Optimal Scheduling Methods to Integrate Plug-in Electric Vehicles with the Power System: A Review

Yang, Zhile; Li, Kang; Foley, Aoife; Zhang, Cheng

doi:10.3182/20140824-6-za-1003.01804

Cited by 24 publications

(22 citation statements)

References 80 publications

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“…where EV P is the average EV charging power. Equation (5) is a quadratic programming problem which can be solved by optimisation algorithms, such as Sequence Quadratic Programming (SQP) [7,8,13] or the meta-heuristic algorithms such as the Genetic Algorithm (GA) [28,29] and Particle Swarm Optimisation (PSO) [30,31]. In this study, SQP was chosen as a least complex method to solve the above problems (in MATLAB).…”

Section: Parametersmentioning

confidence: 99%

An electric vehicle dispatch module for demand-side energy participation

et al. 2016

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The penetration of the electric vehicle (EV) has increased rapidly in recent years mainly as a consequence of advances in transport technology and power electronics and in response to global pressure to reduce carbon emissions and limit fossil fuel consumption. It is widely acknowledged that inappropriate provision and dispatch of EV charging can lead to negative impacts on power system infrastructure. This paper considers EV requirements and proposes a module which uses owner participation, through mobile phone apps and on-board diagnostics II (OBD-II), for scheduled vehicle charging. A multi-EV reference and single-EV real-time response (MRS2R) online algorithm is proposed to calculate the maximum and minimum adjustable limits of necessary capacity, which forms part of decision-making support in power system dispatch. The proposed EV dispatch module is evaluated in a case study and the influence of the mobile app, EV dispatch trending and commercial impact is explored.

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Section: Parametersmentioning

confidence: 99%

An electric vehicle dispatch module for demand-side energy participation

et al. 2016

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“…However, none of these papers consider PEV integration. In Yang et al [47], some useful and popular programming and optimisation methods are reviewed. On the basis of this state-of-the-art desktop study, a more comprehensive survey organised by specific intelligent computational scheduling methodologies is examined, reviewed and categorised in this article.…”

Section: Introductionmentioning

confidence: 99%

Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review

Yang

Foley

2015

Renewable and Sustainable Energy Reviews

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198

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“…This function can be solved by many optimization algorithms such as Sequence Quadratic Programming (SQP) [10], [15], [35] and meta-heuristic algorithms including Genetic Algorithm (GA) [34,36] and Particle Swarm Optimization (PSO) [34,37]. In this paper, SQP is used in solving above problems in MATLAB.…”

Section: Economic and Environmental Applicationsmentioning

confidence: 99%

“…The first approach considers losses as a quadratic expression [34]. This expression is dependent on the system output and the coefficients related to the system configuration.…”

Section: Transmission Lossesmentioning

confidence: 99%