A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM
Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual stati...
Uloženo v:
| Vydáno v: | Frontiers in energy research Ročník 11 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Frontiers Research Foundation
09.05.2023
Frontiers Media S.A |
| Témata: | |
| ISSN: | 2296-598X, 2296-598X |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging. |
|---|---|
| AbstractList | Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing key energy management services to the distribution system operator (DSO). EVAs can also balance the grid’s reactive power as a virtual static VAR compensator (SVC) and provide voltage stability by utilizing advanced electric vehicle (EV) chargers that are capable of four-quadrant operations to provide reactive power management. Finally, managed charging can benefit EVAs themselves by minimizing power factor penalties in their electricity bills. In this paper, we propose a novel EV charging scheduling algorithm based on a hierarchical distributed optimization framework that minimizes peak load and provides reactive power compensation for the DSO by collaboration with EVAs that manage both the active and the reactive charging and discharging power of participating EVs. Utilizing the alternative direction method of multipliers (ADMM), the proposed distributed optimization approach scales well with increased EV charging infrastructure by balancing active and reactive power while decreasing computational burden. In our proposed hierarchical approach, each EVA schedules the active and reactive EV charging and discharging power for 1) reactive power compensation in order to minimize power factor penalty and electricity cost accrued by the EVA, 2) satisfaction of each EV’s energy demand at minimal charging cost, and 3) peak shaving and load management for the DSO. When compared with an uncoordinated charging model, the efficacy of this proposed model is successfully demonstrated through a 300% decreased peak EV load for the DSO, 28% lower electricity costs for EV users, and 98.55% smaller power factor penalty, along with 17.58% lower overall electricity costs, for EVAs. The performance of our approach is validated in a case study with 50 EVs at multiple EVAs in an IEEE 13-bus test case and compared the results with uncoordinated EV charging. |
| Author | Zhang, Chen Sherman, Michael Gadh, Rajit Sheinberg, Rachel Narayana Gowda, Shashank Ahmadian, Amirhossein |
| Author_xml | – sequence: 1 givenname: Chen surname: Zhang fullname: Zhang, Chen – sequence: 2 givenname: Rachel surname: Sheinberg fullname: Sheinberg, Rachel – sequence: 3 givenname: Shashank surname: Narayana Gowda fullname: Narayana Gowda, Shashank – sequence: 4 givenname: Michael surname: Sherman fullname: Sherman, Michael – sequence: 5 givenname: Amirhossein surname: Ahmadian fullname: Ahmadian, Amirhossein – sequence: 6 givenname: Rajit surname: Gadh fullname: Gadh, Rajit |
| BackLink | https://www.osti.gov/servlets/purl/2424370$$D View this record in Osti.gov |
| BookMark | eNp9UctOHDEQHCEiQQg_wMniPhu_ZsZzXBFCkEC5JCg3q223Z41m7ZXtEPH3mWWJFHHg1NWlqlKr62NzHFPEprlgdCWEGj97jHlaccrFitFBUT4cNaecj33bjerX8X_4pDkv5ZFSygTvJKOnzW5NYnrCmcyQJ2yLhRnJ9QOxm2UPcSLFbtD9nvcQ5inlUDdbYlMswWHesw_8hkB0JCPYGp6Q7NIfzGQLESbcYqzEQEFHUiTrL_f3n5oPHuaC56_zrPn59frH1bf27vvN7dX6rrWyo7UVDuzQjaOnvZIMlRXomOcj7zvoRwNoQHaDYr1hg2d-cI4710tvhRGDMUycNbeHXJfgUe9y2EJ-1gmCfiFSnjTkGuyM2vVgvB-lksZJqyiMIE2nlFOD63pqlqzLQ1YqNehiQ0W7WX4Q0VbNJZdioItIHUQ2p1Iyer3ooIYUa4Ywa0b1vi390pbet6Vf21qs_I31373vmP4C7RucTQ |
| CitedBy_id | crossref_primary_10_3390_wevj14110310 crossref_primary_10_1109_OJITS_2025_3593437 crossref_primary_10_3390_su17093847 crossref_primary_10_1063_5_0212427 |
| Cites_doi | 10.1016/j.apenergy.2020.115285 10.1109/TDC.2018.8440531 10.1109/PMAPS.2018.8440360 10.1109/TPWRS.2015.2472957 10.1007/s10107-014-0841-6 10.3390/en13051162 10.1016/j.apenergy.2016.10.117 10.1109/PESW.2001.916993 10.1109/TPWRS.2012.2221750 10.1109/TPWRS.2012.2210288 10.1016/j.apenergy.2013.04.094 10.1109/TSG.2014.2384202 10.1109/TPEL.2013.2251007 10.1109/TVT.2014.2316153 10.1109/TSG.2015.2495181 10.1109/59.336109 10.3389/fenrg.2021.705066 10.3389/fenrg.2021.811964 10.1016/j.est.2020.102114 10.1561/2200000016 10.1109/TSG.2018.2816404 10.3390/en15020589 10.1016/j.neucom.2018.05.073 10.1016/j.enpol.2017.11.015 10.3390/wevj12040236 10.1109/COMST.2016.2518628 10.3389/fenrg.2022.873262 10.1109/ACCESS.2019.2939595 10.1109/PESGM.2018.8586132 10.1016/j.apenergy.2016.12.065 10.1109/TSG.2015.2509030 10.1016/j.jpowsour.2016.10.048 10.1016/j.energy.2019.06.118 10.1109/ICPEE50452.2021.9358582 10.1016/j.jclepro.2020.125203 10.1109/TPWRS.2018.2829021 10.3389/fenrg.2021.713510 10.1016/j.apenergy.2018.07.092 10.1016/j.ijepes.2019.105661 10.1109/TPWRS.2016.2582903 10.1016/j.enconman.2015.11.066 10.1109/TSG.2020.2995923 10.1109/TII.2018.2829710 10.1109/TVT.2015.2504264 10.1016/j.apenergy.2019.113490 10.1016/j.jpowsour.2016.09.116 10.1016/j.epsr.2019.03.003 10.3390/en11010178 10.1021/acssuschemeng.1c05490 10.1016/j.rser.2019.109618 10.1109/TSG.2010.2045163 10.1109/TPWRS.2015.2395723 10.1109/TIA.2015.2483705 10.1016/j.apenergy.2016.05.034 |
| ContentType | Journal Article |
| CorporateAuthor | Univ. of California, Los Angeles, CA (United States) |
| CorporateAuthor_xml | – name: Univ. of California, Los Angeles, CA (United States) |
| DBID | AAYXX CITATION OIOZB OTOTI DOA |
| DOI | 10.3389/fenrg.2023.1078027 |
| DatabaseName | CrossRef OSTI.GOV - Hybrid OSTI.GOV DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2296-598X |
| ExternalDocumentID | oai_doaj_org_article_d6abff9484bd4c80a9a4b588d87d560b 2424370 10_3389_fenrg_2023_1078027 |
| GroupedDBID | 5VS 9T4 AAFWJ AAYXX ACGFS ADBBV AFPKN ALMA_UNASSIGNED_HOLDINGS BCNDV CITATION GROUPED_DOAJ KQ8 M~E OK1 2XV ACXDI IAO IEA ISR OIOZB OTOTI |
| ID | FETCH-LOGICAL-c450t-3dac7599f06841e8c3ed1f29265a69baeba457816b17f1f7dd2dd64fc3b37bb13 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 8 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000993716400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2296-598X |
| IngestDate | Fri Oct 03 12:50:31 EDT 2025 Mon Feb 03 04:56:55 EST 2025 Sat Nov 29 03:07:04 EST 2025 Tue Nov 18 21:26:13 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c450t-3dac7599f06841e8c3ed1f29265a69baeba457816b17f1f7dd2dd64fc3b37bb13 |
| Notes | USDOE |
| OpenAccessLink | https://doaj.org/article/d6abff9484bd4c80a9a4b588d87d560b |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_d6abff9484bd4c80a9a4b588d87d560b osti_scitechconnect_2424370 crossref_citationtrail_10_3389_fenrg_2023_1078027 crossref_primary_10_3389_fenrg_2023_1078027 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-05-09 |
| PublicationDateYYYYMMDD | 2023-05-09 |
| PublicationDate_xml | – month: 05 year: 2023 text: 2023-05-09 day: 09 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Frontiers in energy research |
| PublicationYear | 2023 |
| Publisher | Frontiers Research Foundation Frontiers Media S.A |
| Publisher_xml | – name: Frontiers Research Foundation – name: Frontiers Media S.A |
| References | Zha (B58) 2018; 311 Gao (B18) 2014; 63 Karfopoulos (B29) 2016; 31 Xiong (B54) 2018 (B36) 2022 Thurner (B47) 2018; 33 Gandhi (B17) 2021; 9 Han (B22) 2010; 1 Forrest (B14) 2016; 336 Colmenar-Santos (B8) 2019; 183 Khaki (B31) 2018 Rivera (B45) 2017; 8 Li (B35) 2019; 172 Mehboob (B38) 2019; 10 Haidar (B21) 2016; 52 Fang (B13) 2021; 9 Tan (B46) 1994; 9 Uddin (B49) 2018; 113 (B39) 2021 Peng (B43) 2017; 190 Bishop (B3) 2013; 111 Gowda (B19) 2019 Meelen (B37) 2021; 290 Kou (B33) 2020; 11 Zhang (B59) 2017; 32 Karfopoulos (B28) 2016; 31 Gan (B16) 2013; 28 Yi (B56) 2020; 117 (B48) 2022 Hussain (B26) 2021; 33 Hu (B23) 2021; 9 Kersting (B30) 2001 Al-Ogaili (B1) 2019; 7 Boyd (B4) 2010; 3 Wu (B53) 2019; 253 Wang (B52) 2019; 15 Ravi (B61) 2022; 15 Brinkel (B5) 2020; 276 Wang (B51); 18 Freund (B15) 2016; 155 Pinto (B44) 2015 Bhattarai (B2) 2015; 6 (B6) 2020 Faia (B12) 2021; 9 Wang (B50); 332 Xydas (B55) 2016; 177 Jian (B27) 2017; 186 Kisacikoglu (B32) 2013; 28 Lenka (B34) 2021 Pearre (B42) 2016; 109 Das (B9) 2020; 120 Zhang (B60) 2018; 229 Huda (B25) 2020; 13 Ortega-Vazquez (B41) 2013; 28 de Hoog (B10) 2016; 7 Chung (B7) 2018 Deb (B11) 2018; 11 Huber (B24) 2021; 12 Yu (B57) 2022; 10 Nikkhah Mojdehi (B40) 2016; 65 (B20) 2020 |
| References_xml | – volume: 276 start-page: 115285 year: 2020 ident: B5 article-title: Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115285 – year: 2018 ident: B31 article-title: A hierarchical ADMM based framework for EV charging scheduling doi: 10.1109/TDC.2018.8440531 – volume-title: Standard commercial/industrial rates year: 2022 ident: B36 – year: 2018 ident: B7 article-title: Electric vehicle user behavior prediction using hybrid kernel density estimator doi: 10.1109/PMAPS.2018.8440360 – volume: 31 start-page: 2834 year: 2016 ident: B29 article-title: Distributed coordination of electric vehicles providing V2G regulation services publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2015.2472957 – volume: 155 start-page: 199 year: 2016 ident: B15 article-title: New analysis and results for the Frank–Wolfe method publication-title: Math. Program doi: 10.1007/s10107-014-0841-6 – volume: 13 start-page: 1162 year: 2020 ident: B25 article-title: Techno economic analysis of vehicle to grid (V2G) integration as distributed energy resources in Indonesia power system publication-title: Energies (Basel) doi: 10.3390/en13051162 – volume: 186 start-page: 46 year: 2017 ident: B27 article-title: High efficient valley-filling strategy for centralized coordinated charging of large-scale electric vehicles publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.10.117 – year: 2001 ident: B30 article-title: Radial distribution test feeders doi: 10.1109/PESW.2001.916993 – volume: 28 start-page: 1806 year: 2013 ident: B41 article-title: Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2012.2221750 – volume: 28 start-page: 940 year: 2013 ident: B16 article-title: Optimal decentralized protocol for electric vehicle charging publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2012.2210288 – volume: 111 start-page: 206 year: 2013 ident: B3 article-title: Evaluating the impact of V2G services on the degradation of batteries in PHEV and EV publication-title: Appl Energy doi: 10.1016/j.apenergy.2013.04.094 – volume: 6 start-page: 784 year: 2015 ident: B2 article-title: Optimizing electric vehicle coordination over a heterogeneous mesh network in a scaled-down smart grid testbed publication-title: IEEE Trans Smart Grid doi: 10.1109/TSG.2014.2384202 – volume: 28 start-page: 5717 year: 2013 ident: B32 article-title: EV/PHEV bidirectional charger assessment for V2G reactive power operation publication-title: IEEE Trans. Power Electron doi: 10.1109/TPEL.2013.2251007 – volume: 63 start-page: 3019 year: 2014 ident: B18 article-title: Integrated energy management of plug-in electric vehicles in power grid with renewables publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2014.2316153 – volume: 7 start-page: 827 year: 2016 ident: B10 article-title: A market mechanism for electric vehicle charging under network constraints publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2015.2495181 – volume: 9 start-page: 1517 year: 1994 ident: B46 article-title: Static VAR compensators for critical synchronous motor loads during voltage dips publication-title: IEEE Trans. Power Syst. doi: 10.1109/59.336109 – volume: 9 year: 2021 ident: B12 article-title: Local electricity markets for electric vehicles: An application study using a decentralized iterative approach publication-title: Front. Energy Res. doi: 10.3389/fenrg.2021.705066 – volume: 9 year: 2021 ident: B13 article-title: Multi-objective comprehensive charging/discharging scheduling strategy for electric vehicles based on the improved particle swarm optimization algorithm publication-title: Front. Energy Res. doi: 10.3389/fenrg.2021.811964 – volume: 33 start-page: 102114 year: 2021 ident: B26 article-title: Optimal management strategies to solve issues of grid having electric vehicles (ev): A review publication-title: J. Energy Storage doi: 10.1016/j.est.2020.102114 – volume: 3 start-page: 1 year: 2010 ident: B4 article-title: Distributed optimization and statistical learning via the alternating direction method of multipliers publication-title: Foundations and Trends in Machine Learning doi: 10.1561/2200000016 – volume-title: End-use load profiles for the U.S. Building Stock year: 2021 ident: B39 – volume: 10 start-page: 2999 year: 2019 ident: B38 article-title: Smart operation of electric vehicles with four-quadrant chargers considering Uncertainties publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2018.2816404 – volume: 15 year: 2022 ident: B61 article-title: Utilization of Electric Vehicles for Vehicle-to-Grid Services: Progress and Perspectives publication-title: Energies (Basel) doi: 10.3390/en15020589 – volume: 311 start-page: 209 year: 2018 ident: B58 article-title: Non-convex weighted ℓ nuclear norm based ADMM framework for image restoration publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.05.073 – volume: 113 start-page: 342 year: 2018 ident: B49 article-title: The viability of vehicle-to-grid operations from a battery technology and policy perspective publication-title: Energy Policy doi: 10.1016/j.enpol.2017.11.015 – volume: 12 start-page: 236 year: 2021 ident: B24 article-title: Vehicle to grid impacts on the total cost of ownership for electric vehicle drivers publication-title: World Electr. Veh. J. doi: 10.3390/wevj12040236 – volume: 18 start-page: 1500 ident: B51 article-title: Smart charging for electric vehicles: A survey from the algorithmic perspective publication-title: IEEE Commun. Surv. Tutorials doi: 10.1109/COMST.2016.2518628 – volume: 10 year: 2022 ident: B57 article-title: Optimal regulation strategy of electric vehicle charging and discharging based on dynamic regional dispatching price publication-title: Front. Energy Res. doi: 10.3389/fenrg.2022.873262 – volume: 7 start-page: 128353 year: 2019 ident: B1 article-title: Review on scheduling, clustering, and forecasting strategies for controlling electric vehicle charging: Challenges and recommendations publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2939595 – volume-title: IEEE power and energy society year: 2018 ident: B54 article-title: Electric vehicle driver clustering using statistical model and machine learning doi: 10.1109/PESGM.2018.8586132 – volume-title: Treasury releases initial information on electric vehicle tax credit under newly enacted inflation reduction act year: 2022 ident: B48 – volume: 190 start-page: 591 year: 2017 ident: B43 article-title: An optimal dispatching strategy for V2G aggregator participating in supplementary frequency regulation considering EV driving demand and aggregator’s benefits publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.12.065 – volume: 8 start-page: 1852 year: 2017 ident: B45 article-title: Distributed convex optimization for electric vehicle aggregators publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2015.2509030 – volume: 336 start-page: 63 year: 2016 ident: B14 article-title: Charging a renewable future: The impact of electric vehicle charging intelligence on energy storage requirements to meet renewable portfolio standards publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.10.048 – volume: 183 start-page: 61 year: 2019 ident: B8 article-title: Electric vehicle charging strategy to support renewable energy sources in Europe 2050 low-carbon scenario publication-title: Energy doi: 10.1016/j.energy.2019.06.118 – year: 2021 ident: B34 article-title: Reactive power compensation using vehicle-to-grid enabled bidirectional off-board EV battery charger doi: 10.1109/ICPEE50452.2021.9358582 – volume: 290 start-page: 125203 year: 2021 ident: B37 article-title: Vehicle-to-Grid in the UK fleet market: An analysis of upscaling potential in a changing environment publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.125203 – volume: 33 start-page: 6510 year: 2018 ident: B47 article-title: Pandapower - an open-source Python tool for convenient modeling, analysis, and optimization of electric power systems publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2018.2829021 – volume: 9 year: 2021 ident: B23 article-title: Assessment for voltage violations considering reactive power compensation provided by smart inverters in distribution network publication-title: Front. Energy Res. doi: 10.3389/fenrg.2021.713510 – volume: 229 start-page: 96 year: 2018 ident: B60 article-title: A multi-agent based integrated volt-var optimization engine for fast vehicle-to-grid reactive power dispatch and electric vehicle coordination publication-title: Appl. Energy doi: 10.1016/j.apenergy.2018.07.092 – volume: 117 start-page: 105661 year: 2020 ident: B56 article-title: A highly efficient control framework for centralized residential charging coordination of large electric vehicle populations publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2019.105661 – start-page: 1 year: 2019 ident: B19 article-title: Transmission, distribution deferral and congestion relief services by electric vehicles – volume: 32 start-page: 1451 year: 2017 ident: B59 article-title: Scalable electric vehicle charging protocols publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2016.2582903 – volume: 109 start-page: 130 year: 2016 ident: B42 article-title: Electric vehicle charging to support renewable energy integration in a capacity constrained electricity grid publication-title: Energy Convers. Manag. doi: 10.1016/j.enconman.2015.11.066 – volume: 11 start-page: 4871 year: 2020 ident: B33 article-title: A scalable and distributed algorithm for managing residential demand response programs using alternating direction method of multipliers (ADMM) publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2020.2995923 – volume: 15 start-page: 54 year: 2019 ident: B52 article-title: Coordinated electric vehicle charging with reactive power support to distribution grids publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2018.2829710 – volume: 65 start-page: 4572 year: 2016 ident: B40 article-title: An on-demand compensation function for an EV as a reactive power service provider publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2015.2504264 – volume: 253 start-page: 113490 year: 2019 ident: B53 article-title: A hierarchical charging control of plug-in electric vehicles with simple flexibility model publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.113490 – volume: 332 start-page: 193 ident: B50 article-title: Quantifying electric vehicle battery degradation from driving vs. vehicle-to-grid services publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2016.09.116 – start-page: 37 year: 2015 ident: B44 article-title: An electric vehicle charging station: Monitoring and analysis of power quality – volume: 172 start-page: 152 year: 2019 ident: B35 article-title: An MPC based optimized control approach for EV-based voltage regulation in distribution grid publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2019.03.003 – volume: 11 start-page: 178 year: 2018 ident: B11 article-title: Impact of electric vehicle charging station load on distribution network publication-title: Energies (Basel) doi: 10.3390/en11010178 – volume-title: Executive Order N-79-20 year: 2020 ident: B6 – volume: 9 start-page: 14975 year: 2021 ident: B17 article-title: City-wide modeling of vehicle-to-grid economics to understand effects of battery performance publication-title: ACS Sustain Chem. Eng. doi: 10.1021/acssuschemeng.1c05490 – volume: 120 start-page: 109618 year: 2020 ident: B9 article-title: Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2019.109618 – volume: 1 start-page: 65 year: 2010 ident: B22 article-title: Development of an optimal vehicle-to-grid aggregator for frequency regulation publication-title: IEEE Trans. Smart Grid doi: 10.1109/TSG.2010.2045163 – volume: 31 start-page: 329 year: 2016 ident: B28 article-title: Distributed coordination of electric vehicles providing V2G services publication-title: IEEE Trans. Power Syst. doi: 10.1109/TPWRS.2015.2395723 – volume-title: Gurobi optimizer reference manual year: 2020 ident: B20 – volume: 52 start-page: 483 year: 2016 ident: B21 article-title: Behavioral characterization of electric vehicle charging loads in a distribution power grid through modeling of battery chargers publication-title: IEEE Trans. Ind. Appl. doi: 10.1109/TIA.2015.2483705 – volume: 177 start-page: 354 year: 2016 ident: B55 article-title: A multi-agent based scheduling algorithm for adaptive electric vehicles charging publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.05.034 |
| SSID | ssj0001325410 |
| Score | 2.2724478 |
| Snippet | Electric vehicle aggregators (EVAs) that utilize vehicle-to-grid (V2G) technologies can function as both controllable loads and virtual power plants, providing... |
| SourceID | doaj osti crossref |
| SourceType | Open Website Open Access Repository Enrichment Source Index Database |
| SubjectTerms | EV aggregator EV charging scheduling hierarchical ADMM POWER TRANSMISSION AND DISTRIBUTION reactive power management V2G |
| Title | A novel large-scale EV charging scheduling algorithm considering V2G and reactive power management based on ADMM |
| URI | https://www.osti.gov/servlets/purl/2424370 https://doaj.org/article/d6abff9484bd4c80a9a4b588d87d560b |
| Volume | 11 |
| WOSCitedRecordID | wos000993716400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2296-598X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001325410 issn: 2296-598X databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2296-598X dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001325410 issn: 2296-598X databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Nb9QwELVQxQEOqHyJpRT5wA2FOrHjj-NStnDZigNUvVm2xy5I22TVLj3y2zuThFVOcOGSSJajRM9jj180fo-xdwnplksgKtNosjCzsoqqhQqyAe1kq-VkNmHOz-3lpfs6s_qimrBRHngE7gR0iKU4ZVUElawILqjYWgvWAGbrSKuvMG5Gpoa_KxKJTy3GUzLIwtxJweG4-kBm4UhYjRVkIzPLRINgP956nFizBHN2yJ5MO0O-HL_oKXuQu2fs8Uwv8DnbLnnX3-UN31D9dnWL-Ga-uuCD3BH24EhVMXXQCXMeNlc9Ev8f1zxNnpzUetF85qEDjlvFYaHjW3JJ49f7IhhOaQ143_Hlp_X6Bft-tvp2-qWaLBOqpFqxqySEZFrnitBW1dkmmaEujWt0G7SLIcegcI7WOtam1MUANABalSSjNDHW8iU76Pouv2I8pyB1XYRKTikwbYzaZSjKNCBARrNg9R_4fJr0xMnWYuORVxDkfoDcE-R-gnzB3u-f2Y5qGn_t_ZFGZd-TlLCHBowPP8WH_1d8LNgRjanHDQWp4iYqH0o7T6dipBGv_8crjtgj-uyhDNK9YQe7m1_5mD1Md7uftzdvh8DE6_r36h5c8enw |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+novel+large-scale+EV+charging+scheduling+algorithm+considering+V2G+and+reactive+power+management+based+on+ADMM&rft.jtitle=Frontiers+in+energy+research&rft.au=Zhang%2C+Chen&rft.au=Sheinberg%2C+Rachel&rft.au=Narayana+Gowda%2C+Shashank&rft.au=Sherman%2C+Michael&rft.date=2023-05-09&rft.pub=Frontiers+Research+Foundation&rft.issn=2296-598X&rft.eissn=2296-598X&rft.volume=11&rft_id=info:doi/10.3389%2Ffenrg.2023.1078027&rft.externalDocID=2424370 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2296-598X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2296-598X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2296-598X&client=summon |