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Uncertainty modeling for load flow and hosting capacity analysis of urban electricity distribution systems
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.ORCID iD: 0000-0003-0226-1282
2023 (English)Doctoral thesis, comprehensive summary (Other academic)
Description
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 [en]
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: urn:nbn:se:uu:diva-509205ISBN: 978-91-513-1876-9 (print)OAI: oai:DiVA.org:uu-509205DiVA, id: diva2:1788729
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, FPS8Available from: 2023-09-13 Created: 2023-08-16 Last updated: 2023-09-13
List of papers
1. Review of probabilistic load flow approaches for power distribution systems with photovoltaic generation and electric vehicle charging
Open this publication in new window or tab >>Review of probabilistic load flow approaches for power distribution systems with photovoltaic generation and electric vehicle charging
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2020 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 120, article id 106003Article, review/survey (Refereed) Published
Abstract [en]

The currently increasing penetration of photovoltaic (PV) generation and electric vehicle (EV) charging in electricity distribution grids leads to higher system uncertainties. This makes it vital for load flow analyses to use probabilistic methods that take into account the uncertainty in both load and generation. Such probabilistic load flow (PLF) approaches typically involve three main components: (1) probability distribution models, (2) correlation models, and (3) PLF computations. In this review, state-of-the-art approaches to each of these components are discussed comprehensively, including suggestions of preferred modelling methods specifically for distribution systems with PV generation and EV charging. Research gaps that need to be explored are also identified. For further development of PLF analysis, improving input distribution modelling to be more physically realistic for load, PV generation, and EV charging is vital. Further correlation modelling efforts should focus on developing an effective spatio-temporal correlation model that is able to cope with high-dimensional inputs. The computational speed of PLF analysis needs to be improved to accommodate more complex distribution system models, and time-series approaches should be developed to meet operational needs. Furthermore, collection of higher-quality data is crucial for PLF studies, especially for improving the accuracy in the input variables.

Keywords
Probabilistic load flow, Probabilistic uncertainty modelling, Correlation modelling, Power distribution system, PV generation, EV charging
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Engineering Science with specialization in Civil Engineering and Built Environment
Identifiers
urn:nbn:se:uu:diva-407895 (URN)10.1016/j.ijepes.2020.106003 (DOI)000526402600066 ()
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy StorageSwedish Energy AgencyVattenfall ABStandUp
Available from: 2020-03-31 Created: 2020-03-31 Last updated: 2023-08-16Bibliographically approved
2. Probabilistic load flow analysis of electric vehicle smart charging in unbalanced LV distribution systems with residential photovoltaic generation
Open this publication in new window or tab >>Probabilistic load flow analysis of electric vehicle smart charging in unbalanced LV distribution systems with residential photovoltaic generation
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
Keywords
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:nbn:se:uu:diva-417548 (URN)10.1016/j.scs.2021.103043 (DOI)000697355000003 ()
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage, FPS8
Available from: 2020-08-20 Created: 2020-08-20 Last updated: 2023-08-16Bibliographically approved
3. Combined PV-EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment
Open this publication in new window or tab >>Combined PV-EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment
2021 (English)In: Sustainable Energy, Grids and Networks, E-ISSN 2352-4677, Vol. 26, article id 100445Article in journal (Refereed) Published
Abstract [en]

Photovoltaic (PV) systems and electric vehicles (EVs) integrated in local distribution systems are considered to be two of the keys to a sustainable future built environment. However, large-scale integration of PV generation and EV charging loads poses technical challenges for the distribution grid. Each grid has a specific hosting capacity limiting the allowable PV and EV share. This paper presents a combined PV-EV grid integration and hosting capacity assessment for a residential LV distribution grid with four different energy management system (EMS) scenarios: (1) without EMS, (2) with EV smart charging only, (3) with PV curtailment only, and (4) with both EV smart charging and PV curtailment. The combined PV-EV hosting capacity is presented using a novel graphical approach so that both PV and EV hosting capacity can be analyzed within the same framework. Results show that the EV smart charging can improve the hosting capacity for EVs significantly and for PV slightly. While the PV curtailment can improve the hosting capacity for PV significantly, it cannot improve the hosting capacity for EVs at all. From the graphical analysis, it can be concluded that there is a slight positive correlation between PV and EV hosting capacity in the case of residential areas.

Keywords
Photovoltaic systems, Electric vehicle charging, Residential distribution grid, Hosting capacity, EV smart charging, PV curtailment
National Category
Energy Systems Energy Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering Infrastructure Engineering
Research subject
Engineering Science with specialization in Civil Engineering and Built Environment
Identifiers
urn:nbn:se:uu:diva-417540 (URN)10.1016/j.segan.2021.100445 (DOI)000645076400020 ()
Funder
StandUpSweGRIDS - Swedish Centre for Smart Grids and Energy StorageSwedish Energy AgencyVattenfall AB
Available from: 2020-08-20 Created: 2020-08-20 Last updated: 2023-08-16Bibliographically approved
4. On the properties of residential rooftop azimuth and tilt uncertainties for photovoltaic power generation modeling and hosting capacity analysis
Open this publication in new window or tab >>On the properties of residential rooftop azimuth and tilt uncertainties for photovoltaic power generation modeling and hosting capacity analysis
2023 (English)In: Solar Energy Advances, ISSN 2667-1131, Vol. 3, article id 100036Article in journal (Refereed) Published
Abstract [en]

One of the essential epistemic uncertainties that has not yet been studied enough for distributed photovoltaic systems is the azimuth and tilt of rooftop photovoltaic panels, as previous studies of grid impacts and hosting capacity have tended to assume uniform and optimal roof facet conditions. In this study, rooftop facet orientation distributions are presented and analyzed for all single-family buildings in the Swedish city of Uppsala, based on LiDAR-based data that consist of every roof facet from the around 13,500 single-family buildings in the city. From these distributions, novel methods to proportionally include less suitable roofs for every penetration level are proposed using a simple method based on normal and uniform probability density functions, and are tested for both time-series and stochastic hosting capacity analysis. The results show that under the assumption that the best roof facets are utilized first, a uniform distribution for rooftop facet azimuth and a normal distribution for rooftop facet tilt with parameters that depend linearly on the penetration level were shown to be accurate. The hosting capacity simulations demonstrate how the proposed methods perform significantly better in estimating the photovoltaic hosting capacity than the more common simplified methods for both time-series and stochastic hosting capacity analysis. The proposed model could help distribution system operators as well as researchers in this area to model the rooftop facet orientation uncertainty better and improve the quality of aggregated photovoltaic generation models and hosting capacity analyses.

National Category
Energy Engineering
Identifiers
urn:nbn:se:uu:diva-496720 (URN)10.1016/j.seja.2023.100036 (DOI)
Funder
SOLVE
Available from: 2023-02-20 Created: 2023-02-20 Last updated: 2023-08-16Bibliographically approved
5. A city-level assessment of residential PV hosting capacity for low-voltage distribution systems considering rooftop data and uncertainties
Open this publication in new window or tab >>A city-level assessment of residential PV hosting capacity for low-voltage distribution systems considering rooftop data and uncertainties
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2024 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 371Article in journal (Refereed) Published
Abstract [en]

The increasing trend of small-scale residential photovoltaic (PV) system installation in low-voltage (LV) distribution networks poses challenges for power grids. To quantify these impacts, hosting capacity has become a popular framework for analysis. However, previous studies have mostly focused on small-scale or test feeders and overlooked uncertainties related to rooftop azimuth and tilt. This paper presents a comprehensive evaluation of city-level PV hosting capacity using data from over 300 real LV systems in Varberg, Sweden. A previously developed rooftop azimuth and tilt model is also applied and evaluated. The findings indicate that the distribution systems of the city, with a definition of PV penetration as the percentage of houses with 12 kW installed PV systems, can accommodate up to 90\% PV penetration with less than 1\% risk of overvoltage, and line loading is not a limiting factor. The roof facet orientation modeling proves to be suitable for city-level applications due to its simplicity and effectiveness. Sensitivity studies reveal that PV size assumptions significantly influence hosting capacity analysis. The study provides valuable insights for planning strategies to increase PV penetration in residential buildings and offers technical input for regulators and grid operators to facilitate and manage residential PV systems.

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
PV hosting capacity, Low voltage system, Rooftop solar photovoltaic, Uncertainty modeling
National Category
Energy Systems Energy Engineering
Research subject
Engineering Science with specialization in Civil Engineering and Built Environment
Identifiers
urn:nbn:se:uu:diva-509203 (URN)10.1016/j.apenergy.2024.123715 (DOI)001260532400001 ()
Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage, FPS8SOLVE
Available from: 2023-08-16 Created: 2023-08-16 Last updated: 2024-07-12Bibliographically approved

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Ramadhani, Umar Hanif

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Citation style
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Output format
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