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Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement

Author

Listed:
  • Fernando V. Cerna

    (Department of Electrical Engineering, Federal University of Roraima, Boa Vista 69310-000, Brazil)

  • Mahdi Pourakbari-Kasmaei

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Luizalba S. S. Pinheiro

    (Department of Electrical Engineering, Federal University of Roraima, Boa Vista 69310-000, Brazil)

  • Ehsan Naderi

    (School of Electrical, Computer, and Biomedical Engineering, Southern Illinois University, Carbondale, IL 62901, USA)

  • Matti Lehtonen

    (Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland)

  • Javier Contreras

    (School of Industrial Engineering, University of Castilla-La Mancha, 13071 Ciudad Real, Spain)

Abstract

In prosumers’ communities, the use of storage batteries (SBs) as support for photovoltaic (PV) sources combined with coordination in household appliances usage guarantees several gains. Although these technologies increase the reliability of the electricity supply, the large-scale use of home appliances in periods of lower solar radiation and low electricity tariff can impair the performance of the electrical system. The appearance of new consumption peaks can lead to disturbances. Moreover, the repetition of these events in the short term can cause rapid fatigue of the assets. To address these concerns, this research proposes a mixed-integer linear programming (MILP) model aiming at the optimal operation of the SBs and the appliance usage of each prosumer, as well as a PV plant within a community to achieve the maximum load factor (LF) increase. Constraints related to the household appliances, including the electric vehicle (EV), shared PV plant, and the SBs, are considered. Uncertainties in consumption habits are simulated using a Monte Carlo algorithm. The proposed model was solved using the CPLEX solver. The effectiveness of our proposed model is evaluated with/without the LF improvement. Results corroborate the efficient performance of the proposed tool. Financial benefits are obtained for both prosumers and the energy company.

Suggested Citation

  • Fernando V. Cerna & Mahdi Pourakbari-Kasmaei & Luizalba S. S. Pinheiro & Ehsan Naderi & Matti Lehtonen & Javier Contreras, 2021. "Intelligent Energy Management in a Prosumer Community Considering the Load Factor Enhancement," Energies, MDPI, vol. 14(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3624-:d:577256
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    References listed on IDEAS

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    Cited by:

    1. Huangjie Gong & Dan M. Ionel, 2021. "Improving the Power Outage Resilience of Buildings with Solar PV through the Use of Battery Systems and EV Energy Storage," Energies, MDPI, vol. 14(18), pages 1-16, September.
    2. Cerna, Fernando V. & Pourakbari-Kasmaei, Mahdi & Barros, Raone G. & Naderi, Ehsan & Lehtonen, Matti & Contreras, Javier, 2023. "Optimal operating scheme of neighborhood energy storage communities to improve power grid performance in smart cities," Applied Energy, Elsevier, vol. 331(C).
    3. Mohammed Kharrich & Salah Kamel & Mohamed H. Hassan & Salah K. ElSayed & Ibrahim B. M. Taha, 2021. "An Improved Heap-Based Optimizer for Optimal Design of a Hybrid Microgrid Considering Reliability and Availability Constraints," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    4. Bartłomiej Mroczek & Paweł Pijarski, 2022. "Machine Learning in Operating of Low Voltage Future Grid," Energies, MDPI, vol. 15(15), pages 1-30, July.
    5. Cerna, Fernando V. & Dantas, Jamile T. & Naderi, Ehsan & Contreras, Javier, 2024. "Optimal strategy to reduce energy waste in an electricity distribution network through direct/indirect bulk load control," Energy, Elsevier, vol. 294(C).

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