Probability Calculation for Utilization of Photovoltaic Energy in Electric Vehicle Charging Stations

Pavol Belany (), Peter Hrabovsky and Zuzana Florkova
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Pavol Belany: Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia
Peter Hrabovsky: Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia
Zuzana Florkova: Research Centre, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia

Energies, 2024, vol. 17, issue 5, 1-34

Abstract: In recent years, there has been a growing emphasis on the efficient utilization of natural resources across various facets of life. One such area of focus is transportation, particularly electric mobility in conjunction with the deployment of renewable energy sources. To fully realize this objective, it is crucial to quantify the probability of achieving the desired state—production exceeding consumption. This article deals with the computation of the probability that the energy required to charge an electric vehicle will originate from a renewable source at a specific time and for a predetermined charging duration. The base of the model lies in artificial neural networks, which serve as an ancillary tool for the actual probability assessment. Neural networks are used to forecast the values of energy production and consumption. Following the processing of these data, the probability of energy availability for a given day and month is determined. A total of seven scenarios are calculated, representing individual days of the week. These findings can help users in their decision-making process regarding when and for how long to connect their electric vehicle to a charging station to receive assured clean energy from a local photovoltaic source.

Keywords: electric vehicle charging; photovoltaic power plant; neural network models; charging station consumption; probability trends; user behavior analysis; prediction; probability (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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