Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck
► Describe the mathematical framework for large-scale PHEV/PEV charging control. ► Manage the highly concentrated PHEV chargers considering real-world constraints. ► Develop and implement a suite of computational intelligence-based algorithms. ► Evaluate a variety of charging scenarios and the corre...
Saved in:
| Published in: | Applied energy Vol. 96; pp. 171 - 182 |
|---|---|
| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.08.2012
|
| Subjects: | |
| ISSN: | 0306-2619, 1872-9118 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | ► Describe the mathematical framework for large-scale PHEV/PEV charging control. ► Manage the highly concentrated PHEV chargers considering real-world constraints. ► Develop and implement a suite of computational intelligence-based algorithms. ► Evaluate a variety of charging scenarios and the corresponding control strategies. ► Demonstrate the effectiveness of the proposed computational intelligence approaches.
There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques. |
|---|---|
| AbstractList | There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10 years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques. There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques. ► Describe the mathematical framework for large-scale PHEV/PEV charging control. ► Manage the highly concentrated PHEV chargers considering real-world constraints. ► Develop and implement a suite of computational intelligence-based algorithms. ► Evaluate a variety of charging scenarios and the corresponding control strategies. ► Demonstrate the effectiveness of the proposed computational intelligence approaches. There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) within the next 10years. In the near future, a large number of PHEVs/PEVs in our society will add a large-scale energy load to our power grids, as well as add substantial energy resources that can be utilized. The large penetration of these vehicles into the marketplace poses a potential threat to the existing power grid. The existing parking infrastructure is not ready for the large penetration of plug-in vehicles and the high demand of electricity. Nowadays, the advanced computational intelligence methods can be applied to solve large-scale optimization problems in a Smart Grid environment. In this paper, authors propose and implement a suite of computational intelligence-based algorithms (e.g., Estimation of Distribution Algorithm, Particle Swarm Optimization) for optimally managing a large number of PHEVs/PEVs charging at a municipal parking station. Authors characterize the performance of the proposed methods using a Matlab simulation, and compare it with other optimization techniques. |
| Author | Su, Wencong Chow, Mo-Yuen |
| Author_xml | – sequence: 1 givenname: Wencong surname: Su fullname: Su, Wencong email: wsu2@ncsu.edu – sequence: 2 givenname: Mo-Yuen surname: Chow fullname: Chow, Mo-Yuen email: chow@ncsu.edu |
| BookMark | eNqFkU9r3DAQxUVJoJs0X6H1sRdvNNJaa0MPLcu2CQQayJ-rGMljo40tu5I3kG9fGbeXXBYG5vJ7T5r3LtiZHzwx9hn4Gjio68MaR_IU2re14ADrNLwsP7AVlFuRVwDlGVtxyVUuFFQf2UWMB865AMFXrNsN_XiccHKDxy5zfqKucy15S7nBSHW2WGc9emypJz9lzRAyzDoMLeXRYkfZ_c3--fp-_5xgNF0S9UfvrBuT44jhxfk2q8m-fGLnDXaRrv7tS_b0c_-4u8nvfv-63f24y-2Giyk3lQGzKYytJSjTFDUoy7eyLFQNZcWphsJaaDZcbi0URlo0lS1MWSq0okIhL9nXxXcMw58jxUn3Ltp0GHoajlGLdH5KrhD8JApcbkqQUsmEfltQG4YYAzXauiW4KaDrEqrnPvRB_-9Dz33oNKmPJFfv5GNwPYa308Ivi7DBQWMbXNRPDwlQc4kVh_ln3xeCUqivjoKO1s0V1i6QnXQ9uFOP_AXEgbVm |
| CitedBy_id | crossref_primary_10_1109_TII_2013_2284713 crossref_primary_10_1016_j_segan_2025_101718 crossref_primary_10_1016_j_rser_2015_06_007 crossref_primary_10_1016_j_asoc_2023_110560 crossref_primary_10_1016_j_ijepes_2019_105661 crossref_primary_10_1016_j_compeleceng_2024_109478 crossref_primary_10_1016_j_renene_2014_11_065 crossref_primary_10_1109_TSG_2016_2582749 crossref_primary_10_1016_j_apenergy_2016_04_024 crossref_primary_10_1186_s42162_022_00251_2 crossref_primary_10_1016_j_epsr_2017_03_009 crossref_primary_10_1109_TII_2019_2909276 crossref_primary_10_1016_j_procs_2019_08_136 crossref_primary_10_1155_2015_620425 crossref_primary_10_1007_s11276_019_01993_w crossref_primary_10_1109_ACCESS_2022_3220671 crossref_primary_10_1016_j_renene_2016_06_032 crossref_primary_10_1016_j_energy_2014_02_025 crossref_primary_10_1016_j_scs_2018_05_035 crossref_primary_10_1080_15325008_2019_1627609 crossref_primary_10_1371_journal_pone_0316677 crossref_primary_10_1016_j_apenergy_2015_10_008 crossref_primary_10_3390_su15043301 crossref_primary_10_1016_j_epsr_2017_01_008 crossref_primary_10_1016_j_apenergy_2017_12_036 crossref_primary_10_1016_j_ijepes_2023_109113 crossref_primary_10_1016_j_asoc_2018_07_008 crossref_primary_10_1016_j_rser_2015_12_353 crossref_primary_10_1016_j_energy_2016_12_039 crossref_primary_10_1080_23311916_2016_1203083 crossref_primary_10_1016_j_enconman_2013_09_006 crossref_primary_10_1002_2050_7038_12313 crossref_primary_10_1051_matecconf_20163802002 crossref_primary_10_1002_er_3130 crossref_primary_10_1016_j_apenergy_2014_01_040 crossref_primary_10_3390_en6094394 crossref_primary_10_1016_j_ijhydene_2023_01_285 crossref_primary_10_3390_en10040550 crossref_primary_10_3390_pr10101944 crossref_primary_10_1016_j_aej_2015_11_002 crossref_primary_10_1016_j_apenergy_2016_10_117 crossref_primary_10_1016_j_apenergy_2014_04_047 crossref_primary_10_3390_en16073210 crossref_primary_10_1016_j_energy_2017_07_006 crossref_primary_10_3390_en13215700 crossref_primary_10_3390_en10030377 crossref_primary_10_1016_j_enconman_2014_03_032 crossref_primary_10_3390_en7042449 crossref_primary_10_1109_TSG_2013_2280645 crossref_primary_10_1109_TSG_2013_2246198 crossref_primary_10_1155_2014_462312 crossref_primary_10_3390_su151410758 crossref_primary_10_1016_j_apenergy_2013_06_021 crossref_primary_10_3390_en10010092 crossref_primary_10_1016_j_asoc_2018_01_010 crossref_primary_10_3389_fenrg_2023_1163891 crossref_primary_10_1016_j_energy_2020_117192 crossref_primary_10_1002_ente_201900436 crossref_primary_10_3390_en16010221 crossref_primary_10_1016_j_apenergy_2019_01_024 crossref_primary_10_1109_TIA_2021_3135801 crossref_primary_10_1016_j_energy_2020_119180 crossref_primary_10_1016_j_enconman_2019_06_058 crossref_primary_10_1016_j_apenergy_2015_01_018 crossref_primary_10_1109_TPWRS_2014_2311120 crossref_primary_10_3390_wevj12010011 crossref_primary_10_1016_j_ijepes_2022_108005 crossref_primary_10_1016_j_ins_2020_03_035 crossref_primary_10_1016_j_apenergy_2014_01_003 crossref_primary_10_1109_TVT_2015_2481712 crossref_primary_10_1155_2022_7781369 crossref_primary_10_1016_j_ijepes_2019_02_020 crossref_primary_10_1016_j_trd_2024_104317 crossref_primary_10_1016_j_apenergy_2024_123547 crossref_primary_10_1080_01430750_2018_1501739 crossref_primary_10_3390_en18071639 crossref_primary_10_1038_s41598_024_81206_3 crossref_primary_10_1109_JIOT_2020_3015204 crossref_primary_10_1109_TIA_2016_2622221 crossref_primary_10_1016_j_apenergy_2012_08_021 crossref_primary_10_3390_s20174842 crossref_primary_10_1109_ACCESS_2020_2976710 crossref_primary_10_1016_j_epsr_2013_08_001 crossref_primary_10_3390_en10091333 crossref_primary_10_1016_j_eswa_2015_03_025 crossref_primary_10_1016_j_apenergy_2020_115187 crossref_primary_10_1016_j_energy_2017_10_121 crossref_primary_10_1016_j_rineng_2024_102437 crossref_primary_10_1016_j_apm_2018_07_060 crossref_primary_10_3390_en12142727 crossref_primary_10_1109_TITS_2021_3086006 crossref_primary_10_1007_s00202_017_0569_4 crossref_primary_10_1177_14680874241271819 crossref_primary_10_3390_en13236384 crossref_primary_10_1016_j_apenergy_2013_03_033 crossref_primary_10_1016_j_energy_2013_09_028 crossref_primary_10_3390_su14095098 crossref_primary_10_1016_j_energy_2020_118882 crossref_primary_10_1109_TSG_2012_2217761 crossref_primary_10_1109_TIA_2019_2954067 crossref_primary_10_1016_j_apenergy_2014_08_116 |
| Cites_doi | 10.1109/TSG.2011.2151888 10.2172/972306 10.1007/s10107-004-0559-y 10.1109/TEVC.2007.896686 10.1109/NAPS.2011.6024842 10.1109/61.637002 10.1109/IREP.2010.5563263 10.1109/PESS.2001.970272 10.1007/PL00011391 10.1109/ENERGY.2008.4781014 10.1109/PES.2011.6038937 10.1137/S1052623497325107 10.1109/TSG.2010.2090913 10.2172/903293 10.1109/DRPT.2011.5994167 10.1109/IECON.2011.6119831 10.1109/PES.2009.5275688 10.1109/ISGT.2012.6175581 |
| ContentType | Journal Article |
| Copyright | 2011 Elsevier Ltd |
| Copyright_xml | – notice: 2011 Elsevier Ltd |
| DBID | FBQ AAYXX CITATION 7ST C1K SOI 7S9 L.6 |
| DOI | 10.1016/j.apenergy.2011.11.088 |
| DatabaseName | AGRIS CrossRef Environment Abstracts Environmental Sciences and Pollution Management Environment Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Environment Abstracts Environmental Sciences and Pollution Management AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Environment Abstracts AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Environmental Sciences |
| EISSN | 1872-9118 |
| EndPage | 182 |
| ExternalDocumentID | 10_1016_j_apenergy_2011_11_088 US201600029013 S030626191100794X |
| GroupedDBID | --K --M .~1 0R~ 1B1 1~. 1~5 23M 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN AABNK AACTN AAEDT AAEDW AAHCO AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAQXK AARJD AAXUO AAYOK ABEFU ABFNM ABJNI ABMAC ABTAH ABXDB ABYKQ ACDAQ ACGFS ACNNM ACRLP ADBBV ADEZE ADMUD ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHIDL AHJVU AIEXJ AIKHN AITUG AJBFU AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ ASPBG AVWKF AXJTR AZFZN BELTK BJAXD BKOJK BLXMC CS3 EBS EFJIC EFLBG EJD EO8 EO9 EP2 EP3 FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HVGLF HZ~ IHE J1W JARJE JJJVA KOM LY6 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 R2- RIG ROL RPZ SAC SDF SDG SES SEW SPC SPCBC SSR SST SSZ T5K TN5 WUQ ZY4 ~02 ~G- ABPIF ABPTK FBQ 9DU AAHBH AATTM AAXKI AAYWO AAYXX ABWVN ACLOT ACRPL ACVFH ADCNI ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP CITATION EFKBS ~HD 7ST C1K SOI 7S9 L.6 |
| ID | FETCH-LOGICAL-c402t-b9b1b45bcd316bf5d16c073856d1890ed15cc1f4037c15b3cab9c5b886ac29a23 |
| ISICitedReferencesCount | 130 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000305595500017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0306-2619 |
| IngestDate | Sun Sep 28 08:21:13 EDT 2025 Tue Oct 07 07:59:06 EDT 2025 Tue Nov 18 22:44:00 EST 2025 Sat Nov 29 07:22:01 EST 2025 Wed Dec 27 19:17:40 EST 2023 Fri Feb 23 02:36:58 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Estimation of Distribution Algorithm (EDA) Plug-in Hybrid Electric Vehicle (PHEV) Electric Vehicle (EV) Particle Swarm Optimization (PSO) Plug-in Electric Vehicle (PEV) Smart Grid |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c402t-b9b1b45bcd316bf5d16c073856d1890ed15cc1f4037c15b3cab9c5b886ac29a23 |
| Notes | http://dx.doi.org/10.1016/j.apenergy.2011.11.088 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| PQID | 1034813363 |
| PQPubID | 23462 |
| PageCount | 12 |
| ParticipantIDs | proquest_miscellaneous_2000016520 proquest_miscellaneous_1034813363 crossref_citationtrail_10_1016_j_apenergy_2011_11_088 crossref_primary_10_1016_j_apenergy_2011_11_088 fao_agris_US201600029013 elsevier_sciencedirect_doi_10_1016_j_apenergy_2011_11_088 |
| PublicationCentury | 2000 |
| PublicationDate | 2012-08-01 |
| PublicationDateYYYYMMDD | 2012-08-01 |
| PublicationDate_xml | – month: 08 year: 2012 text: 2012-08-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationTitle | Applied energy |
| PublicationYear | 2012 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Crane, Goldthau, Toman, Light, Johnson, Nader (b0005) 2009 Su W, Chow M-Y. An intelligent energy management system for PHEVs considering demand response. In: Proc 2010 FREEDM annual conference, Tallahassee, Florida, USA; May, 2010. Parks K, Denholm P, Markel T. Cost and emissions associated with plug-in hybrid vehicle charging in the Xcel Energy Colorado Service Territory. Technical Report, National Renewable Energy Laboratory (NREL); May, 2007. Wächter, Biegler (b0160) 2006; 106 Han, Han, Sezaki (b0095) 2010; 1 Staats, Grady, Arapostathis, Thallam (b0120) 1997; 12 Su W, Zeng W, Chow M-Y. A digital testbed for a PHEV/PEV enabled parking lot in a smart grid environment. In: Proc innovative smart grid technologies (ISGT 2012), Washington DC; January 17–19, 2012. Su W, Chow M-Y. Performance evaluation of an EDA-based large-scale plug-in hybrid electric vehicle charging algorithm. In: IEEE Trans Smart Grid, special issue on transportation electrification and vehicle-to-grid application; June, 2011. Su W, Chow M-Y. Optimal energy management for a plug-in electric vehicle enabled charging infrastructure with vehicle-to-grid capability. In: Proc. the 21-th IEEE international symposium on industrial electronics, 2012, submitted for publication. Del Valle, Venayagamoorthy, Mohagheghi, Hernandez, Harley (b0170) 2008; 12 Del Valle, Venayagamoorthy, Mohagheghi, Hernandez, Harley (b0135) 2008; 12 Lozano, Larranaga, Inza, Bengoetxea (b0130) 2006 National Renewable Energy Laboratory (NREL). Using GPS travel data to access the real world driving energy use of plug-in hybrid electric vehicles (PEEVs). Byrd, Gilbert, Nocedal (b0150) 2000; 89 Byrd, Hribar, Nocedal (b0155) 1999; 9 Galus MD, Andersson G. Demand management of grid connected plug-in hybrid electric vehicles (PHEV). In: Energy 2030 conference, 2008. ENERGY 2008. IEEE; 2008. p. 1–8. Sortomme, Hindi, MacPherson, Venkata (b0090) 2011; 2 Kulshrestha P, Wang L, Chow M-Y, Lukic S. Intelligent energy management system simulator for PHEVs at municipal parking deck in a smart grid environment. In: Proc 2009 IEEE Power and Energy Society general meeting, Calgary, Canada; 2009. US Department of Transportation. Highlight of the 2001 national household travel survey (NHTS). Kulshrestha P. An intelligent energy management system for charging of plug-in hybrid electric vehicles at a municipal parking deck. Master thesis, Dept Electrical Engineering, North Carolina State University, Raleigh (NC); 2009. Su, Rahimi Eichi, Zeng, Chow (b0025) 2011 Su W, Chow M-Y. Intelligent energy scheduling at a municipal PHEV/PEV parking deck via multi-objective optimization. In: IEEE trans smart grid, special issue on computational intelligence applications in Smart Grids; 2011 [Extended Abstract Accepted]. Guille C, Gross G. The integration of PHEV aggregation into a power system with wind resources. In: Bulk power system dynamics and control, IREP symposium, Buzios, RJ, Brazil; August 1–6, 2010. City of Livermore. Downtown Parking Study. Livermore, CA; February 2006. . Su W, Chow M-Y. Sensitivity analysis on battery modeling to large-scale charging algorithms. In: Proc 37th annual conference of the IEEE industrial electronics society, Melbourne, Australia; November 7–10, 2011. Larranaga, Lozano (b0125) 2002 Su W, Chow M-Y. Evaluation on intelligent energy management system for PHEVs/PEVs using Monte Carlo method. In: Proceedings of 4th international conference on electric utility deregulation and restructuring and power technologies (DRPT2011), Shandong, China; July 6–9, 2011. Clement-Nyns, Haesen, Driesen (b0085) 2010; 25 Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE international conference on neural networks, Perth, WA; November, 1995. Su W, Chow M-Y. Investigating a large-scale PHEV/PEV parking deck in a smart grid environment. In: Proc 43rd North American power symposium, Boston, MA; August, 2011. Wächter Andreas. Short tutorials: getting started with Ipopt in 90 minutes. IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, IBM Research Report. Abido MA. Particle swarm optimization for multi-machine power system stabilizer design. In: Proc IEEE PES summer meeting, vol. 3; 2001. p. 1346–1351. Sikes K, Gross T, Lin Z, Sullivan J, Cleary T, Ward J. Plug-in hybrid electric vehicle market introduction study: final report. ORNL/TM-2009/019. US Department of Energy; 2010. Duvall M, Knipping E. Environmental assessment of plug-in hybrid electric vehicles. EPRI; July, 2007. Su W, Chow M-Y. Performance evaluation of A PHEV parking station using particle swarm optimization. In: Proc 2011 IEEE power and energy society general meeting, Detroit, Michigan, USA; July 24–29, 2011. 10.1016/j.apenergy.2011.11.088_b0040 10.1016/j.apenergy.2011.11.088_b0140 10.1016/j.apenergy.2011.11.088_b0060 10.1016/j.apenergy.2011.11.088_b0165 10.1016/j.apenergy.2011.11.088_b0045 10.1016/j.apenergy.2011.11.088_b0100 10.1016/j.apenergy.2011.11.088_b0020 Han (10.1016/j.apenergy.2011.11.088_b0095) 2010; 1 10.1016/j.apenergy.2011.11.088_b0065 10.1016/j.apenergy.2011.11.088_b0080 Byrd (10.1016/j.apenergy.2011.11.088_b0150) 2000; 89 Del Valle (10.1016/j.apenergy.2011.11.088_b0135) 2008; 12 10.1016/j.apenergy.2011.11.088_b0015 Crane (10.1016/j.apenergy.2011.11.088_b0005) 2009 10.1016/j.apenergy.2011.11.088_b0115 10.1016/j.apenergy.2011.11.088_b0035 Del Valle (10.1016/j.apenergy.2011.11.088_b0170) 2008; 12 Lozano (10.1016/j.apenergy.2011.11.088_b0130) 2006 10.1016/j.apenergy.2011.11.088_b0030 10.1016/j.apenergy.2011.11.088_b0050 10.1016/j.apenergy.2011.11.088_b0055 10.1016/j.apenergy.2011.11.088_b0110 Larranaga (10.1016/j.apenergy.2011.11.088_b0125) 2002 10.1016/j.apenergy.2011.11.088_b0075 10.1016/j.apenergy.2011.11.088_b0010 10.1016/j.apenergy.2011.11.088_b0175 Sortomme (10.1016/j.apenergy.2011.11.088_b0090) 2011; 2 10.1016/j.apenergy.2011.11.088_b0070 Clement-Nyns (10.1016/j.apenergy.2011.11.088_b0085) 2010; 25 Wächter (10.1016/j.apenergy.2011.11.088_b0160) 2006; 106 Su (10.1016/j.apenergy.2011.11.088_b0025) 2011 Staats (10.1016/j.apenergy.2011.11.088_b0120) 1997; 12 10.1016/j.apenergy.2011.11.088_b0145 Byrd (10.1016/j.apenergy.2011.11.088_b0155) 1999; 9 10.1016/j.apenergy.2011.11.088_b0105 |
| References_xml | – volume: 1 year: 2010 ident: b0095 article-title: Development of an optimal vehicle-to-grid aggregator for frequency regulation publication-title: IEEE Trans Smart Grid – reference: Parks K, Denholm P, Markel T. Cost and emissions associated with plug-in hybrid vehicle charging in the Xcel Energy Colorado Service Territory. Technical Report, National Renewable Energy Laboratory (NREL); May, 2007. – reference: Su W, Chow M-Y. Investigating a large-scale PHEV/PEV parking deck in a smart grid environment. In: Proc 43rd North American power symposium, Boston, MA; August, 2011. – reference: Su W, Chow M-Y. An intelligent energy management system for PHEVs considering demand response. In: Proc 2010 FREEDM annual conference, Tallahassee, Florida, USA; May, 2010. – year: 2002 ident: b0125 article-title: Estimation of Distribution Algorithm: a new tool for evolutionary computation – reference: Guille C, Gross G. The integration of PHEV aggregation into a power system with wind resources. In: Bulk power system dynamics and control, IREP symposium, Buzios, RJ, Brazil; August 1–6, 2010. – reference: Su W, Chow M-Y. Evaluation on intelligent energy management system for PHEVs/PEVs using Monte Carlo method. In: Proceedings of 4th international conference on electric utility deregulation and restructuring and power technologies (DRPT2011), Shandong, China; July 6–9, 2011. – volume: 2 start-page: 198 year: 2011 end-page: 205 ident: b0090 article-title: Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses publication-title: Smart Grid IEEE Trans – year: 2009 ident: b0005 article-title: Imported oil and US national security – volume: 106 start-page: 25 year: 2006 end-page: 57 ident: b0160 article-title: On the implementation of a primal–dual interior point filter line search algorithm for large-scale nonlinear programming publication-title: Math Program – reference: Galus MD, Andersson G. Demand management of grid connected plug-in hybrid electric vehicles (PHEV). In: Energy 2030 conference, 2008. ENERGY 2008. IEEE; 2008. p. 1–8. – reference: Kulshrestha P. An intelligent energy management system for charging of plug-in hybrid electric vehicles at a municipal parking deck. Master thesis, Dept Electrical Engineering, North Carolina State University, Raleigh (NC); 2009. – reference: National Renewable Energy Laboratory (NREL). Using GPS travel data to access the real world driving energy use of plug-in hybrid electric vehicles (PEEVs). < – reference: Su W, Chow M-Y. Performance evaluation of an EDA-based large-scale plug-in hybrid electric vehicle charging algorithm. In: IEEE Trans Smart Grid, special issue on transportation electrification and vehicle-to-grid application; June, 2011. – year: 2011 ident: b0025 article-title: A survey on the electrification of transportation in a smart grid environment publication-title: IEEE Trans Indust Inform – reference: Su W, Zeng W, Chow M-Y. A digital testbed for a PHEV/PEV enabled parking lot in a smart grid environment. In: Proc innovative smart grid technologies (ISGT 2012), Washington DC; January 17–19, 2012. – reference: Abido MA. Particle swarm optimization for multi-machine power system stabilizer design. In: Proc IEEE PES summer meeting, vol. 3; 2001. p. 1346–1351. – volume: 12 year: 2008 ident: b0135 article-title: Particle swarm optimization: basic concepts, variants and applications in power systems publication-title: IEEE Trans Evolut Comput – reference: Duvall M, Knipping E. Environmental assessment of plug-in hybrid electric vehicles. EPRI; July, 2007. < – reference: Su W, Chow M-Y. Performance evaluation of A PHEV parking station using particle swarm optimization. In: Proc 2011 IEEE power and energy society general meeting, Detroit, Michigan, USA; July 24–29, 2011. – reference: Su W, Chow M-Y. Intelligent energy scheduling at a municipal PHEV/PEV parking deck via multi-objective optimization. In: IEEE trans smart grid, special issue on computational intelligence applications in Smart Grids; 2011 [Extended Abstract Accepted]. – volume: 12 start-page: 1258 year: 1997 end-page: 1266 ident: b0120 article-title: A statistical method for predicting net harmonic current generated by a concentration of electric vehicle battery chargers publication-title: IEEE Trans Power Deliv – volume: 12 year: 2008 ident: b0170 article-title: Particle swarm optimization: basic concepts, variants and applications in power systems publication-title: IEEE Trans Evolut Comput – reference: >. – reference: Wächter Andreas. Short tutorials: getting started with Ipopt in 90 minutes. IBM Research Division, Thomas J. Watson Research Center, Yorktown Heights, NY, 10598, IBM Research Report. – reference: Sikes K, Gross T, Lin Z, Sullivan J, Cleary T, Ward J. Plug-in hybrid electric vehicle market introduction study: final report. ORNL/TM-2009/019. US Department of Energy; 2010. – volume: 25 start-page: 371 year: 2010 end-page: 380 ident: b0085 article-title: The impact of charging plug-in hybrid electric vehicles on a residential distribution, grid power systems publication-title: IEEE Trans – volume: 9 start-page: 877 year: 1999 end-page: 900 ident: b0155 article-title: An interior point algorithm for large-scale nonlinear programming publication-title: SIAM J Optim – reference: Kulshrestha P, Wang L, Chow M-Y, Lukic S. Intelligent energy management system simulator for PHEVs at municipal parking deck in a smart grid environment. In: Proc 2009 IEEE Power and Energy Society general meeting, Calgary, Canada; 2009. – reference: Su W, Chow M-Y. Optimal energy management for a plug-in electric vehicle enabled charging infrastructure with vehicle-to-grid capability. In: Proc. the 21-th IEEE international symposium on industrial electronics, 2012, submitted for publication. – year: 2006 ident: b0130 article-title: Towards a new evolutionary computation: advances on estimation of distribution algorithms – reference: Kennedy J, Eberhart R. Particle swarm optimization. In: IEEE international conference on neural networks, Perth, WA; November, 1995. – reference: Su W, Chow M-Y. Sensitivity analysis on battery modeling to large-scale charging algorithms. In: Proc 37th annual conference of the IEEE industrial electronics society, Melbourne, Australia; November 7–10, 2011. – reference: US Department of Transportation. Highlight of the 2001 national household travel survey (NHTS). < – reference: City of Livermore. Downtown Parking Study. Livermore, CA; February 2006. < – volume: 89 start-page: 149 year: 2000 end-page: 185 ident: b0150 article-title: A trust region method based on interior point techniques for nonlinear programming publication-title: Math Program – ident: 10.1016/j.apenergy.2011.11.088_b0045 – ident: 10.1016/j.apenergy.2011.11.088_b0070 – ident: 10.1016/j.apenergy.2011.11.088_b0060 doi: 10.1109/TSG.2011.2151888 – ident: 10.1016/j.apenergy.2011.11.088_b0015 doi: 10.2172/972306 – volume: 106 start-page: 25 issue: 1 year: 2006 ident: 10.1016/j.apenergy.2011.11.088_b0160 article-title: On the implementation of a primal–dual interior point filter line search algorithm for large-scale nonlinear programming publication-title: Math Program doi: 10.1007/s10107-004-0559-y – volume: 12 issue: 2 year: 2008 ident: 10.1016/j.apenergy.2011.11.088_b0135 article-title: Particle swarm optimization: basic concepts, variants and applications in power systems publication-title: IEEE Trans Evolut Comput doi: 10.1109/TEVC.2007.896686 – ident: 10.1016/j.apenergy.2011.11.088_b0030 doi: 10.1109/NAPS.2011.6024842 – volume: 12 start-page: 1258 issue: 3 year: 1997 ident: 10.1016/j.apenergy.2011.11.088_b0120 article-title: A statistical method for predicting net harmonic current generated by a concentration of electric vehicle battery chargers publication-title: IEEE Trans Power Deliv doi: 10.1109/61.637002 – ident: 10.1016/j.apenergy.2011.11.088_b0100 doi: 10.1109/IREP.2010.5563263 – volume: 12 issue: 2 year: 2008 ident: 10.1016/j.apenergy.2011.11.088_b0170 article-title: Particle swarm optimization: basic concepts, variants and applications in power systems publication-title: IEEE Trans Evolut Comput doi: 10.1109/TEVC.2007.896686 – year: 2006 ident: 10.1016/j.apenergy.2011.11.088_b0130 – ident: 10.1016/j.apenergy.2011.11.088_b0145 doi: 10.1109/PESS.2001.970272 – volume: 89 start-page: 149 issue: 1 year: 2000 ident: 10.1016/j.apenergy.2011.11.088_b0150 article-title: A trust region method based on interior point techniques for nonlinear programming publication-title: Math Program doi: 10.1007/PL00011391 – ident: 10.1016/j.apenergy.2011.11.088_b0080 doi: 10.1109/ENERGY.2008.4781014 – ident: 10.1016/j.apenergy.2011.11.088_b0110 – ident: 10.1016/j.apenergy.2011.11.088_b0175 – ident: 10.1016/j.apenergy.2011.11.088_b0020 – ident: 10.1016/j.apenergy.2011.11.088_b0055 doi: 10.1109/PES.2011.6038937 – volume: 9 start-page: 877 issue: 4 year: 1999 ident: 10.1016/j.apenergy.2011.11.088_b0155 article-title: An interior point algorithm for large-scale nonlinear programming publication-title: SIAM J Optim doi: 10.1137/S1052623497325107 – volume: 2 start-page: 198 year: 2011 ident: 10.1016/j.apenergy.2011.11.088_b0090 article-title: Coordinated charging of plug-in hybrid electric vehicles to minimize distribution system losses publication-title: Smart Grid IEEE Trans doi: 10.1109/TSG.2010.2090913 – ident: 10.1016/j.apenergy.2011.11.088_b0105 – ident: 10.1016/j.apenergy.2011.11.088_b0075 – volume: 25 start-page: 371 year: 2010 ident: 10.1016/j.apenergy.2011.11.088_b0085 article-title: The impact of charging plug-in hybrid electric vehicles on a residential distribution, grid power systems publication-title: IEEE Trans – ident: 10.1016/j.apenergy.2011.11.088_b0140 – year: 2002 ident: 10.1016/j.apenergy.2011.11.088_b0125 – ident: 10.1016/j.apenergy.2011.11.088_b0010 doi: 10.2172/903293 – ident: 10.1016/j.apenergy.2011.11.088_b0165 – year: 2011 ident: 10.1016/j.apenergy.2011.11.088_b0025 article-title: A survey on the electrification of transportation in a smart grid environment publication-title: IEEE Trans Indust Inform – volume: 1 issue: 1 year: 2010 ident: 10.1016/j.apenergy.2011.11.088_b0095 article-title: Development of an optimal vehicle-to-grid aggregator for frequency regulation publication-title: IEEE Trans Smart Grid – ident: 10.1016/j.apenergy.2011.11.088_b0040 doi: 10.1109/DRPT.2011.5994167 – ident: 10.1016/j.apenergy.2011.11.088_b0115 – ident: 10.1016/j.apenergy.2011.11.088_b0050 doi: 10.1109/IECON.2011.6119831 – year: 2009 ident: 10.1016/j.apenergy.2011.11.088_b0005 – ident: 10.1016/j.apenergy.2011.11.088_b0035 doi: 10.1109/PES.2009.5275688 – ident: 10.1016/j.apenergy.2011.11.088_b0065 doi: 10.1109/ISGT.2012.6175581 |
| SSID | ssj0002120 |
| Score | 2.4347801 |
| Snippet | ► Describe the mathematical framework for large-scale PHEV/PEV charging control. ► Manage the highly concentrated PHEV chargers considering real-world... There is a growing need to address the potential problems caused by the emergence of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles... |
| SourceID | proquest crossref fao elsevier |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 171 |
| SubjectTerms | algorithms artificial intelligence Electric Vehicle (EV) electricity energy energy resources Estimation of Distribution Algorithm (EDA) infrastructure markets Particle Swarm Optimization (PSO) Plug-in Electric Vehicle (PEV) Plug-in Hybrid Electric Vehicle (PHEV) Smart Grid society vehicles (equipment) |
| Title | Computational intelligence-based energy management for a large-scale PHEV/PEV enabled municipal parking deck |
| URI | https://dx.doi.org/10.1016/j.apenergy.2011.11.088 https://www.proquest.com/docview/1034813363 https://www.proquest.com/docview/2000016520 |
| Volume | 96 |
| WOSCitedRecordID | wos000305595500017&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-9118 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002120 issn: 0306-2619 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3Nb9MwFLe6jQMcJhhMKwNkJMSlMkuc5sPHCWUaUEol2qmcLNtxpk4lLWs77c_nObbboAIbBy5RFdmJld-v7z37fSH0RqUUjFARAAKZJF2dasIiLUgpmTBdVKNI1YnCvbTfz8ZjNmi1PvlcmJtpWlXZ7S2b_1eo4R6AbVJn_wHu9UPhBvwG0OEKsMP1XsDbPg3-jG_SKLlJjMoqOtqm-31fB77YSMrO1ASFkwWApjuD8_wCljDIL2C4ya4qOnUeyWRuMrdEfcDeKbSrqO-r2DqL1r5g426q4_hMwUynJetoAutT-jwj31YuG82dPZggjsyfPbicqyAhZg_WlKesKRBD22DF6dbQNhraEtv2BOHqnZjbFdrSqnV11WyjqLxzvv-Fn416PT7Mx8O38x_EtBAzrnbXT2UH7dE0ZiCl904_5OOPa8VMXZVOv-hGwvjvX_0nW2WnFLMt3V0bJMPHaN_tJPCpZcAT1NLVAXrUqC95gA7zTRojDHVyfPEUTX8hCd4mCbbLxBuSYCAJFrhBEmxIcgIUwY4ieE0R7CiCDUWeodFZPnx_TlzfDaK6AV0SyWQou7FURRQmsoyLMFGBqXqUFGHGAl2EsVJh2Q2iVIWxjJSQTMUyyxKhKBM0OkS71azSRwhnZVKKMqYiiksTEiCoUEFSpqabpQTp0Eax_8JcuaL0pjfKlPvowyvukeEGGdixckCmjU7W8-a2LMudM5gHkDvj0hqNHEh459wjQJyLS9C7fPSVmqqMxp0N26c2eu1pwEEwG2-bqPRstYDnmRz3KEr-MobWzrUkpsHzezznGD3c_AlfoN3l9Uq_RA_UzXKyuH7l2P4TMpa9NQ |
| linkProvider | Elsevier |
| 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=Computational+intelligence-based+energy+management+for+a+large-scale+PHEV%2FPEV+enabled+municipal+parking+deck&rft.jtitle=Applied+energy&rft.au=Su%2C+Wencong&rft.au=Chow%2C+Mo-Yuen&rft.date=2012-08-01&rft.issn=0306-2619&rft.volume=96&rft.spage=171&rft.epage=182&rft_id=info:doi/10.1016%2Fj.apenergy.2011.11.088&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0306-2619&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0306-2619&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0306-2619&client=summon |