An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids
This article presents an electric vehicle (EV)-integrated security constrained unit commitment (EV-SCUC) based on N-1 criteria for single line outage contingency under intermittency of load and renewable energy sources (RESs). Two EV control mechanisms are proposed. In first, EV balance control, fin...
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| Veröffentlicht in: | IEEE transactions on industrial informatics 2024-03, Vol.20 (3), p.1-11 |
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| container_title | IEEE transactions on industrial informatics |
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| creator | Akbari, Amirhossein Alavi-Koosha, Ahmadreza Moradi-Sepahvand, Mojtaba Toulabi, Mohammadreza Amraee, Turaj Bathaee, Seyyed Mohammad Taghi |
| description | This article presents an electric vehicle (EV)-integrated security constrained unit commitment (EV-SCUC) based on N-1 criteria for single line outage contingency under intermittency of load and renewable energy sources (RESs). Two EV control mechanisms are proposed. In first, EV balance control, financial targets are prioritized while flexibility is provided for the power network. In second control, after derivation of cumulative power transfer distribution factor (CPTDF) from power network, a CPTDF-based EV control is modeled toward an incentive-based congestion criteria management. The uncertainties of solar, wind, and load are captured by a hybrid chronological time-period clustering algorithm with Monte Carlo method. The performance of the proposed EV-SCUC model is tested on the IEEE 6-bus and the IEEE One and Two Area RTS-96 systems with integrated EV fleets. Under EV spatiotemporal constraints, results confirm that the system operator can decisively control commitment decisions, total cost, incentive payments, and pre/postcontingency congestion criteria under different EV control parameters and RES penetration deterministically or stochastically. |
| doi_str_mv | 10.1109/TII.2023.3316755 |
| format | Article |
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Two EV control mechanisms are proposed. In first, EV balance control, financial targets are prioritized while flexibility is provided for the power network. In second control, after derivation of cumulative power transfer distribution factor (CPTDF) from power network, a CPTDF-based EV control is modeled toward an incentive-based congestion criteria management. The uncertainties of solar, wind, and load are captured by a hybrid chronological time-period clustering algorithm with Monte Carlo method. The performance of the proposed EV-SCUC model is tested on the IEEE 6-bus and the IEEE One and Two Area RTS-96 systems with integrated EV fleets. Under EV spatiotemporal constraints, results confirm that the system operator can decisively control commitment decisions, total cost, incentive payments, and pre/postcontingency congestion criteria under different EV control parameters and RES penetration deterministically or stochastically.</description><identifier>ISSN: 1551-3203</identifier><identifier>EISSN: 1941-0050</identifier><identifier>DOI: 10.1109/TII.2023.3316755</identifier><identifier>CODEN: ITIICH</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Algorithms ; Clustering ; Congestion ; Constraints ; Contingency ; Contingency management ; Costs ; Criteria ; Discharges (electric) ; Electric vehicles ; Electric vehicles (EVs) ; Generators ; incentive mechanism ; Monte Carlo simulation ; Power systems ; Power transfer distribution factor ; Renewable energy sources ; Security ; security constrained unit commitment (SCUC) ; Sensitivity ; sensitivity factors ; Smart grid ; Uncertainty ; Unit commitment</subject><ispartof>IEEE transactions on industrial informatics, 2024-03, Vol.20 (3), p.1-11</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c245t-71ace448068d46fdc816c840d1b37c00729127825972f658efba019c5a6fa19c3</cites><orcidid>0000-0002-6131-0219 ; 0000-0003-3800-3541 ; 0000-0002-5198-0067 ; 0000-0003-3909-9952 ; 0000-0002-6531-1665 ; 0000-0003-0494-3930</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10265294$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10265294$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Akbari, Amirhossein</creatorcontrib><creatorcontrib>Alavi-Koosha, Ahmadreza</creatorcontrib><creatorcontrib>Moradi-Sepahvand, Mojtaba</creatorcontrib><creatorcontrib>Toulabi, Mohammadreza</creatorcontrib><creatorcontrib>Amraee, Turaj</creatorcontrib><creatorcontrib>Bathaee, Seyyed Mohammad Taghi</creatorcontrib><title>An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids</title><title>IEEE transactions on industrial informatics</title><addtitle>TII</addtitle><description>This article presents an electric vehicle (EV)-integrated security constrained unit commitment (EV-SCUC) based on <inline-formula><tex-math notation="LaTeX">N-1</tex-math></inline-formula> criteria for single line outage contingency under intermittency of load and renewable energy sources (RESs). Two EV control mechanisms are proposed. In first, EV balance control, financial targets are prioritized while flexibility is provided for the power network. In second control, after derivation of cumulative power transfer distribution factor (CPTDF) from power network, a CPTDF-based EV control is modeled toward an incentive-based congestion criteria management. The uncertainties of solar, wind, and load are captured by a hybrid chronological time-period clustering algorithm with Monte Carlo method. The performance of the proposed EV-SCUC model is tested on the IEEE 6-bus and the IEEE One and Two Area RTS-96 systems with integrated EV fleets. Under EV spatiotemporal constraints, results confirm that the system operator can decisively control commitment decisions, total cost, incentive payments, and pre/postcontingency congestion criteria under different EV control parameters and RES penetration deterministically or stochastically.</description><subject>Algorithms</subject><subject>Clustering</subject><subject>Congestion</subject><subject>Constraints</subject><subject>Contingency</subject><subject>Contingency management</subject><subject>Costs</subject><subject>Criteria</subject><subject>Discharges (electric)</subject><subject>Electric vehicles</subject><subject>Electric vehicles (EVs)</subject><subject>Generators</subject><subject>incentive mechanism</subject><subject>Monte Carlo simulation</subject><subject>Power systems</subject><subject>Power transfer distribution factor</subject><subject>Renewable energy sources</subject><subject>Security</subject><subject>security constrained unit commitment (SCUC)</subject><subject>Sensitivity</subject><subject>sensitivity factors</subject><subject>Smart grid</subject><subject>Uncertainty</subject><subject>Unit commitment</subject><issn>1551-3203</issn><issn>1941-0050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpNkM1OwzAQhCMEEqVw58DBEueUtR3n51hVpUQqAtGWa-Q6G-oqcYrtgvoCPDeuyoHTrFYzs6svim4pjCiF4mFZliMGjI84p2kmxFk0oEVCYwAB52EWgsacAb-MrpzbAvAMeDGIfsaGlEah8foLyTOqjTTadaTpLZm2qLzVirzjRqsWHXmV1muld9Lr3hBtyALV3mp_IJPeOG-lNliTldE-LLpO-y4Uk74hb2jwW65bjEvj8cNKH3yLLtSRmdW1u44uGtk6vPnTYbR6nC4nT_H8ZVZOxvNYsUT4OKNSYZLkkOZ1kja1ymmq8gRquuaZAshYQVmWM1FkrElFjs1aAi2UkGkjg_JhdH_q3dn-c4_OV9t-b004WbGC54Gf4BBccHIp2ztnsal2VodnDxWF6ki7CrSrI-3qj3aI3J0iGhH_2VkqWJHwX85ffH0</recordid><startdate>20240301</startdate><enddate>20240301</enddate><creator>Akbari, Amirhossein</creator><creator>Alavi-Koosha, Ahmadreza</creator><creator>Moradi-Sepahvand, Mojtaba</creator><creator>Toulabi, Mohammadreza</creator><creator>Amraee, Turaj</creator><creator>Bathaee, Seyyed Mohammad Taghi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Two EV control mechanisms are proposed. In first, EV balance control, financial targets are prioritized while flexibility is provided for the power network. In second control, after derivation of cumulative power transfer distribution factor (CPTDF) from power network, a CPTDF-based EV control is modeled toward an incentive-based congestion criteria management. The uncertainties of solar, wind, and load are captured by a hybrid chronological time-period clustering algorithm with Monte Carlo method. The performance of the proposed EV-SCUC model is tested on the IEEE 6-bus and the IEEE One and Two Area RTS-96 systems with integrated EV fleets. 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| subjects | Algorithms Clustering Congestion Constraints Contingency Contingency management Costs Criteria Discharges (electric) Electric vehicles Electric vehicles (EVs) Generators incentive mechanism Monte Carlo simulation Power systems Power transfer distribution factor Renewable energy sources Security security constrained unit commitment (SCUC) Sensitivity sensitivity factors Smart grid Uncertainty Unit commitment |
| title | An Incentive Mechanism for Electric Vehicles Participation in Security Constrained Unit Commitment of Renewable-Integrated Smart Grids |
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