A review of EVs charging: From the perspective of energy optimization, optimization approaches, and charging techniques

Amjad, Muhammad; Ahmad, Ayaz; Rehmani, Mubashir Husain; Umer, Tariq

doi:10.1016/j.trd.2018.03.006

Cited by 147 publications

(86 citation statements)

References 176 publications

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“…40 PSO has better performance compared with GA and other MA in terms of exploitation and exploration ability. This state has to be found out while following the constraint making optimisation problem a binary optimisation problem.…”

Section: Solution Methodologymentioning

confidence: 96%

“…To solve this binary optimisation problem, meta-heuristic algorithms (MA) like particle swarm optimisation (PSO) and genetic algorithm (GA) should be adopted. 40 PSO has better performance compared with GA and other MA in terms of exploitation and exploration ability. 41 Hence, in this study, PSO is selected to solve the optimisation problem over the GA and other MAs.…”

Section: Solution Methodologymentioning

confidence: 96%

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Charging cost minimisation by centralised controlled charging of electric vehicles

Patil

Kalkhambkar

2019

Int Trans Electr Energ Syst

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Summary With the recent breakthroughs in battery technology and large scale production, electric vehicles (EVs) are becoming cheaper. In a few years, mass deployment of EVs will put severe stress on the electricity network. Charging of EV during peak hours may overload the distribution grid transformer, and EV owners may have to pay more money for electricity during peak hours. To address these issues, a coordinated scheduling model is proposed in this paper. A mathematical model is formulated to minimise the charging cost of each EV while satisfying the constraints. In this work, time of use (ToU) tariff from the utility and actual power demand from household and EVs are used to conduct simulation for one week in summer and winter season with different levels of EV penetration. The results demonstrate that the proposed scheduling model can significantly reduce the charging cost for the EV owner and power peaks in the distribution network.

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Section: Solution Methodologymentioning

confidence: 96%

Section: Solution Methodologymentioning

confidence: 96%

Charging cost minimisation by centralised controlled charging of electric vehicles

Patil

Kalkhambkar

2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

show abstract

“…The continuous variable s is a vector of startup cost of each generation unit at each time slot. Equation (47) represents the constraint involving u, g, and p, such as the power balance constraint and the spinning reserve constraint (10) and (11). The objective function (43) generation cost includes the coal fuel cost and the startup cost which can be reduced to the form of Equation (41).…”

Section: Nonconvex Mixed-integer Programming (Mip) Theorem 4 the Pmentioning

confidence: 99%

“…In the decentralized method, the vehicle users can directly determine their charging profiles lured by incentives from grid operators. 11 As such, the execution time should be concerned when PEVs penetration increases in the grid. Xydas et al 8 introduce a decentralized scheduling algorithm for EV charging, where EVs were separated in "Responsive" and "Unresponsive" EVs to control signals.…”

Section: Introductionmentioning

confidence: 99%

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Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

Shang

Zheng

Shao

et al. 2019

Int Trans Electr Energ Syst

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With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods.

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GGlueVaR‐based participation of electric vehicles in automatic demand response for two‐stage scheduling

Gao

Yang

et al. 2020

Int J Energy Res

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Due to the uncertainty of the external situation and the varied ability of electric vehicle (EV) owners to understand and process information, the demand response optimization method is not timely and flexible enough. This article puts forward a two-stage electric vehicle automatic demand response (ADR) optimization method based on generalized Glue value-at-risk (GGlueVaR) to solve existing problems. First, a two-stage electric vehicle ADR optimization method is proposed considering both the EV owner's benefit and network load fluctuation. In the process of ADR, different risk preferences of electric vehicle owners affect the EV owner participation in ADR. Second, the GGlueVaRbased EV owner willingness decision model is adopted to measure an EV group's risk attitude. Finally, a case study is provided to verify the effectiveness of the proposed method. Results show that the proposed model reduced the average charging cost of EV owners by 45% and increased the profit resulting from DR by 91% compared with the price-based demand response model. Therefore, the proposed model is more efficient than disorder charging model. The method is timelier and more flexible compared with other prior demand response optimization methods.

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A review of EVs charging: From the perspective of energy optimization, optimization approaches, and charging techniques

Cited by 147 publications

References 176 publications

Charging cost minimisation by centralised controlled charging of electric vehicles

Charging cost minimisation by centralised controlled charging of electric vehicles

Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

GGlueVaR‐based participation of electric vehicles in automatic demand response for two‐stage scheduling

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