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Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.ORCID iD: 0000-0001-6292-0695
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics.ORCID iD: 0000-0003-4887-9547
Arup, 13 Fitzroy St, London W1T 4BQ, England.;Univ Cambridge, Ctr Sustainable Rd Freight, Dept Engn, Trumpington St, Cambridge CB2 1PZ, England..
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2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 89, p. 61-71Article, review/survey (Refereed) Published
Abstract [en]

Photovoltaics (PV) and electric vehicles (EVs) are promising technologies for increasing energy efficiency and the share of renewable energy sources in power and transport systems. As regards the deployment, use and system integration of these technologies, spatio-temporal modeling of PV power production and EV charging is of importance for several purposes such as urban planning and power grid design and operation. There is an abundance of studies and reviews on modeling of PV power production and EV charging available in the literature. However, there is a lack of studies that review the opportunities for combined modeling of the power consumption and production associated with these technologies. This paper aims to fill this research gap by presenting a review of previous research regarding modeling of spatio-temporal PV power production and charging load of EVs. The paper provides a summary of previous work in both fields and the combination of the fields. Finally, research gaps that need to be further explored are identified. This survey revealed some research gaps that need to be further addressed. Improving the accuracy of PV power production ramp-rate modeling in addition to quantifying the aggregate clear-sky index on city-scale are two priorities for the PV potential studies. For the EV charging load models, differences in model assumptions, such as charging locations, charging powers and charging profiles, need to be studied more extensively. Moreover, there is an imminent need for metering the load of charging stations. This is essential in developing accurate models and time series forecasting techniques. For studies exploring both the PV and EV impacts, local weak points in a spatial network need to be discovered, especially for the city-scale studies. Cooperation between eminent researchers in the PV and EV fields might propagate state-of-the-art models from the separate fields to the combined studies.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2018. Vol. 89, p. 61-71
Keywords [en]
Photovoltaics power production models, Electric vehicle charging models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-357484DOI: 10.1016/j.rser.2018.02.034ISI: 000430853300007OAI: oai:DiVA.org:uu-357484DiVA, id: diva2:1241300
Funder
Swedish Energy Agency, 40864-1Swedish Energy Agency, 40199-1EU, European Research Council, 004525-2015Available from: 2018-08-23 Created: 2018-08-23 Last updated: 2018-09-03Bibliographically approved
In thesis
1. Modeling and forecasting the load in the future electricity grid: Spatial electric vehicle load modeling and residential load forecasting
Open this publication in new window or tab >>Modeling and forecasting the load in the future electricity grid: Spatial electric vehicle load modeling and residential load forecasting
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The energy system is being transitioned to increase sustainability. This transition has been accelerated by the increased awareness about the adverse effects of the greenhouse gas (GHG) emissions into the atmosphere. The transition includes switching to electricity as the energy carrier in some sectors, e.g., transportation, increasing the contribution of renewable energy sources (RES) to the grid, and digitalizing the grid services.

Electric vehicles (EVs) are promoted and subsidized in many countries among the sustainability initiatives. Consequently, the global sales of EVs rapidly increased in the recent years. Many EV owners might charge their EVs only at home, thereby increasing the residential load. The residential load might further increase due to the initiatives to electrify the heating/cooling sector.

This thesis contributes to the knowledge about the operation of the future energy system by modeling the spatial charging load of private EVs in cities, and by proposing a forecasting model to predict the residential load. Both models can be used to evaluate the impacts of both technologies on the local electricity grid. In addition, demand response (DR) schemes can be proposed to reduce the adverse effects of both the charging load of EVs and the residential load.

A case study of the EV model on the Herrljunga city grid showed that 100% EV penetration with 3.7 kW (charging rate of 14.8 km/h) chargers will not cause voltage violations in the grid. Winter load is responsible for 5% voltage drop at the weakest bus, and EVs add only 1% to this drop. In a Swedish city, charging EVs will require adding extra 1.43 kW/car to the grid capacity—assuming 22 kW (charging rate of 88 km/h) residential chargers. If the EV charging is not restricted to residential locations, an increase of 1.23 kW/car is expected.

The proposed forecasting model is comparable in accuracy to previously developed models. As an advantage, the model produces a probability density function (PDF) describing the model’s certainty in the forecast. In contrast, many previous contributions provided only point forecasts.

Place, publisher, year, edition, pages
Uppsala universitet, 2018. p. 57
Keywords
Electric Vehicles, GIS, Photovoltaics, Residential Load Forecasting
National Category
Transport Systems and Logistics Infrastructure Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:uu:diva-359432 (URN)
Presentation
2018-10-22, Polhemsalen, Ångström laboratoriet, Lägerhyddsv. 1, 752 37, Uppsala, Sweden, 13:00 (English)
Opponent
Supervisors
Available from: 2018-09-27 Created: 2018-09-02 Last updated: 2018-09-27Bibliographically approved

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Shepero, MahmoudMunkhammar, JoakimWidén, Joakim

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