Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/1/26 |
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| author | Gabriel Henrique Danielsson Leonardo Nogueira Fontoura da Silva Joelson Lopes da Paixão Alzenira da Rosa Abaide Nelson Knak Neto |
| author_facet | Gabriel Henrique Danielsson Leonardo Nogueira Fontoura da Silva Joelson Lopes da Paixão Alzenira da Rosa Abaide Nelson Knak Neto |
| author_sort | Gabriel Henrique Danielsson |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-090dc44ee18d476b8b6f7f9f414ef4c0 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-090dc44ee18d476b8b6f7f9f414ef4c02025-01-10T13:16:51ZengMDPI AGEnergies1996-10732024-12-011812610.3390/en18010026Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic AnalysisGabriel Henrique Danielsson0Leonardo Nogueira Fontoura da Silva1Joelson Lopes da Paixão2Alzenira da Rosa Abaide3Nelson Knak Neto4Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, BrazilGraduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, BrazilGraduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, BrazilGraduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Rio Grande do Sul, BrazilAcademic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Rio Grande do Sul, BrazilThe 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.https://www.mdpi.com/1996-1073/18/1/26electric vehicleenergy forecastenergy management systemfast charging stationnanogridrenewable energy |
| spellingShingle | Gabriel Henrique Danielsson Leonardo Nogueira Fontoura da Silva Joelson Lopes da Paixão Alzenira da Rosa Abaide Nelson Knak Neto Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis Energies electric vehicle energy forecast energy management system fast charging station nanogrid renewable energy |
| title | Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis |
| title_full | Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis |
| title_fullStr | Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis |
| title_full_unstemmed | Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis |
| title_short | Rules-Based Energy Management System for an EV Charging Station Nanogrid: A Stochastic Analysis |
| title_sort | rules based energy management system for an ev charging station nanogrid a stochastic analysis |
| topic | electric vehicle energy forecast energy management system fast charging station nanogrid renewable energy |
| url | https://www.mdpi.com/1996-1073/18/1/26 |
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