Transactive Charging Control of Electric Vehicles Considering Voltage Unbalance and Transformers' Loss of Life
This paper presents a novel transactive energy (TE) management model for real-time coordination of plug-in electric vehicles (PEVs) in the distribution network. The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and d...
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Veröffentlicht in: | IEEE transactions on smart grid 2023-05, Vol.14 (3), p.2226-2235 |
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description | This paper presents a novel transactive energy (TE) management model for real-time coordination of plug-in electric vehicles (PEVs) in the distribution network. The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers' loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model. |
doi_str_mv | 10.1109/TSG.2022.3213764 |
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The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers' loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model.</description><identifier>ISSN: 1949-3053</identifier><identifier>EISSN: 1949-3061</identifier><identifier>DOI: 10.1109/TSG.2022.3213764</identifier><identifier>CODEN: ITSGBQ</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Costs ; Electric potential ; electric vehicle ; Electric vehicles ; Electrical plugs ; Flexibility ; Optimization ; Power transformer insulation ; Predictive control ; Real-time energy management ; Real-time systems ; transactive energy ; transformer loss of life ; Transformers ; Unbalance ; Uncertainty ; Voltage ; Voltage control ; voltage unbalance</subject><ispartof>IEEE transactions on smart grid, 2023-05, Vol.14 (3), p.2226-2235</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c244t-a9ef49a1b5f448bc9fd3d076fbbde4d73745353f9f0af1bbcab8602c8a7fc9fa3</cites><orcidid>0000-0002-9028-6449 ; 0000-0001-9363-5571 ; 0000-0002-7895-5646</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9925638$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9925638$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Ranjbar, Hossein</creatorcontrib><creatorcontrib>Saber, Hossein</creatorcontrib><creatorcontrib>Sharifzadeh, Mahdi</creatorcontrib><title>Transactive Charging Control of Electric Vehicles Considering Voltage Unbalance and Transformers' Loss of Life</title><title>IEEE transactions on smart grid</title><addtitle>TSG</addtitle><description>This paper presents a novel transactive energy (TE) management model for real-time coordination of plug-in electric vehicles (PEVs) in the distribution network. The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers' loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model.</description><subject>Costs</subject><subject>Electric potential</subject><subject>electric vehicle</subject><subject>Electric vehicles</subject><subject>Electrical plugs</subject><subject>Flexibility</subject><subject>Optimization</subject><subject>Power transformer insulation</subject><subject>Predictive control</subject><subject>Real-time energy management</subject><subject>Real-time systems</subject><subject>transactive energy</subject><subject>transformer loss of life</subject><subject>Transformers</subject><subject>Unbalance</subject><subject>Uncertainty</subject><subject>Voltage</subject><subject>Voltage control</subject><subject>voltage unbalance</subject><issn>1949-3053</issn><issn>1949-3061</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1LAzEQxRdRsGjvgpeAB09b87UfOcpSq7DgwbbXkGQn7ZZtUpOt4H_vri2dywzM770ZXpI8EDwjBIuX5ddiRjGlM0YJK3J-lUyI4CJlOCfXlzljt8k0xh0eijGWUzFJ3DIoF5Xp2x9A1VaFTes2qPKuD75D3qJ5B6YPrUFr2LamgzguY9tAGMG173q1AbRyWnXKGUDKNejf0_qwhxCfUe1jHJ3q1sJ9cmNVF2F67nfJ6m2-rN7T-nPxUb3WqaGc96kSYLlQRGeW81IbYRvW4CK3WjfAm4IVPGMZs8JiZYnWRukyx9SUqrADrNhd8nTyPQT_fYTYy50_BjeclLTEnBBGSD5Q-ESZMPwYwMpDaPcq_EqC5RisHIKVY7DyHOwgeTxJWgC44ELQLGcl-wMe5HW7</recordid><startdate>20230501</startdate><enddate>20230501</enddate><creator>Ranjbar, Hossein</creator><creator>Saber, Hossein</creator><creator>Sharifzadeh, Mahdi</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The proposed model employs the flexibility of PEV charging through a TE market platform to reduce the total cost, voltage unbalances, and distribution transformers' loss of life. In the proposed TE market model, the PEV owners send the information of the required reimbursement for the response and the target level of state-of-charge at departure to the retailer. Then, the retailer runs an optimization problem to determine the scheduling of PEVs by minimizing the total cost including the cost of exchanging energy with upstream grid and payment to PEVs to use their flexibility. Besides, the model predictive control approach is used to effectively integrate the upcoming system operation conditions and dealing with system uncertainties. Moreover, linearization techniques are employed to reduce the computational complexity and improve the precision of the solutions. Finally, the proposed framework is applied to the unbalanced IEEE 33-bus distribution network, and the numerical analyses illustrate the effectiveness of the proposed model.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/TSG.2022.3213764</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-9028-6449</orcidid><orcidid>https://orcid.org/0000-0001-9363-5571</orcidid><orcidid>https://orcid.org/0000-0002-7895-5646</orcidid></addata></record> |
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subjects | Costs Electric potential electric vehicle Electric vehicles Electrical plugs Flexibility Optimization Power transformer insulation Predictive control Real-time energy management Real-time systems transactive energy transformer loss of life Transformers Unbalance Uncertainty Voltage Voltage control voltage unbalance |
title | Transactive Charging Control of Electric Vehicles Considering Voltage Unbalance and Transformers' Loss of Life |
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