Scheduling and performance analysis under a stochastic model for electric vehicle charging stations

Kim, Jerim; Son, Sung-Yong; Lee, Jungmin; Ha, Hyung-Tae

doi:10.1016/j.omega.2015.11.010

Cited by 30 publications

(16 citation statements)

References 19 publications

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“…The problem with the existing methods is that demand uncertainty is not captured. For this reason, Markov-chain theory was applied in a few studies to stochastically model the arrival rate of EVs at a CS [9,11,26,27]. For instance, a death-and-birth process model was proposed by Farkas and Prikler [26].…”

Section: Literature Reviewmentioning

confidence: 99%

“…For this reason, Markov-chain theory was applied in a few studies to stochastically model the arrival rate of EVs at a CS [9,11,26,27]. For instance, a death-and-birth process model was proposed by Farkas and Prikler [26]. A mathematical model for the average waiting rate and the mean queue length was calculated based on the steady-state probabilities.…”

Section: Literature Reviewmentioning

confidence: 99%

“…First, Markov-chain model is proposed to provide a relation between the demand and the desired number of sockets per CS, as well as the desired number of CSs in an urban area to maintain the best quality of service. Then, the work of Farkas and Prikler [26] was extended by considering the balking parameters; EVs will enter with a probability α in the case when the served EVs in the CSs equals the total number of charging slots.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The arrival time of EV to the CS is assumed to follow a Poisson process with λ rate, and the charging rate is following exponential process with µ rate [17,26]. CS has c sockets available for EVs, and a maximum capacity of N vehicles.…”

Section: Markov-chain Of Ev Charging Stationmentioning

confidence: 99%

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Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

2021

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Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Markov-chain Of Ev Charging Stationmentioning

confidence: 99%

See 2 more Smart Citations

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

2021

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“…The mathematical models developed in the literature are plentiful. They can be categorized based on their modeling technique to Markov chain models [21,129,130], queuing theory models [131][132][133], deterministic models [134], Bayesian inference model [135], Markov-modulated Poisson process [136], zero 205 inflated Poisson [137], joint searching scheduling algorithm [138], fuzzy based model [139], Gaussian mixture model [140], and other stochastic models [141,142]; or based on the simulation techniques to Monte Carlo [129,135,[142][143][144], and fuzzy linear programing [145]; or based on the probability distribution to Poisson distribution [138,144], Gaussian distribution [129,142]. Overall, the accuracy of the model is not dependent on the modeling technique, rather it depends on the model assumptions.…”

Section: Overview Of Recent Modelsmentioning

confidence: 99%

Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review

Shepero

Widén

Bishop

et al. 2018

Renewable and Sustainable Energy Reviews

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Photovoltaics (PV) and electric vehicles (EVs) are promising technologies for increasing energy efficiency and the share of renewable energy sources in power and transport systems. As regards the deployment, use and system integration of these technologies, spatio-temporal modeling of PV power production and EV charging is of importance for several purposes such as urban planning and power grid design and operation. There is an abundance of studies and reviews on modeling of PV power production and EV charging available in the literature. However, there is a lack of studies that review the opportunities for combined modeling of the power consumption and production associated with these technologies. This paper aims to fill this research gap by presenting a review of previous research regarding modeling of spatio-temporal PV power production and charging load of EVs. The paper provides a summary of previous work in both fields and the combination of the fields. Finally, research gaps that need to be further explored are identified.This survey revealed some research gaps that need to be further addressed. Improving the accuracy of PV power production ramp-rate modeling in addition to quantifying the aggregate clear-sky index on cityscale are two priorities for the PV potential studies. For the EV charging load models, differences in model assumptions, such as charging locations, charging powers and charging profiles, need to be studied more extensively. Moreover, there is an imminent need for metering the load of charging stations. This is essential in developing accurate models and time series forecasting techniques. For studies exploring both the PV and EV impacts, local weak points in a spatial network need to be discovered, especially for the city-scale studies. Cooperation between eminent researchers in the PV and EV fields might propagate state-of-the-art models from the separate fields to the combined studies.vehicles has risen rapidly in the world, with over 565,000 plug-in cars sold globally 2011-2015 [6]. In the year 2016, the global sales of plug-in cars exceeded 750,000 vehicles [7]. Electric vehicles (EVs) is a term applied to hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs) [8]. In this paper, EVs refer only to PHEVs and BEVs.As an example, a target of 20 million EVs and fuel cell vehicles, on the road was set by the Electric 15 vehicle initiative (EVI) to be achieved in 2020 [9]. This target is expected to be surpassed if the 78% growth rate achieved in the year 2015 was maintained [10]. However in the year 2016, the annual growth rate of EV sales has fallen below 50% for the first time since 2010, and the annual growth rate of publicly available charging has increased by 72% [11]. The Electric and plug-in hybrid roadmap aims that EVs represent 50% of the sales of vehicles worldwide by 2050 [12]. This is to say that the current research is investigating 20 penetration levels of EVs that are expected to take place within decades, which is ...

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Mitigating unbalance and improving voltage considering higher penetration of EVs and DG using hybrid optimization technique

Saravanan

Kannayeram

Muniraj³

2021

Int Trans Electr Energ Syst

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Summary In this manuscript, an efficient hybrid method is proposed aimed at mitigating imbalance, improving the voltage in the higher penetration of electric vehicles (EVs) and distributed generation (DG). The proposed hybrid method is the combination of the mayfly algorithm (MA) and the radial basis function neural network (RBFNN), called the mayfly algorithm‐radial basis function neural network (MA‐RBFNN) method. These major contributions of the proposed work mitigate network unbalancing and voltage profile with the loss of network energy, utilize obtainable network capacity and enlarge the comfort level for EV consumers. The MA is utilized for solving the multi‐objective optimization issue of coordinating EV together with DG in the distribution grid. The RBFNN agrees with the distribution system operators to mutually enhance the phase sequence with the optimum dispatch of DGs for improving the performance of the network. The proposed system allows distribution system operators to mutually enhance the phase sequence with an optimum DG dispatch for recovering the performance of the network and solve the multi‐objective optimization issue of coordinating EV and DG on the distribution network. This proposed technique is implemented on the MATLAB/Simulink platform as well as evaluated for efficiency, likened to the existing methods such as genetic algorithm (GA), particle swarm optimization (PSO) and MA technique. The performance of the proposed method was analysed in terms of quality of solution comparison, integral of the time‐weighted absolute error (ITAE), integral squared error (ISE) and loss reduction (%). These comparative outcomes display the eminence of the proposed method more than any other existing technique.

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Scheduling and performance analysis under a stochastic model for electric vehicle charging stations

Cited by 30 publications

References 19 publications

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review

Mitigating unbalance and improving voltage considering higher penetration of EVs and DG using hybrid optimization technique

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