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Probabilistic Approach to Integrate Photovoltaic Generation into PEVs Charging Stations Considering Technical, Economic and Environmental Aspects

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  • Najmat Celene Branco

    (Faculty of Electrical and Biomedical Engineering, Federal University of Para, Belem, PA 66075-110, Brazil)

  • Carolina M. Affonso

    (Faculty of Electrical and Biomedical Engineering, Federal University of Para, Belem, PA 66075-110, Brazil)

Abstract

This paper investigates the integration of a photovoltaic system into plug-in electric vehicles charging stations in a university campus building located in Belem, Brazil, considering technical, economic, and environmental impacts in a probabilistic approach. Monte Carlo method is implemented to probabilistically estimate output variables, representing uncertainties from input data such as solar generation, vehicles demand and building load. Simulations are based on local irradiance data and electricity demand measurements collected by a local monitoring system installed in the building. The analysis comprehends a study time horizon of 10 years and evaluates transformer load and voltage level, carbon emissions avoided, and the financial feasibility of the project. Results show the connection of a PV system with penetration level of 15.6% can significantly reduce transformer overload occurrence by 69% and decrease overload duration time on average from 4 to 1 h at 10th year. PV system can reduce PEV CO 2 emission by 97.4% on average compared with internal combustion engine vehicles. From a financial perspective, the project is feasible and economically attractive with a payback time that ranges from 6 to 8 years, being an attractive solution to the Amazon region to support a cleaner energy matrix.

Suggested Citation

  • Najmat Celene Branco & Carolina M. Affonso, 2020. "Probabilistic Approach to Integrate Photovoltaic Generation into PEVs Charging Stations Considering Technical, Economic and Environmental Aspects," Energies, MDPI, vol. 13(19), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5086-:d:421426
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    References listed on IDEAS

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    1. Francesco Lo Franco & Mattia Ricco & Riccardo Mandrioli & Gabriele Grandi, 2020. "Electric Vehicle Aggregate Power Flow Prediction and Smart Charging System for Distributed Renewable Energy Self-Consumption Optimization," Energies, MDPI, vol. 13(19), pages 1-25, September.
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    4. Colmenar-Santos, Antonio & Linares-Mena, Ana-Rosa & Borge-Diez, David & Quinto-Alemany, Carlos-Domingo, 2017. "Impact assessment of electric vehicles on islands grids: A case study for Tenerife (Spain)," Energy, Elsevier, vol. 120(C), pages 385-396.
    5. Heba M. Abdullah & Rashad M. Kamel & Anas Tahir & Azzam Sleit & Adel Gastli, 2020. "The Simultaneous Impact of EV Charging and PV Inverter Reactive Power on the Hosting Distribution System’s Performance: A Case Study in Kuwait," Energies, MDPI, vol. 13(17), pages 1-22, August.
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    Cited by:

    1. Lijuan Sun & Menggang Chen & Yawei Shi & Lifeng Zheng & Songyang Li & Jun Li & Huijuan Xu, 2022. "Solving PEV Charging Strategies with an Asynchronous Distributed Generalized Nash Game Algorithm in Energy Management System," Energies, MDPI, vol. 15(24), pages 1-13, December.
    2. Ana Carolina Kulik & Édwin Augusto Tonolo & Alberto Kisner Scortegagna & Jardel Eugênio da Silva & Jair Urbanetz Junior, 2021. "Analysis of Scenarios for the Insertion of Electric Vehicles in Conjunction with a Solar Carport in the City of Curitiba, Paraná—Brazil," Energies, MDPI, vol. 14(16), pages 1-15, August.
    3. Gustavo Leite Gonçalves & Raphael Abrahão & Paulo Rotella Junior & Luiz Célio Souza Rocha, 2022. "Economic Feasibility of Conventional and Building-Integrated Photovoltaics Implementation in Brazil," Energies, MDPI, vol. 15(18), pages 1-16, September.

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