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  • 1.
    Fachrizal, Reza
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Qian, Kun
    University of Southern Denmark.
    Lindberg, Oskar
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Shepero, Mahmoud
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Adam, Rebecca
    University of Southern Denmark.
    Widén, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Urban-scale energy matching optimization with smart EV charging and V2G in a net-zero energy city powered by wind and solar energy2024In: eTransportation, E-ISSN 2590-1168, Vol. 20, article id 100314Article in journal (Refereed)
    Abstract [en]

    Renewable energy sources (RES) and electric vehicles (EVs) are two promising technologies that are widely recognized as key components for achieving sustainable cities. However, intermittent RES generation and increased peak load due to EV charging can pose technical challenges for the power systems. Many studies have shown that improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). This paper takes a city on the west coast of Sweden as a case study. The smart charging and V2G schemes in this study aim to minimize the mismatch between generation and load and are formulated as quadratic programming problems. Results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance is increased from 68% in the opportunistic charging scenario to 73% in the smart charging scenario and to 84% in the V2G scenario. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings in this paper indicate a high potential from EV flexibility in improving urban energy system performance. 

    Download full text (pdf)
    fulltext
  • 2.
    Fachrizal, Reza
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Synergy between Photovoltaic Power Generation and Electric Vehicle Charging in Urban Energy Systems: Optimization Models for Smart Charging and Vehicle-to-Grid2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Cities are responsible for around 75% of global primary energy use and 70% of global greenhouse gas (GHG) emissions, with buildings and urban mobility being two key contributors. Actions to reduce GHG emissions have been promoted and implemented in many countries in the world. These include switching to electric vehicles (EVs) and renewable energy sources (RES), such as solar photovoltaics (PV). The transition has led to rapid increase in EV and PV adoption worldwide in the recent decades. However, large-scale integration of EVs and PV in urban energy systems poses new challenges such as increased peak loads, power mismatch, component overloading, and voltage violations. Improved synergy between EVs, PV and other loads can overcome these challenges. Coordinated charging of EVs, or so-called EV smart charging, is potentially a promising solution to improve the synergy. The synergy can be further enhanced with vehicle-to-grid (V2G) schemes, where an EV can not only charge, but also discharge power from its battery. 

    This doctoral thesis investigates the synergy between EV charging and PV power generation with the application of EV smart charging and V2G schemes. The investigation was carried out through simulation studies on the system levels of residential buildings, workplaces, distribution grid, and city-scale. Smart charging and V2G optimization models with an objective to reduce the net-load (load minus generation) variability were developed and simulated. 

    The results show that the PV-EV synergy can be improved with the proposed smart charging schemes. However, the levels of improvement depend highly on the user mobility behavior from and to the destined charging locations. PV-EV synergy is limited in residential buildings due to low EV occupancy during high solar power production, but has high potential at workplace charging stations due to high EV occupancy during the same time. In the case studies presented in this thesis, it was found that the implementation of smart charging can improve the synergy by up to around 9 percentage points in residential buildings and up to around 40 percentage points in workplaces. On a city-scale level, both optimal sizing and V2G play essential roles in improving city-scale generation-load synergy, as they can increase the load matching from 33% to 84%. The results also show that improved synergy leads to enhanced power grid performance and combined PV-EV grid hosting capacity.

    In conclusion, the thesis demonstrates that EV smart charging schemes can improve PV-EV synergy, leading to enhanced performance of urban energy systems.

    List of papers
    1. Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: a review
    Open this publication in new window or tab >>Smart charging of electric vehicles considering photovoltaic power production and electricity consumption: a review
    Show others...
    2020 (English)In: eTransportation, ISSN 2590-1168, Vol. 4, article id 100056Article, review/survey (Refereed) Published
    Abstract [en]

    Photovoltaics (PV) and electric vehicles (EVs) are two emerging technologies often considered as cornerstones in the energy and transportation systems of future sustainable cities. They both have to be integrated into the power systems and be operated together with already existing loads and generators and, often, into buildings, where they potentially impact the overall energy performance of the buildings. Thus, a high penetration of both PV and EVs poses new challenges. Understanding of the synergies between PV, EVs and existing electricity consumption is therefore required. Recent research has shown that smart charging of EVs could improve the synergy between PV, EVs and electricity consumption, leading to both technical and economic advantages. Considering the growing interest in this field, this review paper summarizes state-of-the-art studies of smart charging considering PV power production and electricity consumption. The main aspects of smart charging reviewed are objectives, configurations, algorithms and mathematical models. Various charging objectives, such as increasing PV utilization and reducing peak loads and charging cost, are reviewed in this paper. The different charging control configurations, i.e., centralized and distributed, along with various spatial configurations, e.g., houses and workplaces, are also discussed. After that, the commonly employed optimization techniques and rule-based algorithms for smart charging are reviewed. Further research should focus on finding optimal trade-offs between simplicity and performance of smart charging schemes in terms of control configuration, charging algorithms, as well as the inclusion of PV power and load forecast in order to make the schemes suitable for practical implementations.

    Place, publisher, year, edition, pages
    Elsevier, 2020
    Keywords
    Photovoltaics, electric vehicles, electricity consumption, smart charging, energy management system, charging optimization
    National Category
    Energy Systems Other Electrical Engineering, Electronic Engineering, Information Engineering Infrastructure Engineering Transport Systems and Logistics
    Research subject
    Engineering Science with specialization in Civil Engineering and Built Environment
    Identifiers
    urn:nbn:se:uu:diva-407862 (URN)10.1016/j.etran.2020.100056 (DOI)000658425300002 ()
    Available from: 2020-04-03 Created: 2020-04-03 Last updated: 2023-04-07Bibliographically approved
    2. Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles
    Open this publication in new window or tab >>Improved Photovoltaic Self-Consumption in Residential Buildings with Distributed and Centralized Smart Charging of Electric Vehicles
    2020 (English)In: Energies, E-ISSN 1996-1073, Vol. 13, no 5, article id 1153Article in journal (Refereed) Published
    Abstract [en]

    The integration of photovoltaic (PV) and electric vehicle (EV) charging in residential buildings has increased in recent years. At high latitudes, both pose new challenges to the residential power systems due to the negative correlation between household load and PV power production and the increase in household peak load by EV charging. EV smart charging schemes can be an option to overcome these challenges. This paper presents a distributed and a centralized EV smart charging scheme for residential buildings based on installed photovoltaic (PV) power output and household electricity consumption. The proposed smart charging schemes are designed to determine the optimal EV charging schedules with the objective to minimize the net load variability or to flatten the net load profile. Minimizing the net load variability implies both increasing the PV self-consumption and reducing the peak loads. The charging scheduling problems are formulated and solved with quadratic programming approaches. The departure and arrival time and the distance covered by vehicles in each trip are specifically modeled based on available statistical data from the Swedish travel survey. The schemes are applied on simulated typical Swedish detached houses without electric heating. Results show that both improved PV self-consumption and peak load reduction are achieved. The aggregation of distributed smart charging in multiple households is conducted, and the results are compared to the smart charging for a single household. On the community level, both results from distributed and centralized charging approaches are compared.

    Place, publisher, year, edition, pages
    MDPI, 2020
    Keywords
    electric vehicles, photovoltaics, electricity consumption, smart charging, self-consumption, residential buildings
    National Category
    Energy Systems Civil Engineering Infrastructure Engineering 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-406065 (URN)10.3390/en13051153 (DOI)000524318700140 ()
    Available from: 2020-03-04 Created: 2020-03-04 Last updated: 2023-08-28Bibliographically 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. Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance
    Open this publication in new window or tab >>Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance
    2022 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 307, article id 118139Article in journal (Refereed) Published
    Abstract [en]

    The integration of photovoltaic (PV) systems and electric vehicles (EVs) in the built environment, including at workplaces, has increased significantly in the recent decade and has posed new technical challenges for the power system, such as increased peak loads and component overloading. Several studies show that improved matching between PV generation and EV load through both optimal sizing and operation of PV-EV systems can minimize these challenges. This paper presents an optimal PV-EV sizing framework for workplace solar powered charging stations considering load matching performances. The proposed optimal sizing framework in this study uses a novel score, called self-consumption-sufficiency balance (SCSB), which conveys the balance between self-consumption (SC) and self-sufficiency (SS), based on a similar principle as the F1-score in machine learning. A high SCSB score implies that the system is close to being self-sufficient without exporting or curtailing a large share of local production. The results show that the SCSB performance tends to be higher with a larger combined PV-EV size. In addition to presenting PV-EV optimal sizing at the workplace charging station, this study also assesses a potential SC and SS enhancement with optimal operation through smart charging schemes. The results show that smart charging schemes can significantly improve the load matching performances by up to 42.6 and 40.8 percentage points for SC and SS, respectively. The smart charging scheme will also shift the combined optimal PV-EV sizes. Due to its simplicity and universality, the optimal sizing based on SCSB score proposed in this study can be a benchmark for future studies on optimal sizing of PV-EV system, or distributed generation-load in general.

    Place, publisher, year, edition, pages
    Elsevier, 2022
    Keywords
    Photovoltaic systems, Electric vehicle charging, Workplace charging station, Optimal sizing, Smart charging, PV self-consumption
    National Category
    Infrastructure Engineering Energy Systems Energy Engineering
    Research subject
    Engineering Science with specialization in Civil Engineering and Built Environment
    Identifiers
    urn:nbn:se:uu:diva-459125 (URN)10.1016/j.apenergy.2021.118139 (DOI)000771039600002 ()
    Projects
    SweGRIDS FPS 26 - Smart charging strategies and optimal PV-EV sizing to increase the combined PV-EV hosting capacity in the distribution grid
    Funder
    Swedish Energy AgencySweGRIDS - Swedish Centre for Smart Grids and Energy Storage, FPS26StandUp
    Available from: 2021-11-19 Created: 2021-11-19 Last updated: 2023-04-07Bibliographically approved
    5. Urban-scale energy matching optimization with smart EV charging and V2G in a net-zero energy city powered by wind and solar energy
    Open this publication in new window or tab >>Urban-scale energy matching optimization with smart EV charging and V2G in a net-zero energy city powered by wind and solar energy
    Show others...
    2024 (English)In: eTransportation, E-ISSN 2590-1168, Vol. 20, article id 100314Article in journal (Refereed) Published
    Abstract [en]

    Renewable energy sources (RES) and electric vehicles (EVs) are two promising technologies that are widely recognized as key components for achieving sustainable cities. However, intermittent RES generation and increased peak load due to EV charging can pose technical challenges for the power systems. Many studies have shown that improved load matching through energy system optimization can minimize these challenges. This paper assesses the optimal urban-scale energy matching potentials in a net-zero energy city powered by wind and solar energy, considering three EV charging scenarios: opportunistic charging, smart charging, and vehicle-to-grid (V2G). This paper takes a city on the west coast of Sweden as a case study. The smart charging and V2G schemes in this study aim to minimize the mismatch between generation and load and are formulated as quadratic programming problems. Results show that the optimal load matching performance is achieved in a net-zero energy city with the V2G scheme and a wind-PV electricity production share of 70:30. The load matching performance is increased from 68% in the opportunistic charging scenario to 73% in the smart charging scenario and to 84% in the V2G scenario. It is also shown that a 2.4 GWh EV battery participating in the V2G scheme equals 1.4 GWh stationary energy storage in improving urban-scale load matching performance. The findings in this paper indicate a high potential from EV flexibility in improving urban energy system performance. 

    Place, publisher, year, edition, pages
    Elsevier, 2024
    Keywords
    electric vehicle smart charging, vehicle-to-grid, wind energy, solar energy, urban energy system, net zero energy
    National Category
    Energy Systems Energy Engineering Infrastructure Engineering 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-499940 (URN)10.1016/j.etran.2024.100314 (DOI)
    Funder
    Swedish Energy Agency, 49421-1Swedish Energy Agency, 50986-1ÅForsk (Ångpanneföreningen's Foundation for Research and Development), 23-397SOLVEStandUpInterreg, 38-2-8-19
    Note

    De två första författarna delar förstaförfattarskapet

    Available from: 2023-04-05 Created: 2023-04-05 Last updated: 2024-01-24Bibliographically approved
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  • 3.
    Qian, Kun
    et al.
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, Sønderborg, Denmark.
    Fachrizal, Reza
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Ebel, Thomas
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, Sønderborg, Denmark.
    Adam, Rebecca
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, Sønderborg, Denmark.
    The Impact of Considering State of Charge Dependent Maximum Charging Powers on the Optimal Electric Vehicle Charging Scheduling2023In: IEEE Transactions on Transportation Electrification, E-ISSN 2332-7782, Vol. 9, no 3, p. 4517-4530Article in journal (Refereed)
    Abstract [en]

    Intelligent charging solutions facilitate mobility electrification. Mathematically, electric vehicle (EV) charging scheduling formulations are constrained optimization problems. Therefore, accurate constraint modeling is theoretically and practically relevant for scheduling. However, the current scheduling literature lacks an accurate problem formulation, including the joint modeling of the nonlinear battery charging profile and minimum charging power constraints. The minimum charging power constraint prevents allocating inexecutable charging profiles. Furthermore, if the problem formulation does not consider the battery charging profile, the scheduling execution may deviate from the allocated charging profile. An insignificant deviation indicates that simplified modeling is acceptable. After providing the problem formulation targeting the maximum possible vehicle battery state of charge (SoC) on departure, the numerical assessment shows how the constraint consideration impacts the scheduling performance in typical charging scenarios (weekday workplace and weekend public charging where the grid supplies up to forty vehicles). The simulation results show that the nonlinear battery charging constraint is practically negligible: For many connected EVs, the grid limit frequently overrules that constraint. The resulting difference between the final mean SoCs using and not using accurate modeling does not exceed 0.2%. Consequently, the results justify simplified modeling (excluding the nonlinear charging profile) for similar scenarios in future contributions.

    Download full text (pdf)
    fulltext
  • 4.
    Qian, Kun
    et al.
    University of Southern Denmark.
    Fachrizal, Reza
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Ebel, Thomas
    University of Southern Denmark.
    Adam, Rebecca
    University of Southern Denmark.
    Validating and Improving an Aggregated EV Model for Energy Systems Evaluation2023Conference paper (Refereed)
    Abstract [en]

    With the trend in transportation electrification, electric vehicle (EV) charging/discharging scheduling has become an area of concern. Scheduling can help manage EV charging/discharging activities. Besides, it is also valuable for energy systems evaluation, in which case modeling the individual EV has high complexity and requires a long computation time due to too many EVs. The aggregated model significantly reduces computation time but may sacrifice accuracy. This work investigates the trade-off between accuracy and computational time when designing intelligent EV charging/discharging scheduling by comparing the individual and aggregated models. This work first provides a detailed problem formulation. The simulation results show that the aggregated model can achieve similar energy system performance estimations from the energy-matching perspective compared to the individual model, given that the system allows vehicle-to-grid (V2G). Otherwise, the aggregated model will overestimate the performance. Thus, this work, in the meantime, proposes an extra constraint to avoid such overestimation when V2G is not allowed. Given the validated accuracy of the aggregated model and its advantage of low complexity and computation time, the aggregated model is more suitable for assessing large (e.g., city-level) energy systems. 

  • 5.
    Qian, Kun
    et al.
    Univ Southern Denmark, Dept Mech & Elect Engn, Ctr Ind Elect CIE, DK-6400 Sonderborg, Denmark..
    Fachrizal, Reza
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment. Univ Southern Denmark, Dept Mech & Elect Engn, Ctr Ind Elect CIE, DK-6400 Sonderborg, Denmark..
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Ebel, Thomas
    Univ Southern Denmark, Dept Mech & Elect Engn, Ctr Ind Elect CIE, DK-6400 Sonderborg, Denmark..
    Adam, Rebecca
    Univ Southern Denmark, Dept Mech & Elect Engn, Ctr Ind Elect CIE, DK-6400 Sonderborg, Denmark..
    Validating and Improving an Aggregated EV Model for Energy Systems Evaluation2023In: 2023 11th International Conference on Smart Grid, icSmartGrid, IEEE, 2023Conference paper (Refereed)
    Abstract [en]

    With the trend in transportation electrification, electric vehicle (EV) charging/discharging scheduling has become an area of concern. Scheduling can help manage EV charging/discharging activities. Besides, it is also valuable for energy systems evaluation, in which case modeling the individual EV has high complexity and requires a long computation time due to too many EVs. The aggregated model significantly reduces computation time but may sacrifice accuracy. This work investigates the trade-off between accuracy and computational time when designing intelligent EV charging/discharging scheduling by comparing the individual and aggregated models. This work first provides a detailed problem formulation. The simulation results show that the aggregated model can achieve similar energy system performance estimations from the energy-matching perspective compared to the individual model, given that the system allows vehicle-to-grid (V2G). Otherwise, the aggregated model will overestimate the performance. Thus, this work, in the meantime, proposes an extra constraint to avoid such overestimation when V2G is not allowed. Given the validated accuracy of the aggregated model and its advantage of low complexity and computation time, the aggregated model is more suitable for assessing large (e.g., city-level) energy systems.

  • 6.
    Qian, Kun
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Fachrizal, Reza
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Ebel, Thomas
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, S⊘nderborg, Denmark.
    Adam, Rebecca
    Department of Mechanical and Electrical Engineering, Centre for Industrial Electronics (CIE), University of Southern Denmark, S⊘nderborg, Denmark.
    Validating and Improving the Aggregated EV Model for Energy Systems Evaluation2023In: 2023 11th International Conference on Smart Grid (icSmartGrid), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 1-7Conference paper (Refereed)
    Abstract [en]

    With the trend in transportation electrification, electric vehicle (EV) charging/discharging scheduling has become an area of concern. Scheduling can help manage EV charging/discharging activities. Besides, it is also valuable for energy systems evaluation, in which case modeling the individual EV has high complexity and requires a long computation time due to too many EVs. The aggregated model significantly reduces computation time but may sacrifice accuracy. This work investigates the trade-off between accuracy and computational time when designing intelligent EV charging/discharging scheduling by comparing the individual and aggregated models. This work first provides a detailed problem formulation. The simulation results show that the aggregated model can achieve similar energy system performance estimations from the energy-matching perspective compared to the individual model, given that the system allows vehicle-to-grid (V2G). Otherwise, the aggregated model will overestimate the performance. Thus, this work, in the meantime, proposes an extra constraint to avoid such overestimation when V2G is not allowed. Given the validated accuracy of the aggregated model and its advantage of low complexity and computation time, the aggregated model is more suitable for assessing large (e.g., city-level) energy systems.

  • 7.
    Huang, Pei
    et al.
    Dalarna Univ, Dept Energy & Built Environm, Falun, Sweden..
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Fachrizal, Reza
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Lovati, Marco
    Aalto Univ, Dept Architecture, Espoo 02150, Finland..
    Zhang, Xingxing
    Dalarna Univ, Dept Energy & Built Environm, Falun, Sweden..
    Sun, Yongjun
    City Univ Hong Kong, Div Bldg Sci & Technol, Hong Kong, Peoples R China..
    Comparative studies of EV fleet smart charging approaches for demand response in solar-powered building communities2022In: Sustainable cities and society, ISSN 2210-6707, Vol. 85, article id 104094Article in journal (Refereed)
    Abstract [en]

    The use of electric vehicles (EVs) has been on the rise during the past decade, and the number is expected to rapidly increase in the future. At aggregated level, the large EV charging loads, if not well regulated, will cause great stress on the existing grid infrastructures. On the other hand, considered as a resource-efficient and cost-effective demand response resource, EV fleet smart charging control methods have been developed and applied to mitigate power issues of the grid while avoiding expensive upgrade of power grid infrastructure. Until now, there is no systematic study on how different coordination mechanisms affecting the EV fleet's charging demand response performance. Thus, it is still unclear which one may perform better in the increasingly common solar-powered building communities, especially as demand response is increasingly concerned. Aiming to fill in such knowledge gaps, this study conducted systematic comparative studies of three representative control methods selected from the non-coordinated, bottom-up coordinated, and top-down coordinated control categories. Their power regulation performances have been comparatively investigated in two perspectives: minimizing peak power exchanges with the grid and maximizing PV self-utilization, based on a real building community in Sweden. Meanwhile, their computational performances have also been investigated. The study results show that due to the ability to schedule and coordinate all the EVs simultaneously, the top-down coordinated control is superior to the other two control methods in the considered demand response performances. Note that its better performance is realized with a higher computational load, leading to possible convergence difficulties in practice. The study results will help improve understanding of how coordination affect the EV smart charging control performances. It will pave the way for developments of more sophisticated control methods for EV smart charging in more complex scenarios.

  • 8.
    Sakti, Anjar Dimara
    et al.
    Remote Sensing and Geographic Information Science Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia.
    Ihsan, Kalingga Titon Nur
    Center for Remote Sensing, Institut Teknologi Bandung, Bandung 40132, Indonesia.
    Anggraini, Tania Septi
    Center for Remote Sensing, Institut Teknologi Bandung, Bandung 40132, Indonesia.
    Shabrina, Zahratu
    Department of Geography, King’s College London, London WC2B 4BG, UK.
    Sasongko, Nugroho Adi
    Research Center for Sustainable Production System and Life Cycle Assessment, National Research and Innovation Agency, Jakarta 10340, Indonesia.
    Fachrizal, Reza
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Aziz, Muhammad
    Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan.
    Aryal, Jagannath
    Faculty of Engineering and Information Technology, The University of Melbourne, Melbourne, VIC 3010, Australia.
    Yuliarto, Brian
    Faculty of Industrial Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia.
    Hadi, Pradita Octoviandiningrum
    Power Engineering Research Group, School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung 40132, Indonesia.
    Wikantika, Ketut
    Remote Sensing and Geographic Information Science Research Group, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung 40132, Indonesia.
    Multi-Criteria Assessment for City-Wide Rooftop Solar PV Deployment: A Case Study of Bandung, Indonesia2022In: Remote Sensing, E-ISSN 2072-4292, Vol. 14, no 12, article id 2796Article in journal (Refereed)
    Abstract [en]

    The world faces the threat of an energy crisis that is exacerbated by the dominance of fossil energy sources that negatively impact the sustainability of the earth’s ecosystem. Currently, efforts to increase the supply of renewable energy have become a global agenda, including using solar energy which is one of the rapidly developing clean energies. However, studies in solar photovoltaic (PV) modelling that integrates geospatial information of urban morphological building characters, solar radiation, and multiple meteorological parameters in low-cost scope have not been explored fully. Therefore, this research aims to model the urban rooftop solar PV development in the Global South using Bandung, Indonesia, as a case study. This research also has several specific purposes: developing a building height model as well as determining the energy potential of rooftop solar PV, the energy needs of each building, and the residential property index. This study is among the first to develop the national digital surface model (DSM) of buildings. In addition, the analysis of meteorological effects integrated with the hillshade parameter was used to obtain the solar PV potential value of the roof in more detail. The process of integrating building parameters in the form of rooftop solar PV development potential, energy requirements, and residential property index of a building was expected to increase the accuracy of determining priority buildings for rooftop solar PV deployment in Bandung. This study shows that the estimated results of effective solar PV in Bandung ranges from 351.833 to 493.813 W/m2, with a total of 1316 and 36,372 buildings in scenarios 1 and 2 being at a high level of priority for solar PV development. This study is expected to be a reference for the Indonesian government in planning the construction of large-scale rooftop solar PV in urban areas to encourage the rapid use of clean energy. Furthermore, this study has general potential for other jurisdictions for the governments focusing on clean energy using geospatial information in relation with buildings and their energy consumption. 

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  • 9.
    Fachrizal, Reza
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Shepero, Mahmoud
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Åberg, Magnus
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance2022In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 307, article id 118139Article in journal (Refereed)
    Abstract [en]

    The integration of photovoltaic (PV) systems and electric vehicles (EVs) in the built environment, including at workplaces, has increased significantly in the recent decade and has posed new technical challenges for the power system, such as increased peak loads and component overloading. Several studies show that improved matching between PV generation and EV load through both optimal sizing and operation of PV-EV systems can minimize these challenges. This paper presents an optimal PV-EV sizing framework for workplace solar powered charging stations considering load matching performances. The proposed optimal sizing framework in this study uses a novel score, called self-consumption-sufficiency balance (SCSB), which conveys the balance between self-consumption (SC) and self-sufficiency (SS), based on a similar principle as the F1-score in machine learning. A high SCSB score implies that the system is close to being self-sufficient without exporting or curtailing a large share of local production. The results show that the SCSB performance tends to be higher with a larger combined PV-EV size. In addition to presenting PV-EV optimal sizing at the workplace charging station, this study also assesses a potential SC and SS enhancement with optimal operation through smart charging schemes. The results show that smart charging schemes can significantly improve the load matching performances by up to 42.6 and 40.8 percentage points for SC and SS, respectively. The smart charging scheme will also shift the combined optimal PV-EV sizes. Due to its simplicity and universality, the optimal sizing based on SCSB score proposed in this study can be a benchmark for future studies on optimal sizing of PV-EV system, or distributed generation-load in general.

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  • 10.
    Fachrizal, Reza
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Lindberg, Oskar
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Kinasih, Annisa Dhini Septi
    Department of Mathematics and Computer Science, Karlstad University.
    Muntean, Adrian
    Department of Mathematics and Computer Science, Karlstad University.
    Widén, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Residential building with rooftop solar PV system, battery storage and electric vehicle charging: Environmental impact and energy matching assessments for a multi-family house in a Swedish city2022In: 21st Wind & Solar Integration Workshop, The Institution of Engineering and Technology (IET) , 2022, p. 1-8Conference paper (Refereed)
    Abstract [en]

    In this paper, environmental impact and energy matching assessments for a residential building with a rooftop photovoltaic (PV)system, battery energy storage system (BESS) and electric vehicles (EV) charging load are conducted. This paper studies a real multi-family house with a rooftop PV system in a city located on the west-coast of Sweden, as a case study. The environmental impact parameter assessed in this study is CO2 equivalent (CO2-eq) emissions. It should be noted that the CO2-eqemission assessment takes into account the whole life cycle, not only the operational processes. The assessments consider boththe household and transport energy demands for the building’s residents. Results show that, CO2-eq emissions from the building electricity usage are increased by 1.65 ton/year with the integrationof PV-BESS system. This is because the Swedish electricity mix has a lower CO2-eq emissions than the PV-BESS system. The total CO2-eq emissions from the transport needs of the building’s residents are significantly decreased, by 32.9 ton/year, if they switch from fossil-fuel-powered cars to EVs. However, the integration of EVs increases the power demand significantly which could be problematic for the power system. In such scenario, the highly-utilized distributed PV systems, enhanced by BESS, can be a low-carbon solution to address the increased power demand challenges coming from transport electrification.

  • 11.
    Fachrizal, Reza
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Ramadhani, Umar Hanif
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Munkhammar, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Widén, Joakim
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.
    Combined PV-EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment2021In: Sustainable Energy, Grids and Networks, E-ISSN 2352-4677, Vol. 26, article id 100445Article in journal (Refereed)
    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.

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    SEGAN100445
  • 12.
    Fachrizal, Reza
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Civil Engineering and Built Environment.