based Energy Management for Optimal Energy Dispatch in a Microgrid," Energies, MDPI, vol. 17(2), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:486-:d:1322053
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    References listed on IDEAS

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    1. Wilson Pavon & Esteban Inga & Silvio Simani & Maddalena Nonato, 2021. "A Review on Optimal Control for the Smart Grid Electrical Substation Enhancing Transition Stability," Energies, MDPI, vol. 14(24), pages 1-15, December.
    2. Muhammad Faizan Tahir & Haoyong Chen & Muhammad Sufyan Javed & Irfan Jameel & Asad Khan & Saifullah Adnan, 2019. "Integration of Different Individual Heating Scenarios and Energy Storages into Hybrid Energy System Model of China for 2030," Energies, MDPI, vol. 12(11), pages 1-20, May.
    3. Rana, Md Masud & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Uddin, Moslem & Sarkar, Md Rasel, 2021. "A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system," Energy, Elsevier, vol. 234(C).
    4. Uddin, Moslem & Romlie, M.F. & Abdullah, M.F. & Tan, ChiaKwang & Shafiullah, GM & Bakar, A.H.A., 2020. "A novel peak shaving algorithm for islanded microgrid using battery energy storage system," Energy, Elsevier, vol. 196(C).
    5. Kim, H.J. & Kim, M.K., 2023. "A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid," Applied Energy, Elsevier, vol. 332(C).
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    Cited by:

    1. E. Poongulali & K. Selvaraj, 2024. "Improved load demand prediction for cluster microgrids using modified temporal convolutional feed forward network," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 87(3), pages 561-574, November.

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