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V2G Optimization Strategy Based on the Cuckoo Optimization Algorithm from the Perspective of a Multi-Party Cooperative Game.

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Title: V2G Optimization Strategy Based on the Cuckoo Optimization Algorithm from the Perspective of a Multi-Party Cooperative Game.
Authors: Li, Zhuoqun1 (AUTHOR), Liu, Xianglu1,2 (AUTHOR), Qiu, Shi2,3 (AUTHOR) 15651781199@163.com, Sun, Zhou1 (AUTHOR), Wan, Yi1,2 (AUTHOR), Zhao, Yongliang3 (AUTHOR), Chen, Fei2 (AUTHOR), Zhang, Xu2 (AUTHOR), Gong, Gangjun2 (AUTHOR)
Source: Energies (19961073). May2026, Vol. 19 Issue 10, p2289. 25p.
Subject Terms: *Multi-objective optimization, *Optimization algorithms, *Game theory, *Electric vehicle charging stations, *Electric power system stability
Abstract: This paper comprehensively considers the interest demands of three core stakeholders in V2G scenarios: electric vehicle (EV) users, the power grid, and electric vehicle aggregators (EVAs). EV users prioritize charging waiting time and queuing probability to improve travel experience; the power grid focuses on charging facility utilization and power supply reliability to maximize operational benefits; and the EVA concerns its own load level and charging/discharging pricing strategies to optimize operating income. A tripartite multi-objective optimization model for grid–EV–EVA-coordinated charging and discharging is constructed, and an improved multi-objective cuckoo search algorithm is proposed to solve the model. The algorithm integrates an iterative search process (initialization, Lévy flight search, nest abandonment and update) and a cooperative game process (iteration, convergence conditions, equilibrium implementation). Guided by the dominant strength law, the algorithm's Pareto-optimal solution set is ranked. Finally, a V2G collaborative optimization strategy that balances the interests of all stakeholders is obtained, which can effectively reduce EV users' charging waiting time, improve the utilization rate of grid charging facilities, and guarantee the static voltage stability of the distribution network. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 194141404
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Label: Title
  Group: Ti
  Data: V2G Optimization Strategy Based on the Cuckoo Optimization Algorithm from the Perspective of a Multi-Party Cooperative Game.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Zhuoqun%22">Li, Zhuoqun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Xianglu%22">Liu, Xianglu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Qiu%2C+Shi%22">Qiu, Shi</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> 15651781199@163.com</i><br /><searchLink fieldCode="AR" term="%22Sun%2C+Zhou%22">Sun, Zhou</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wan%2C+Yi%22">Wan, Yi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yongliang%22">Zhao, Yongliang</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Fei%22">Chen, Fei</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Xu%22">Zhang, Xu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gong%2C+Gangjun%22">Gong, Gangjun</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2289. 25p.
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  Data: *<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Game+theory%22">Game theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+vehicle+charging+stations%22">Electric vehicle charging stations</searchLink><br />*<searchLink fieldCode="DE" term="%22Electric+power+system+stability%22">Electric power system stability</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper comprehensively considers the interest demands of three core stakeholders in V2G scenarios: electric vehicle (EV) users, the power grid, and electric vehicle aggregators (EVAs). EV users prioritize charging waiting time and queuing probability to improve travel experience; the power grid focuses on charging facility utilization and power supply reliability to maximize operational benefits; and the EVA concerns its own load level and charging/discharging pricing strategies to optimize operating income. A tripartite multi-objective optimization model for grid–EV–EVA-coordinated charging and discharging is constructed, and an improved multi-objective cuckoo search algorithm is proposed to solve the model. The algorithm integrates an iterative search process (initialization, Lévy flight search, nest abandonment and update) and a cooperative game process (iteration, convergence conditions, equilibrium implementation). Guided by the dominant strength law, the algorithm's Pareto-optimal solution set is ranked. Finally, a V2G collaborative optimization strategy that balances the interests of all stakeholders is obtained, which can effectively reduce EV users' charging waiting time, improve the utilization rate of grid charging facilities, and guarantee the static voltage stability of the distribution network. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141404
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        Value: 10.3390/en19102289
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        Text: English
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      – SubjectFull: Game theory
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      – SubjectFull: Electric vehicle charging stations
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      – SubjectFull: Electric power system stability
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      – TitleFull: V2G Optimization Strategy Based on the Cuckoo Optimization Algorithm from the Perspective of a Multi-Party Cooperative Game.
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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