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This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations without numerical difficulties. [ABSTRACT FROM AUTHOR]

Abstract The problem of minimizing the overall travel time, considering the one spent at charging stations, with the aim to improve the cruising range for Electric Vehicles (EVs) in road networks, is examined in this paper. Since the battery capacity of EVs is limited, it is inevitable for them to stop at charging stations. As they are lacking and the recharging process is time-consuming, an efficient route planning is essential. This paper extends a previously presented approach, i.e., EVRC (Electric Vehicle Route Planning with Recharging). In detail, the solution presented in this work considers a set of factors into account simultaneously and introduces a functional approach that estimates time-optimal routes for road networks with realistic models of charging stops. The key features include the efficient computation of the recharging station's locations, the battery charging amount under a partial charging scheme, the nonlinear charging functions, and the service time duration at recharging stations. The experimental results highlight that the proposed approach outperforms other similar methods in facing the same problem. The extensive experimental evaluations show that refusing partial charging and neglecting nonlinear charging, as well as servicing time, may generate some infeasible or too costly solutions. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are environmentally friendly and are considered to be a promising approach toward a green transportation infrastructure with lower greenhouse gas emissions. However, the limited driving range of EVs demands a strategic allocation of charging facilities, hence providing recharging opportunities that help reduce EV owners' anxiety about their vehicles' range. In this paper, we study a set covering method where self-avoiding walks are utilized to find the most significant locations for charging stations. In the corresponding optimization problem, we derive a lower bound of the number of charging stations in a transportation network to obtain full coverage of the most probable routes. The proposed method is applied to a transportation network of the southern part of Sweden. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are emerging as a favorable strategy to meet the increasing environmental concerns and energy insufficiency, and this trend is expected to grow in the near future. However, the inadequate charging infrastructure is becoming a major barrier to the wide acceptance of EVs. Deployment of this infrastructure is expected to maximize the adoption of EVs to facilitate users' range anxiety. Therefore, connectivity between the charging stations (CS) is mandatory. Understanding the real-time status of CSs can provide valuable information to users such as availability of charging provisions, reserves and the time to reach the CS. The intent of this paper is to provide a better EV charging system by utilizing the advantages of the Internet of Things (IoT) technology. The IoT paradigm offers the present facilities a real-time interactional view of the physical world by a variety of sensors and broadcasting tools. This research article proposes a real-time server-based forecasting application: i) to provide scheduling management to avoid waiting time; and ii) to provide a real-time CS recommendation for EVs with an economic cost and reduced charging time. In addition, the proposed scheme avoids third-party intervention and protects EV user privacy and complex information exchange between the user and CS. The end users can easily use the CS based on their requirements. This synergetic application is built up through the PHP programming language in the Linux UBUNTU 16.04 LTS operating system, and all relevant information is processed and managed through Cloud Structured Query Language (CSQL) from a Google cloud platform. The effectiveness of this application is also validated through a low-cost test system using LTC 4150, ESP 8266 Wi-Fi module and Arduino. • Inadequate charging infrastructure is a major barrier to the wide acceptance of EVs. • We propose a real-time server-based forecasting application. • We present an EV charging system by utilizing the advantages of the IoT technology. • The application avoids third-party intervention and protects EV user privacy. • The effectiveness of this application is validated through a low-cost test system. [ABSTRACT FROM AUTHOR]

Salp swarm algorithm (SSA) is one of the recently developed meta-heuristic optimization algorithms. Since SSA outperforms other swarm-based algorithms, it has recently been employed in various applications, including feature selection, neural network training and renewable energy systems. In this paper, an improved salp swarm algorithm based on a Gaussian random walk is proposed, which enhances the algorithm's performance particularly for multidimensional constrained global optimization problems. The integration of a Gaussian random walk into the algorithm balances between its exploration and exploitation capabilities. Furthermore, the proposed algorithm introduces a new re-dispersion strategy in the case of stagnation at local optimum points, which considerably enhances exploration. The performance of the proposed algorithm is evaluated using a set of twenty-three benchmark test functions and is compared to the performance of prevalent metaheuristic algorithms. Statistical analysis is performed using Wilcoxon signed-rank test, and the results reveal considerable improvement over the competing algorithms. Then, 21 real-world optimization problems are used to further evaluate the efficacy of the proposed algorithm. The winners of the CEC2020 Competition on Real-World Single Objective Constrained Optimization, SASS, sCMAgES, EnMODE, and COLSHADE algorithms, are used as four comparable algorithms in the real-world optimization problems. The convergence curves and simulations provide very competitive performance compared to the comparative algorithms. The proposed algorithm is used to address one of the most challenging real-world constrained problems in power system applications, namely, determining the optimal charging schedule for electric vehicles at charging stations. The results reveal that the proposed algorithm outperforms other existing algorithms in terms of increasing the charging revenues and achieving maximum power grid stability. • Proposing a Gaussian random walk to improve the utilization and exploration of followers in the salp swarm algorithm. • Introducing the concept of multi-strategy leaders for re-dispersion in case of stagnation at local optimum points. • Validation of using a penalty-based method with SSA to handle multidimensional global optimization problems with constraints. • Evaluating the proposed algorithm with benchmark functions and real-world problems shows superiority over recent methods. • The proposed algorithm optimally schedules electric vehicle charging, addressing a key power system challenge. [ABSTRACT FROM AUTHOR]

Vehicle-to-Grid (V2G) technology is currently used in plug-in hybrid electric vehicles (PHEV) during the discharging mode. This technology has become a considerable mechanism to provide a backup power source for distribution networks. In the meanwhile, these vehicles need Charging Stations (CSs) to receive power from the grid. Therefore, determining the optimal CS placement in the distribution network to utilize the V2G technology of PHEV has become an essential component so as to enhance the performance of the distribution network in the time of peak demand. This paper discusses a generalized methodology to assess the optimal placement when installing the CS in the distribution network. This optimization is done using a novel heuristic optimization technique called quantum binary lightning search algorithm. A multi-objective function is set to utilize the V2G technology to minimize line loading, voltage deviation, and circuit power loss. The performance of the proposed method is compared to the performance of other heuristic optimization techniques. As a result, the proposed algorithm has fewer limitations in terms of premature convergence than other heuristic optimization techniques. [ABSTRACT FROM AUTHOR]

With the increasing number of Electric Vehicles (EVs) in modern society, a number of challenges and opportunities are presenting themselves. For example, how to choose charging station locations to minimize the Distribution Network's (DN) power loss when a large number of EVs are connected to the DN. How impact factors, such as different load patterns, EVs’ charging locations and network topology, affect charging station location is becoming vital. In this paper a new charging station location methodology informed by impact factor analysis is proposed by using the Active and Reactive Power Dispatch of charging stations in terms of power loss minimization. Results for the 36 DN with three different scenarios are presented. In addition, a more realistic model based on EV's daily travel patterns is built to illustrate how these impact factors affect charging station location. It is demonstrated that the optimal charging station location in terms of power loss minimization can be found by using the new methodology, and it is not affected by the EVs’ charging location and load patterns, it is affect by the network topology. [ABSTRACT FROM AUTHOR]

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• The sizing problem of a Charging Station with smart charging capability is modeled. • A novel formulation for the Quality of Service (QoS) level of the CS is presented. • The sizing problem is brought to a solvable form after constraint relaxations. • Simulation results show the relation between the QoS and the cost of the chargers. • Simulations show the impact of smart charging towards reducing infrastructure cost. The increasing penetration of electric vehicles (EVs) inherently couples the transportation system with the electricity system through charging stations (CSs). Today's regulatory context highly incentivizes CS infrastructure investments that are expected to have a significant impact on reducing air pollution, cutting emissions and promoting environmentally sustainable cities. The Sizing problem of a CS typically concerns the minimization of the investment cost for charging facilities, subject to the CS being able to fulfill a certain level of charging requests. Several studies have shown the potential of Smart Charging technologies, towards controlling the charging profiles of EVs, so as to achieve a lower operational cost or a lower peak to average power consumption ratio for the CS, by shifting the charging of some EVs. By making more efficient use of charging facilities, Smart Charging can also help reducing the amount of chargers required in order to achieve a certain Quality of Service (QoS) for the CS's clients. In this paper we solve the CS's sizing problem (i.e. decisions on number and types of installed chargers) through an optimization framework that minimizes the investment cost of CS operators, subject to achieving a certain QoS for their clients (EV owners). In particular, we extend the existing CS sizing models by taking into account also the smart charging capabilities during operation. We present a novel formulation for the QoS level of the CS using chance-constraints and propose some relaxations that constitute the problem solvable. Finally, we present a methodology that enhances the scalability of the optimal sizing algorithm. The proposed methodology is able to offer valuable services to CS operators in competitive environments. [ABSTRACT FROM AUTHOR]