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

Najmat Celene Branco and Carolina M. Affonso
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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

Energies, 2020, vol. 13, issue 19, 1-18

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.

Keywords: probabilistic analysis; Monte Carlo; plug-in electric vehicles; economic feasibility; photovoltaic system; CO 2 emissions (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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