Abstract
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.
Highlights
Academic Editors: Karim El-Basyouny and Tae J
Queuing in Charging stations (CSs) can be modeled as a birth-and-death process, where the birth rate is Queuing in CS can be modeled as a birth-and-death process, where the birth rate is the arrival rate of Electric Vehicles (EV) to the CS, the death rate is the charging rate, and the state represents the arrival rate of EVs to the CS, the death rate is the charging rate, and the state represents the number of EVs at the CS at time t
These parameters have been used and estimated by Farkas and Prikler [26]: N: Number of parking slots = maximum allowable number of EVs in the CS (Maximum Capacity) = EVs being served + EVs waiting in the queue c: Number of charging sockets μ: Charging rate (1/min) λ: EV arrival rate to the CS (1/min) α: The entering probability when the CS is full (0.3 for all experiments)
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. These parameters include: location, level, and size and capacity of the available CSs as discussed below:. Many constraints need to be taken into consideration when developing the optimization algorithm of allocating EVCSs. Based on related work in the literature, these constraints can be classified as: (I) Budget-related constraints including demand and cost constraints, and (II) Route-related constraints including available routes between candidate locations, the distance EV can go before charge, traffic, weather, etc. Based on related work in the literature, these constraints can be classified as: (I) Budget-related constraints including demand and cost constraints, and (II) Route-related constraints including available routes between candidate locations, the distance EV can go before charge, traffic, weather, etc In this paper, both budget and route-related constraints are considered.
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