Intelligent control of vehicle to grid power

Khayyam, Hamid; Ranjbarzadeh, Hassan; Marano, Vincenzo

doi:10.1016/j.jpowsour.2011.11.010

Cited by 80 publications

(36 citation statements)

References 10 publications

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“…In fact, it is not only good information flow about the market side which is important for best use of electric vehicles but also for the actual power quality in the grid. Khayyam et al [24], for example, found that a smart grid improved voltage stability significantly over standard charging control of plug-in vehicles.…”

Section: Discussionmentioning

confidence: 99%

Simulation of demand management and grid balancing with electric vehicles

Druitt

Früh

2012

Journal of Power Sources

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Section: Discussionmentioning

confidence: 99%

Simulation of demand management and grid balancing with electric vehicles

Druitt

Früh

2012

Journal of Power Sources

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“…These include depth of discharge, discharge rate, ambient temperature, charging regime, battery maintenance procedures, etc. Lithium-ion seems an ideal material for a battery [21]. All time stages used here are set to be one hour.…”

Section: Battery Characteristics Of Phevsmentioning

confidence: 99%

Microgrid economic operation considering plug-in hybrid electric vehicles integration

Chen

Duan

2015

J. Mod. Power Syst. Clean Energy

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In this paper, the microgrid economic scheduling mathematical model considering the integration of plug-in hybrid electric vehicles (PHEVs) is presented and the influence of different charging and discharging modes on microgrid economic operation is analyzed. The generic algorithm is used to find an economically optimal solution for the microgrid and PHEV owners. The scheduling of PHEVs and the microgrid are optimized to reduce daily electricity cost and the potential benefits of controlled charging/discharging are explored systematically. Constraints caused by vehicle utilization as well as technical limitations of distributed generation and energy storage system are taken into account. The proposed economic scheduling is evaluated through a simulation by using a typical grid-connected microgrid model.

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“…This method-ology is used to support Virtual Power Players (VPP) to obtain an adequate management [13] of the available resources, including V2G in the smart grid context [14].…”

Section: Energy Resource Management Including V2gmentioning

confidence: 99%

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

Soares

Sousa

Morais

et al. 2013

Applied Soft Computing

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This paper presents a modified Particle Swarm Optimization (PSO) methodology to solve the problem of energy resources management with high penetration of distributed generation and Electric Vehicles (EVs) with gridable capability (V2G). The objective of the day-ahead scheduling problem in this work is to minimize operation costs, namely energy costs, regarding the management of these resources in the smart grid context. The modifications applied to the PSO aimed to improve its adequacy to solve the mentioned problem.The proposed Application Specific Modified Particle Swarm Optimization (ASMPSO) includes an intel-ligent mechanism to adjust velocity limits during the search process, as well as self-parameterization of PSO parameters making it more user-independent. It presents better robustness and convergence charac-teristics compared with the tested PSO variants as well as better constraint handling. This enables its use for addressing real world large-scale problems in much shorter times than the deterministic methods, providing system operators with adequate decision support and achieving efficient resource scheduling, even when a significant number of alternative scenarios should be considered.The paper includes two realistic case studies with different penetration of gridable vehicles (1000 and 2000). The proposed methodology is about 2600 times faster than Mixed-Integer Non-Linear Program-ming (MINLP) reference technique, reducing the time required from 25 h to 36 s for the scenario with 2000 vehicles, with about one percent of difference in the objective function cost value.

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Intelligent control of vehicle to grid power

Cited by 80 publications

References 10 publications

Simulation of demand management and grid balancing with electric vehicles

Simulation of demand management and grid balancing with electric vehicles

Microgrid economic operation considering plug-in hybrid electric vehicles integration

Application-Specific Modified Particle Swarm Optimization for energy resource scheduling considering vehicle-to-grid

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