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

Gabriel Henrique Danielsson (), Leonardo Nogueira Fontoura da Silva, Joelson Lopes da Paixão, Alzenira da Rosa Abaide and Nelson Knak Neto
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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

Energies, 2024, vol. 18, issue 1, 1-21

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.

Keywords: electric vehicle; energy forecast; energy management system; fast charging station; nanogrid; renewable energy; rules-based; stochastic analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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