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Probabilistic load flow analysis of electric vehicle smart charging in unbalanced LV distribution systems with residential photovoltaic generation
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-0226-1282
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-4191-3570
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-0051-4098
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment. (Built Environment Energy Systems Group)ORCID iD: 0000-0003-4887-9547
2021 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 72, p. 103043-, article id 103043Article in journal (Refereed) Published
Abstract [en]

Several studies have presented electric vehicle smart charging schemes to increase the temporal matching between photovoltaic generation and electric vehicle charging, including a smart charging scheme with an objective to minimize the net-load variance. This method has proved, through simulations, that the self consumption could be increased, but the benefit of the approach has not been tested on a low voltage distribution system. To increase the quality of grid impact analyses of the smart charging scheme, probabilistic methods that include input and spatial allocation uncertainties are more appropriate. In this study, a probabilistic load flow analysis is performed by modelling the variability of electric vehicle mobility, household load, photovoltaic system generation, and the adoption of photovoltaic system and electric vehicle in society. The results show that the smart charging scheme improves the low voltage distribution system performance and increases the correlations between network nodes. It is also shown that concentrated allocation has more severe impacts, in particular at lower penetration levels. This paper can form the basis for the development of probabilistic impact analysis of smart charging to allow society to integrate more electric vehicles and photovoltaic systems for a more sustainable future.

Place, publisher, year, edition, pages
Elsevier, 2021. Vol. 72, p. 103043-, article id 103043
Keywords [en]
probabilistic load flow, smart charging, electric vehicle, unbalanced residential grid
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Energy Systems
Research subject
Engineering Science with specialization in Civil Engineering and Built Environment
Identifiers
URN: urn:nbn:se:uu:diva-417548DOI: 10.1016/j.scs.2021.103043ISI: 000697355000003OAI: oai:DiVA.org:uu-417548DiVA, id: diva2:1459597
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage, FPS8Available from: 2020-08-20 Created: 2020-08-20 Last updated: 2023-08-16Bibliographically approved
In thesis
1. Uncertainty and correlation modeling for load flow analysis of future electricity distribution systems: Probabilistic modeling of low voltage networks with residential photovoltaic generation and electric vehicle charging
Open this publication in new window or tab >>Uncertainty and correlation modeling for load flow analysis of future electricity distribution systems: Probabilistic modeling of low voltage networks with residential photovoltaic generation and electric vehicle charging
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The penetration of photovoltaic (PV) and electric vehicles (EVs) continues to grow and is predicted to claim a vital share of the future energy mix. It poses new challenges in the built environment, as both PV systems and EVs are widely dispersed in the electricity distribution system. One of the vital tools for analyzing these challenges is load flow analysis, which provides insights on power system performance. Traditionally, for simplicity, load flow analysis utilizes deterministic approaches and neglecting  correlation between units in the system. However, the growth of distributed PV systems and EVs increases the uncertainties and correlations in the power system and, hence, probabilistic methods are more appropriate.

This thesis contributes to the knowledge of how uncertainty and correlation models can improve the quality of load flow analysis for electricity distribution systems with large numbers of residential PV systems and EVs. The thesis starts with an introduction to probabilistic load flow analysis of future electricity distribution systems. Uncertainties and correlation models are explained, as well as two energy management system strategies: EV smart charging and PV curtailment. The probabilistic impact of these energy management systems in the electricity distribution system has been assessed through a comparison of allocation methods and correlation analysis of the two technologies.

The results indicate that these energy management system schemes improve the electricity distribution system performance. Furthermore, an increase in correlations between nodes is also observed due to these schemes. The results also indicate that the concentrated allocation has more severe impacts, in particular at lower penetration levels. Combined PV-EV hosting capacity assessment shows that a combination of EV smart charging with PV curtailment in all buildings can further improve the voltage profile and increase the hosting capacity.  The smart charging scheme also increased the PV hosting capacity slightly. The slight correlation between PV and EV hosting capacity shows that combined hosting capacity analysis of PV systems and EVs is beneficial and is suggested to be done in one framework. Overall, this thesis concludes that an improvement of uncertainty and correlation modeling is vital in probabilistic load flow analysis of future electricity distribution systems.

Place, publisher, year, edition, pages
Uppsala: Department of Civil and Industrial Engineering, 2021. p. 45
Keywords
Probabilistic load flow, Electricity distribution systems, Uncertainty model, Correlation model, Photovoltaics, Electric vehicle, Residential buildings, Hosting capacity
National Category
Energy Systems Civil Engineering Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Civil Engineering and Built Environment
Identifiers
urn:nbn:se:uu:diva-434951 (URN)
Presentation
2021-04-09, 4001, Ångström laboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
Swedish Energy Agency, FPS8
Available from: 2021-03-05 Created: 2021-02-20 Last updated: 2021-03-22Bibliographically approved
2. Uncertainty modeling for load flow and hosting capacity analysis of urban electricity distribution systems
Open this publication in new window or tab >>Uncertainty modeling for load flow and hosting capacity analysis of urban electricity distribution systems
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Urban demographics are changing, with more than half of the global population currently residing in urban areas. Traditionally, cities are often seen as passive energy consumers relying on external centralized systems. Motivated by the need to mitigate climate change, a shift is underway as cities actively shape energy systems. This shift involves decentralized power generation, electric vehicle (EV)-related electricity usage shifts, enhanced building energy efficiency, and increasing interaction between local power generation and load. This poses some challenges to distribution grid operation such as voltage violation, decreased power quality, equipment damage, power losses, and reliability issues. Addressing these issues requires load flow analysis, and to quantify the impacts based on load flow analysis, the hosting capacity concept has been introduced. Although traditional load flow analysis lacks uncertainty consideration, the growth of distributed photovoltaics (PV) generation and EVs demands enhanced accuracy through uncertainty modeling.

This thesis contributes to the knowledge of how uncertainty and correlation models can improve the quality of load flow and hosting capacity analysis for urban electricity distribution systems with high penetration of residential PV systems and EVs through the combination of methodological and case studies. Methodological studies propose uncertainty models for input variables and investigate their impact on load flow and hosting capacity assessment. Case studies demonstrate enhanced hosting capacity analysis quality through applied uncertainty models.

Results show that concentrated allocation of PV systems and EVs had more severe impacts, in particular at lower penetration levels, and smart charging in concentrated allocation had more significant benefits to reduce peak load and voltage drop. Results regarding residential building roofs show that the inclusion of more residential buildings when the PV penetration increases will require including a lot of less-optimal facets, and, hence, a novel method has been proposed to proportionally include less optimal roofs at every penetration level. The smart charging scheme, which has as its main objective to reduce the net-load variability, improves the electricity distribution system performance, and combined with PV curtailment, can further increase the hosting capacity. An increase in correlations between nodes is also observed due to this smart charging scheme. The city-level simulations show that the distribution system of the city can accommodate a 90% penetration level of PV with less than 1% risk of overvoltage and line loading does not limit the hosting capacity. The method used to model roof facet orientation proves effective for city level applications, given its simplicity and effectiveness.

In summary, this thesis concludes that the quality and knowledge of load flow and hosting capacity analysis for urban electricity distribution systems can be improved by several methods, including: the probabilistic model of PV power generation and EV charging profiles, the inclusion of EMS, the consideration of spatial allocation methods of PV and EV, the assessment of the correlation between PV and EV, and the consideration of rooftop tilt and azimuth uncertainties.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2023. p. 90
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 2296
Keywords
Uncertainty model, Photovoltaics, Electric vehicle, Residential buildings, Hosting capacity, Probabilistic load flow, Electricity distribution systems, Urban Energy Systems
National Category
Energy Systems Energy Engineering
Research subject
Engineering Science with specialization in Civil Engineering and Built Environment
Identifiers
urn:nbn:se:uu:diva-509205 (URN)978-91-513-1876-9 (ISBN)
Public defence
2023-10-06, Heinz-Otto Kreiss, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage, FPS8
Available from: 2023-09-13 Created: 2023-08-16 Last updated: 2023-09-13

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Ramadhani, Umar HanifFachrizal, RezaMunkhammar, JoakimWidén, Joakim

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