Parameter Optimization Model Photovoltaic Battery System for Charging Electric Cars

Peter Tauš, Marcela Taušová, Peter Sivák, Mária Shejbalová Muchová and Eva Mihaliková
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Peter Tauš: Institute of Earth Sources, Faculty of Mining, Ecology, Process Technologies and Geotechnology, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia
Marcela Taušová: Institute of Earth Sources, Faculty of Mining, Ecology, Process Technologies and Geotechnology, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia
Peter Sivák: Institute of Earth Sources, Faculty of Mining, Ecology, Process Technologies and Geotechnology, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia
Mária Shejbalová Muchová: Institute of Earth Sources, Faculty of Mining, Ecology, Process Technologies and Geotechnology, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia
Eva Mihaliková: Department of Economics and Management of Public Administration, Faculty of Public Administration, Pavol Jozef Šafárik University in Košice, Popradská 66, 041 32 Košice, Slovakia

Energies, 2020, vol. 13, issue 17, 1-17

Abstract: Sales of electric cars and vehicles (EVs) have recently been showing a rapidly growing trend. In connection with rising electricity prices as well as social pressure on the environmental impacts of electromobility, there is also increasing interest of EV owners in the ecological source of electricity. The largest group of owners of EVs are residents of family houses, so, logically, they focus their attention on the possibility of using photovoltaic (PV) charging systems for EV charging. The design of the PV system for supporting EV charging is problematic due to several input parameters in the calculation of energy needs and due to the inconsistencies of electricity generation with normal electric vehicle (EV) charging time. While the PV system produces electricity during the day, family homeowners require charging EVs mainly at night. This requires batteries as part of a PV system. The optimal design of the PV of the battery system must take into account the real consumption of EV, the average daily distance traveled, the location, the weather bridging time, and, last but not least, the investor’s financial situation. The timing mismatch of electricity needs and generation may result in the oversizing or sub-scaling of the PV system depending on the time period for which the investor claims full coverage. With an average daily EV consumption of 10 kWh/day, the overproduction of electricity may be at 8620 kWh per year if it is required to fully cover PV systems in January. Conversely, for the installation of PVs for full coverage in August, the year-round electricity deficit will be 1500 kWh per year. For the analyzed geographical conditions, i.e., Latitude 48.8, the optimum performance of PV system for one-day electricity storage is 3.585 kW. This corresponds to the full coverage of EV consumption in March, the price of the whole system varies from EUR 9000 to EUR 20,000 depending on the type of battery. In addition to the battery price, the required accumulation time for electricity to overcome adverse weather increases the required performance of a photovoltaic system (PVS), which again results in system overshooting and financial loss by not using the generated electricity. This cycle of interdependencies is usually very difficult to adjust optimally. In the contribution, we analyzed the mutual relationships of calculating the performance of a PVS according to the daily consumption of EV and required time of overcoming adverse weather. The input data for the analyses were normal average EV consumption and the number of daily km traveled from 10 to 100 km/day scaled to 10. The optimization process consisted of determining the necessary performance of the PVS and its production in the event of a requirement to ensure full energy demand in each month. In addition, different types of batteries that influence the investment price enter into optimization analyses. This depends on the energy density of a given battery, the depth of discharge, capacity, and type. The result of this research is a computational model for determining a new indicator—we called it the monthly deviation factor. This indicates the degree of oversizing or undersizing of the PV system in relation to the stated factors.

Keywords: photovoltaic systems; electric cars charging; battery system; economy; renewable energy sources; energy efficiency (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 (1)

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