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Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis

Author

Listed:
  • Gabriel Henrique Danielsson

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Leonardo Nogueira Fontoura da Silva

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Joelson Lopes da Paixão

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Alzenira da Rosa Abaide

    (Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, Brazil)

  • Nelson Knak Neto

    (Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Rio Grande do Sul, Brazil)

Abstract

The article presents the development of a Rules-Based Energy Management System for a nanogrid that serves an electric vehicle charging station. This nanogrid is composed of photovoltaic generation, a wind turbine, a battery energy storage system, and a fast electric vehicle charger. The objective is to prioritize the use of renewable energy sources, reducing costs and promoting energy efficiency. The methodology includes forecasting models based on an Artificial Neural Network for photovoltaic generation, a parametric estimation for wind generation, and a Monte Carlo simulation to predict the energy consumption of electric vehicles. The developed algorithm makes decisions every 15 min, considering variables such as energy tariff, battery state of charge, renewable generation forecast, and energy consumption forecast. The results showed that the system adequately balances energy generation, consumption, and storage, even under forecasting uncertainties. The use of the Monte Carlo simulation was crucial for evaluating the financial impacts of forecast errors, enabling robust decision-making. This energy management system proved to be effective and sustainable for nanogrids dedicated to electric vehicle charging, with the potential to reduce operational costs and increase energy reliability and the use of renewable energy sources.

Suggested Citation

  • Gabriel Henrique Danielsson & Leonardo Nogueira Fontoura da Silva & Joelson Lopes da Paixão & Alzenira da Rosa Abaide & Nelson Knak Neto, 2024. "Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis," Energies, MDPI, vol. 18(1), pages 1-21, December.
    • Handle: RePEc:gam:jeners:v:18:y:2024:i:1:p:26-:d:1552967
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    References listed on IDEAS

    as
    1. Zhaoxuan Li & SM Mahbobur Rahman & Rolando Vega & Bing Dong, 2016. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting," Energies, MDPI, vol. 9(1), pages 1-12, January.
    2. Ziad M. Ali & Martin Calasan & Shady H. E. Abdel Aleem & Francisco Jurado & Foad H. Gandoman, 2023. "Applications of Energy Storage Systems in Enhancing Energy Management and Access in Microgrids: A Review," Energies, MDPI, vol. 16(16), pages 1-41, August.
    3. Saugat Upadhyay & Ibrahim Ahmed & Lucian Mihet-Popa, 2024. "Energy Management System for an Industrial Microgrid Using Optimization Algorithms-Based Reinforcement Learning Technique," Energies, MDPI, vol. 17(16), pages 1-18, August.
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