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Electric vehicle (EV) load integration and environmental concerns in recent years have motivated energy users to step forward in reducing carbon footprints by promoting energy trading. In the energy trading market, different energy stakeholders participate in trading energy with each other to maximize their utilities and improve trading efficiency. The social welfare (SW) of the energy market plays a significant role in keeping the market going and motivating new users to engage in energy trading. The SW of the energy market depends on its structure, where energy flows from producer to end user in a fair way. This paper introduces a hierarchical energy trading framework, formulated as a three-stage Stackelberg game, to enhance SW. In the first stage of this hierarchy, the retailer, acting as the sole leader, seeks to maximize profits through optimized energy sales to charging stations (CS). Subsequently, in the second stage, each CS operates as a leader for EVs, aiming to maximize profits by minimizing energy trading costs, while also acting as a follower in response to the retailer's strategies. The third and final stage involves EVs collaboratively acting as a unified strategic agent to minimize their trading costs through optimized charging and discharging schedules. This paper also introduces an average price penalty function to further enhance SW in the energy market. The proposed model, addressing the complexities of energy trading interactions, is formulated as a non-linear optimization problem with constraints. It has been implemented in a Python environment and solved using the Gurobi optimization solver, demonstrating its potential to improve the efficiency, fairness, and overall social welfare of the energy trading market. • Introduces a hierarchical energy trading framework using a three-stage Stackelberg game to enhance market social welfare. • Incorporates renewable energy at the retailer level, diversifying energy sources and boosting ecological relevance. • Establishes comprehensive pricing functions across all levels, including reciprocal EV discharge signals, for dynamic market interaction. • Implements an average price penalty at charging stations to preserve social welfare and promote efficient energy use. • Demonstrates model efficiency and practical applicability through tests on the IEEE 33 bus system, showing superior profitability and convergence. [ABSTRACT FROM AUTHOR]

Integrating electric vehicles (EVs) into the electric power system poses significant challenges to grid operation and planning due to the potential constraints on the power system. To minimize grid congestion, the best option is to increase the production of photovoltaic (PV) energy for domestic use and EV charging stations. This paper addresses the nexus of technical, financial, and environmental effects of customer involvement in economic development and load management. In this paper, a new model design of solar-powered EV charging stations is proposed and implemented in HOMER Grid, and a case study has explored how economic, technical, and energy management benefits can be achieved through customer energy involvement and the integration of PV-based charging stations. The proposed PV-based charging stations contribute toward the energy management of the region, and the study observes the real-time optimal charging and discharging strategy of PV-based grid-connected charging stations. The case study results show that the investigated area can produce 1,070,804.096 MWh/year of energy through maximum customer involvement, consequently reducing overall energy costs. Moreover, the study demonstrates that the selected region can produce 86,961,688 kWh/year through the PV system, achieving 363,899 charging sessions per year, offering maximum advantages and facilitation for EV charging. The proposed model applicability shows that large-scale customer involvement can bring significant techno-economic benefits and attract regional investments. • This paper proposes a new design of a grid-tied renewable energy system. • It investigates the energy economics and environmental effects for a practical location. • The research examines energy management including PV systems and electric vehicles. • The results of proposed model are compared with other models. [ABSTRACT FROM AUTHOR]

Reverse logistics plays a critical role in sustainable development and circular economy by transforming waste into value. However, transporting large volumes of waste and recyclables generates significant carbon emissions. This paper explores an industry-led strategy for charging infrastructure development, which integrates the reverse logistics network designs with a network of charging stations to promote electric trucks (E-trucks) adoption and reduce carbon footprints in transportation. Our model optimizes operational costs while ensuring accessibility to charging infrastructure. A case study in Norway demonstrates that, despite higher operating costs due to additional charging stations, the adoption of E-trucks can decrease total reverse logistics costs by 0.18%—2.37% and cut carbon emissions by 16.3%—17.6%. These findings support the economic viability and environmental benefits of this strategy for advancing sustainable practices in transportation, and it further highlights the interdependencies between logistics network design and charging infrastructure planning. [ABSTRACT FROM AUTHOR]

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]

Demand response is expected to play a fundamental role in renewable energy communities to alleviate the electricity demand–supply mismatch, especially in the presence of stochastic load and generation. In this paper, we consider an electric vehicle charging station that participates in incentive-based demand response programs. A real-time charging scheme is devised to optimize the charging station operation by coordinating the charging process of the electric vehicles, and complying with the incoming demand response requests. In this context, vehicle demand is assumed uncertain, while demand response requests ask for a change in the charging profile over certain time intervals, in exchange for a monetary reward. By exploiting the probability distributions describing the vehicle charging process, a stochastic formulation is employed to devise a novel charging algorithm aimed at reducing the charging station operational cost. Such a procedure can (i) handle the uncertainty affecting the charging process in different settings and scenarios, and (ii) exploit the information collected in real-time to refine forecasts and hence ensure a higher demand flexibility. Numerical results show that the proposed approach ensures considerable cost reduction compared to the benchmarks, and features highly scalable runtimes. • A stochastic receding horizon scheme is introduced for real-time EV charging. • Acquired online information is employed to refine load forecasts. • EV probability distributions are used to manage the uncertain demand of EVs. • The algorithm performance is validated by comparison with oracle-based optimization. • Computation times are feasible for problem instances involving high EV penetrations. [ABSTRACT FROM AUTHOR]

This paper presents a platform for evaluating sensor data fusion algorithms based on a Network-on-Chip (NoC). Initially the NoC is simulated by MPSoCSim, which contains an ARM and several MicroBlaze processors. The approach is furthermore evaluated on a real FPGA system in an automotive context. Charging stations for electric vehicles consist of expensive sensors, as the energy measurement system is an important part used for billing purposes, for safety of the charging process and for security. The sensors, that are usually used, are special made solutions, which are thermally stable and highly accurate. The NoC infrastructure presented in this paper results in a new method for sensor data fusion, which decreases costs by using inexpensive sensor units. These sensor units are sensitive to temperature changes. The target of the sensor data fusion approach is to replace expensive sensors with inexpensive ones without loosing accuracy. The presented simulation platform allows an easy exchange of sensor models and sensor data fusion algorithms. [ABSTRACT FROM AUTHOR]

The advent of distributed energy resources (DERs) in microgrids has provided significant benefits. University campus microgrids can manage their DER efficiently to minimize consumption, energy losses, and environmental impacts. Thus, to boost the transition from a traditional university campus to a sustainable and smart campus, this paper proposes an optimal siting and sizing study of photovoltaic systems and electric vehicle charging stations for a microgrid of a Brazilian university. This problem is formulated as mixed-integer nonlinear programming and is carried out in two stages. The first stage minimizes grid losses by locating and sizing photovoltaic panels, while the second stage minimizes the grid losses and maximizes the proximity of charging stations to the load centers by optimizing the place and sizing of charging stations. In addition, this work considers load uncertainty through the Monte Carlo method with a scenario reduction technique. The results estimate that photovoltaic systems can reduce 13.48% of the monthly electricity cost. The optimal charging station location is usually nearby the photovoltaic system siting or the substation. However, the increase in the number of chargers in buses with high consumption causes an increase of around 5% in grid losses. Furthermore, this paper discusses possible scenarios for photovoltaic systems and charging station installation based on the benefits of the transition to a sustainable and smart campus, considering Brazilian electricity sector policies. • Perform a scenario reduction to deal with load and generation curves uncertainties. • Minimize electrical losses by optimizing the siting and sizing photovoltaic systems. • Perform multi-objective optimization for siting and sizing charging stations. • Sensitivity analysis for distributed energy resource installation. • Discuss the advent of distributed energy resources to university campus. [ABSTRACT FROM AUTHOR]

This paper investigates the pandemic impact on electric vehicle (EV) monitoring framework designed for analysing and tracking of EV charging data in the Croatian electric vehicle charging station grid. The developed framework encompasses multiple features of the EV charging grid behaviour, notably: (i) global predictions of the utilized energy, (ii) number of charging vehicles, and (iii) total time spent while charging. In this paper, we analyse framework performance and key performance indicator (KPI) values during the pre-lockdown period, soft-lockdown period, and post-lockdown lift-off period to show how the pandemic situations and governmental lockdown regulations can impact EV grid performance and EV charging business. In addition to that, we test a daily framework anomaly detection flow that tracks the global predictive performance of the predictive models on the EV grid, to detect if the framework detects this to be a situation that is novel (anomalous) during the soft-lockdown period. Results show that changes in the behaviour during the lockdown period reduce electric demand on EV charging spots, with customers having lower demand and similar behaviour as in other European countries. Furthermore, the soft-lockdown period is detected as a period with more extreme anomalous values for supervised models, and this is shown as an increase in residual anomaly values, flagging 3 times more days as an extreme anomaly than in the normal periods. • Data science framework performance during COVID-19 lockdown period is analysed. • Negative impact of soft-lockdown to EV charging demand is found. • Intraday seasonality of energy demand is found to be unchanged. • Extreme anomaly values for predictive models are detected during the soft-lockdown. [ABSTRACT FROM AUTHOR]

In the contest of European and National choices regarding energy and mobility policies, the goal of this paper is to introduce reasonable guidelines for the local promotion of electric mobility in an area of central Italy and possibly to extend the methodology to other regions/countries. In fact, the path towards car electrification is started worldwide, but the rate of increase of electrically powered cars will depend primarily on charging stations availability. In this respect, utilities that manage/own local grids may be key actors by investing on infrastructures and thus locally creating the conditions for the spreading of electric cars. A techno-economic analysis of the investment of a local utility has been performed taking into account medium- and long term-forecasts of electricity and fuel prices, as well as electric cars market share and economic risk. The study proved that an investment in urban charging station infrastructure in Italy can be profitable even without incentives, with a payback period of 4–5 years. The results of the analysis have been exploited by an Italian utility which owns/manages the local electric grid in two municipalities. The first steps of the plan implementation, which started in 2014, are reported in the paper. [ABSTRACT FROM AUTHOR]

This paper is aimed to present an experimental criterion that allows researchers and designers to evaluate the performance of DC micro-grids dedicated to charging operations of full EV. The laboratory methodology is explained through tests on a demonstrator of power architecture, specifically designed as simplified case study of a DC charging station for fully-electrified low-power two-wheeler, such as: electric scooters and bikes. This experimental prototype is composed by a 20 kW AC/DC bidirectional grid-tied converter, which realizes the DC conversion stage, and two DC/DC power converters, interconnected with the micro-grid through a DC bus. The power architecture provides on one hand the charging operations of electric two-wheeler battery packs and on the other hand the integration of the micro-grid with an ES buffer, which has the main function of supporting the main grid while an electric vehicle is on charge. The performance of the considered architecture is characterized and analyzed in different operative conditions, through a specific management of the energy fluxes. The laboratory tests evaluate efficiency, charging times and impact on the main grid, with specific reference to the DC charging operations of electric scooters. The obtained experimental results show the advantages of adopting the DC buffer architecture, compared with an AC commercial battery charger, considered as reference in this work. Finally, the numerical data of this paper, related to the single power components and to the experimental results evaluated for the whole demonstrator, effectively support the lack of knowledge in the literature about charging stations for EVs. In fact, these pieces of information, based on experimental tests, are expected to support the building of simulation models and the identification of the best energy management and control strategies to be adopted in a smart-grid scenario, characterized by distributed generation systems including renewable energy sources. [ABSTRACT FROM AUTHOR]

This paper is aimed to analyze design criteria, setting up, control strategies and experimental tests related to a power configuration of DC micro-grid for fast charging of full electric and plug in hybrid vehicles. The proposed DC fast charging architecture is derived by an analysis comparing the main characteristics of well known architectures, mainly based on AC and DC bus, taking also into account the integration of renewable energy sources (RESs) with stationary energy storage systems and fleets of road electric/hybrid vehicles. On the base of the proposed architecture a laboratory prototype of charging station has been realized by means of a 20 kW AC/DC bidirectional grid tie converter interconnected with two different power DC/DC converters of similar rated power. In this micro-grid architecture the AC/DC converter realizes a conversion stage at 790 V DC, whereas other two converters allow either the electric vehicle battery packs to be charged or an energy storage buffer to save electric energy and support the main grid during the fast charging operations. The laboratory tests described in this paper are mainly devoted to characterize the laboratory demonstrator, in different operative conditions, such as vehicle-to-grid (V2G), charging/discharging operations of different types of storage systems and fast charging operations of road electric vehicles. Then the study of the proposed power conversion architecture is focused on the evaluation of charging/discharging power, efficiency, energy flux management and its impact on the main grid. In addition proper control strategies are evaluated and implemented, allowing the proposed architecture to follow the required operations. The obtained experimental results demonstrate real advantages in terms of charging times and power requirements from the main grid, when adopting DC buffer architecture for fast charging operations. Finally, these results support the identification of a knowledge base, useful to evaluate energy management and control strategies to be adopted for DC charging stations and each one of their power converters in a smart grid scenario with distributed generation systems. [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]

Abstract This paper presents a significantly improved optimization model for the planning of the charging infrastructure for electric-drive vehicles, where the optimization objective function is the minimization of overall (installation, maintenance, operation) placement costs of charging stations with regards to a charging technology. The constraints involve the electric power system reliability check, ensuring charging reliability and the required quality of service of the charging infrastructure. In ensuring the charging reliability, at least one candidate location must be selected within the driving range of electric vehicles and suitable charging technologies placed to accommodate the disposable charging times of electric vehicle users for the requested quality of service. The proposed optimization model presents an upgrade of an existing optimization formulation since it includes a power system reliability check based on a DC power flow model. To show the general applicability and significance of the model, a test 10 × 10 grid road network and a standard six-bus test power system are considered. Numeric results illustrate the optimal charging stations placement layout and overall costs placement for different driving ranges and the required quality of service level by including a power system reliability check, to serve both the charging infrastructure investors and electric power system operators. Highlights • Including the electric power system reliability check, to derive an optimal charging stations placement solution. • New constraints in the charging stations placement optimization model, to serve both the investors and network operators. • Lines outage criticality ranking, to provide network operators valuable information for the network expansion planning. [ABSTRACT FROM AUTHOR]

Highlights • We developed a model to simulate BEV trips based on travel surveys. • BEVs in 2017 could already cover 85–90% of trips in Finland and Switzerland. • Charging infrastructure at households should be expanded faster. • With infrastructure policies in place, BEVs can cover 99% or more of all trips. Abstract Limitations of battery capacity in battery electric vehicles (BEVs) contribute to what is known as range anxiety, and therefore poses an obstacle to their mass-market adoption. While high-range BEVs have been recently introduced, it is not clear whether they will be able to cover all possible trips without long recharging detours, and what the infrastructure needs of those vehicles are. To understand the impact of range limitations in Switzerland and Finland, we constructed a simulation model that is based on representative national travel surveys. We use it to calculate the potential of BEVs to cover any trips and investigate options to increase this coverage. The options discussed in this paper are ways to facilitate easy recharging, such as infrastructure development policies. We complement our results with insights from three focus groups. The results suggest that 85–90% of all national trips could have already been covered with BEVs prevalent in 2016. If the charging station infrastructure is developed appropriately and high-range BEVs are adopted, it is possible to reach a potential coverage of 99% or more in both countries. Deploying charging stations at users' homes and in residential areas does contribute significantly to this improvement and is desirable from a car user's perspective. Providing fast-charging stations in other locations is necessary to maximise the potential. We recommend to focus policy efforts on the development of residential charging options and to increase the visibility of electro-mobility using fast-charging stations. [ABSTRACT FROM AUTHOR]

Highlights • This study develops a mixed user equilibrium formulation with electric vehicles (EVs) and gasoline vehicles (GVs). • A new concept called EV charging ratio is introduced to capture EV charging behavior for inner-urban trips. • The formulation with exogenous charging ratio is convex in link flows, stations flows, and EV demands. • The formulation incorporates EV elastic demand, whose utility function is calibrated via a market survey in Hong Kong. • The study observes the property of link flows preservation for a range of EV market penetration. Abstract In this paper, a mixed user equilibrium (MUE) model with Electric Vehicles (EVs) and Gasoline Vehicles (GVs) is proposed to account for the charging behavior of EVs in an urban network. The main difference between EVs and GVs lies in that certain EVs with immediate charging need have to traverse a specific station for recharging, while GVs and other EVs without immediate charging need do not have such a requirement. The proportion of EVs with immediate charging need, referred to as charging ratio in this study, is an OD specific endogenous variable, related to their daily commute trip lengths and EV driving ranges, i.e., EVs will need recharging once every few days. The MUE conditions state that EVs with charging need choose the routes via a charging station while en route to their destinations with minimum travel time cost, electricity cost plus charging station cost; whereas GVs and EVs without charging need select the routes with minimum travel cost without having to traverse any charging station. This study also captures the interaction between network design (such as charging station locations) and EV demand which follows a logit model calibrated with an EV market survey conducted in Hong Kong. We formulate the MUE problem first with a nonlinear complementarity (NCP) approach and solve it with a gap function, then we relax the charging ratio to be exogenous and formulate a convex mathematical program for efficient solutions, with the charging ratio iteratively determined. Furthermore, we observe that the resultant link flows exhibit the property of link flow preservation, i.e., the total link flows remain unchanged under a range of EV and GV demands. We first solve the Yang-Bell network to demonstrate its properties, and then solve the Sioux-Falls network to show its solution efficiency. [ABSTRACT FROM AUTHOR]

As the number of electric vehicles (EVs) continues to rise, it is essential to consider appropriate management strategies for coordinating EVs connected to different buses in the power networks. In light of this, this paper proposes a stochastic two-stage bi-level model for coordinating EVs in a distribution network with charging stations under alternating current optimal power flow (ACOPF) constraints. The scheduling problem is considered to independently minimize the costs of the distribution system operator (DSO) and EVs parked at different charging stations located at various buses of the network. In the proposed model, the DSO as the leader, and all EVs as independent followers, are individual entities who try to follow their priorities and objectives. The amount and price of exchanged power between the DSO and EVs are optimally determined in the proposed model. The proposed bi-level model has been converted to a single-level model using the Karush–Kuhn–Tucker (KKT) conditions. Afterwards, the Big M method is used to convert the non-linear equations that appear due to utilizing the KKT approach. The scenario-based uncertainty modeling is used to model the uncertainty in input data such as day-ahead and real-time market prices, EVs' initial state of charge (SOC), and arrival/departure time. The centralized unilateral form of the model has also been developed to validate the proposed model. The results indicate that the bi-level model can lead to cost reduction for the EVs. [ABSTRACT FROM AUTHOR]

• Proposal of a procedure for network planning focused on maximizing the widespread of CSs. • Development of a µGA based procedure which carries out a multi objective optimization taking into account monetary and geographical constraints for planning the most effective distribution network reinforcements enabling CSs installation. • Tests of the procedure on the IEEE 70-bus test network. • Tests of the procedure on the real case study of a large and existing 471-bus DS, exploiting load profiles measured every 15 min in one year. The widespread increase of electric vehicles sales forces Distribution System Operators to evaluate and improve the capability of the distribution system to host distributed charging infrastructures. This paper suggests a procedure to identify grid reinforcements to increase the capability of the system as well as to evaluate the current hosting capacity. The developed procedure is based on a micro-genetic algorithm and its solution identifies the optimal grid reinforcements to be planned on a Medium Voltage network in order to increase the number of charging stations that can be connected. The procedure requires the model of the network, the yearly load profiles of the secondary substations and the algorithm output takes into account an envisioned budget to be spent, avoiding network violations and an iniquitous distribution of the charging stations. The procedure was firstly validated considering a 70 nodes test network. Furthermore, the real case study of the distribution system of the city of Terni was assessed focusing on one of its main subnetwork with 471 nodes; the procedure was exploited to assess the current hosting capacity and to estimate that on average 0.01 M€ has to be spent on grid reinforcements in order to further increase hosting capacity. [ABSTRACT FROM AUTHOR]

A massive use of electric vehicles is nowadays considered to be a key element of a sustainable transportation policy and the availability of charging stations is a crucial issue for their extensive use. Charging stations in an urban area have to be deployed in such a way that they can satisfy a demand that may dramatically vary in space and time. In this paper we present an optimization model for the location of charging stations that takes into account the main specific features of the problem, in particular the different charging technologies, and their associated service time, and the fact that the demand depends on space and time. To measure the importance of incorporating the time dependence in an optimization model, we also present a simpler model that extends a classical location model and does not include the temporal dimension. A worst-case analysis and extensive computational experiments show that ignoring the temporal dimension of the problem may lead to a substantial amount of unsatisfied demand. • Introducing a node-based multi-period model for the location of charging stations. • The multi-period model takes into account several characteristics of the real problem. • The multi-period model is compared to a single-period one via worst-case analysis. • Extensive computational experiments showing the importance of time dimension. [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]

Half of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles. [ABSTRACT FROM AUTHOR]

The massive deployment of plug-in electric vehicles (PEVs), renewable energy resources (RES), and distributed energy storage systems (DESS) has gained significant interest under the smart grid vision. However, their special features and operational characteristics have created a paradigm shift in distribution network resource allocation studies. This paper presents a combined model formulation for the concurrent optimal resource allocation of PEVs charging stations, RES and DESS in distribution networks. The formulation employs a general objective function that optimizes the total Annual Cost of Energy (ACOE). The decision variables in the formulation are the locations and capacities of PEVs charging stations, RES, and DESS units. A Markov Chain Monte Carlo (MCMC) simulation model is utilized to account for the uncertainties of PEVs charging demand and output generation of RES units. Also, in order to enhance the accuracy of the resource allocation problem, the coordinated control of PEVs charging, RES output power, and DESS charging/discharging are incorporated in the formulated model. The formulation is decomposed into two interdependent sub-problems and solved using a combination of metaheuristic and deterministic optimization techniques. A sample case study is presented to illustrate the performance of the algorithm. [ABSTRACT FROM AUTHOR]

• A novel system is presented that predicts day-ahead demand profile and optimizes EV charging/discharging to minimize peak demand. • The system is designed to comply with preplanned EV trip schedules and minimum state of charge (SOC) requirements. • Four machine learning methods are tested to predict day ahead demand profile of a case study building at 15-minute intervals. • Five peak demand reduction scenarios are analyzed using combination of EVs, a stationary battery, and a PV system. • The results show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. Reducing electricity peak demand is essential to maintain the balance between supply and demand side in electricity power markets, as well as reduce utility costs and environmental impacts. With growth in adoption of Electric Vehicles (EVs), there is an emerging opportunity to balance electrical power demand of buildings by storing electricity in EVs during low demand periods and discharging electricity into buildings during peak demand periods. Due to uncertainty in time and magnitude of peak demand, decision makers are always faced with a challenging task to identify optimal schedules for charging and discharging EVs to minimize peak electricity demand. This paper presents the development of a novel system that is capable of predicting day-ahead building electricity demand profile and identifying optimum schedule of charging and discharging EVs to minimize electricity peak demand. The system is designed to comply with planned EV trip schedules and minimum state of charge (SOC). The system consists of (1) machine Learning (ML) model to predict electrical power demand, and (2) demand management optimization model to identify optimal schedule for charging and discharging EVs. Four methods are explored to develop the ML model, including histogram-based gradient boosting, random forest, deep artificial neural network (DNN), and long short-term memory (LSTM). A case study of multi-tenant commercial building is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The results of the case study shows that LSTM has the best performance in terms of mean absolute error, root mean square error, and mean absolute percentage error with average values of 7.44, 17.78, and 20.08 %, respectively. Five scenarios for shaving peak electricity demand, including combinations of two electric vehicles, a stationary battery, and a PV system are investigated. Scenarios including the stationary battery and the PV system are considered to evaluate the full potential of peak demand reduction in the case study building. The results of the demand management optimization model show up to 36 % reduction in peak demand using two EVs, one stationary battery, and PV system of 40 kW capacity. The key contributions that this study adds to existing knowledge are: (1) developing machine learning models to predict day-ahead electricity demand in 15-minute intervals, (2) integrating machine learning and optimization algorithms in identifying EV charging and discharging schedules to minimize utility cost by shaving peak energy demand, and (3) considering planned EV trips and minimum SOC requirements in identifying optimal charging and discharging of EVS to shave peak energy demand in buildings. The implementation of this system provides practical solutions for managing electricity demand in commercial buildings using EVs. By reducing energy consumption and promoting the innovative use of EVs, this system offers a sustainable approach to managing electricity demand in commercial buildings. [ABSTRACT FROM AUTHOR]

India’s ever increasing population has made it necessary to develop alternative modes of transportation with electric vehicles being the most preferred option. The major obstacle is the deteriorating impact on the utility distribution system brought about by improper setup of these charging stations. This paper deals with the optimal planning (siting and sizing) of charging station infrastructure in the city of Allahabad, India. This city is one of the upcoming smart cities, where electric vehicle transportation pilot project is going on under Government of India initiative. In this context, a hybrid algorithm based on genetic algorithm and improved version of conventional particle swarm optimization is utilized for finding optimal placement of charging station in the Allahabad distribution system. The particle swarm optimization algorithm re-optimizes the received sub-optimal solution (site and the size of the station) which leads to an improvement in the algorithm functionality and enhances quality of solution. The genetic algorithm and improved version of conventional particle swarm optimization algorithm will also be compared with a conventional genetic algorithm and particle swarm optimization. Through simulation studies on a real time system of Allahabad city, the superior performance of the aforementioned technique with respect to genetic algorithm and particle swarm optimization in terms of improvement in voltage profile and quality. [ABSTRACT FROM AUTHOR]

This study focused on the adoption of Plug-in Electric Vehicles (PEVs) as a policy towards having a more sustainable transportation with lower Greenhouse Gas (GHG) emissions. The current paper aimed to explore potential factors that can be attributed to purchasing PEVs in order to estimate their penetration in 58 California counties. A Multiple Logistic Regression Analysis was applied to the 2012 California Household Travel Survey dataset, which includes both PEV and conventional car buyers’ information, as well as some other secondary data sources. The model developed a broad set of factors including demographic and travel-related characteristics, socioeconomic variables, and infrastructural and regional specifications. The results identified that a household's income, maximum level of education in the household, the buyer's car sharing status, charging stations density, and gas price in the region can significantly impact PEV adoption. The model was validated using data from the 2012 Household Travel Survey conducted in the Delaware Valley region. With sufficient data availability, the methodology can be applied to evaluate changes in vehicle fleet composition and the levels of emissions in response to transportation policies. The model is believed to have a wide range of applications in electricity utilizing, gasoline/diesel retailing, and battery and automotive manufacturing. Additionally, the model can assist policy makers and transportation planners to optimize their infrastructural investments by identifying counties where the response of drivers to added charging station would be maximized, implying that larger benefits can be achieved. [ABSTRACT FROM AUTHOR]

Nowadays, Electric Vehicles (EVs) are considered a disruptive technology that will-with time-become dominant over their predecessor. EVs range and cost are closely linked, where users can essentially trade one for the other as the battery is the single largest cost of an EV. Developing countries (e.g., Jordan) face several challenges related to the current supporting environment and infrastructure. Thus, the lack of reliable charging infrastructure is a key obstacle that would slow down the EVs' expansion. This paper aims to understand the feasibility of instigating EV charge facilities based on different parameters including charging technologies, charging-power demand, and potential locations of EV charge facilities. For this purpose, the Maximum Covering Location Model (MCLM) is utilized to optimize the charging demand. This optimization model is based upon the anticipated charging demand (daytime and nighttime). Also, different charging scenarios are selected to deal with different levels of charging technologies classified based on their charging speed and power. Moreover, a random parameters approach is adopted, for the first time, to model the demand for charging power in each district. A linear mixed-integer problem with multiple constraints is also formulated to optimize the charging station location and level of coverage over a relatively large metropolitan area. Thus, the study has a novel input to the prevailing charging demand modeling from coverage and financial feasibility perspectives, as well as guides stockholders and policymakers to improve EVs' adoption. [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]

• Control authority in smart e-mobility is usually on the side of charging stations. • Electric vehicle based concept is an alternative where vehicles control charging. • In a proposed concept charging stations are merely an enabling infrastructure. • Electric vehicle based system yields higher revenues for the vehicle owners. The existing models designed to reap the benefits of electric vehicles' flexibility in the literature almost exclusively identify charging stations as active players exploiting this flexibility. Such stations are seen as static loads able to provide flexibility only when electric vehicles are connected to them. This standpoint, however, suffers from two major issues. First, the charging stations need to anticipate important parameters of the incoming vehicles, e.g. time of arrival/departure, state-of-energy at arrival/departure. Second, it interacts with vehicles only when connected to a specific charging station, thus overlooking the arbitrage opportunities when they are connected to other stations. This conventional way of addressing the electric vehicles is referred to as charging station-based e-mobility system. A new viewpoint is presented in this paper, where electric vehicles are observed as dynamic movable storage that can provide flexibility at any charging station. The paper defines both the existing system, where the flexibility is viewed from the standpoint of charging stations, and the proposed one, where the flexibility is viewed from the vehicles' standpoint. The both concepts are mathematically formulated as linear optimization programs and run over a simple case study to numerically evaluate the differences. Each of the four issues identified are individually examined and omission of corresponding constraints is analysed and quantified. The main result is that the proposed system yields better results for the vehicle owners. [ABSTRACT FROM AUTHOR]

• The capacity optimization model of the integrated photovoltaic- energy storage-charging station was built. • The case study bases on the data of 21 charging stations in Beijing. • The construction of the integrated charging station shows the maximum economic and environment benefit in hospital and minimum in residential. • A decline in energy storage costs increases the benefits of all-scale investments, an increase in electric vehicles promotes the benefits of small-scale investments, expansion of the peak-to-valley price distance increases the benefits of large-scale investments. The coupled photovoltaic-energy storage-charging station (PV-ES-CS) is an important approach of promoting the transition from fossil energy consumption to low-carbon energy use. However, the integrated charging station is underdeveloped. One of the key reasons for this is that there lacks the evaluation of its economic and environmental benefits. Based on the electricity load of different types of buildings and the data of electric vehicle charging stations in Beijing, this paper analyzes the economic and environmental benefits of integrated charging station developed over different scales by the capacity optimization model. Said benefits are shown to differ considerably with different locations (building types): the construction of the integrated charging station in places where the electricity load is high at night, and where the curve of daytime electricity consumption is consistent with the PV generation curve (e.g., hospitals), shows maximal economic benefits and carbon dioxide emissions reduction. In other (e.g., residential) areas, where power consumption is lower across both day and nighttime hours, the economic benefits and carbon dioxide emissions reduction are lowest. The economic and environmental benefits of the integrated charging station also markedly differ on different scales: with scale expansion, the rate of return on investment and the carbon dioxide emissions reduction first increase and then decrease. A decline in energy storage costs increases the economic benefits of all integrated charging station scales, an increase in EVs increases the economic benefits of small-scale investments, and expansion of the peak-to-valley price difference increases the economic benefits of large-scale investments. [ABSTRACT FROM AUTHOR]

High purchase prices and the lack of supporting infrastructure are major hurdles to the adoption of plug-in electric vehicles (PEVs). It is widely recognized that the government could help break these barriers through incentive policies, such as offering rebates to PEV buyers or funding charging stations. The objective of this paper is to propose a modeling framework that can optimize the design of such incentive policies. The proposed model characterizes the impact of the incentives on the dynamic evolution of PEV market penetration over a discrete set of time intervals, by integrating a simplified consumer vehicle choice model and a macroscopic travel and charging model. The optimization problem is formulated as a nonlinear and non-convex mathematical program and solved by a specialized steepest descent direction algorithm. We show that, under mild regularity conditions, the KKT conditions of the proposed model are necessary for local optimum. Results of numerical experiments indicate that the proposed algorithm is able to obtain satisfactory local optimal policies quickly. These optimal policies consistently outperform the alternative policies that mimic the state-of-the-practice by a large margin, in terms of both the total savings in social costs and the market share of PEVs. Importantly, the optimal policy always sets the investment priority on building charging stations. In contrast, providing purchase rebates, which is widely used in current practice, is found to be less effective. [ABSTRACT FROM AUTHOR]

Proper placement of electric vehicle supply equipment (charging stations) requires an understanding of vehicle usage patterns. Using data from the National Household Travel Survey on vehicle mileage and destination patterns, analyses were performed to determine electric vehicles' charging needs, as a function of battery size and state of charge. This paper compares electric vehicle charging needs with Department of Energy electric vehicle charging data from real-world charging infrastructure. By combining the electric vehicles charging needs with charging data from real-world applications, locations with high electric vehicle charging likelihood are identified. [ABSTRACT FROM AUTHOR]

Battery swapping stations (BSSs) and charging stations (CSs), which provide electric vehicle battery refueling services, are important participants in the electricity and carbon markets. Battery swapping stations (BSSs) and charging stations (CSs), which provide electric vehicle battery refueling services, are important participants in the electricity and carbon markets. The coordination between BSSs and CSs offers a great opportunity for enhancing system flexibility and lowering operation costs, which is underexplored in the literature. Thus, this paper proposes a cooperative operation approach for a BSS, a CS, and a group of residential buildings (RBs) in a community microgrid to minimize their operation costs. These three entities can trade electricity and carbon allowance in the central and local markets. The local electricity and carbon trading problem is formulated using Nash bargaining theory to determine the amounts of electricity and carbon allowance to be traded and the corresponding payments. The Nash bargaining problem is decomposed into an electricity and carbon allowance scheduling problem (P–S1) and a payment bargaining problem (P–S2). The P–S1 is transformed into a mixed-integer linear programming (MILP) problem using a linearization technique, while the closed-form solution to the P–S2 is derived. Compared to the noncooperative benchmark, the proposed approach reduces operation costs of the BSS, CS, and RBs by 49.85%, 67.17%, and 77.24%, and their carbon emissions by 10.52%, 26.73%, and 27.82%. • Cooperation between a battery swapping station and a charging station is proposed. • Nash bargaining theory is used to formulate local electricity and carbon trading. • A linearization technique is employed to solve the Nash bargaining problem. • Impacts of different coalition combinations and market settings are investigated. [ABSTRACT FROM AUTHOR]

• Analyze the connection and difference between for the network-based and metanetwork-based approaches for the optimal en-route charging station location problem. • Introduce subpaths and station-subpath metanetworks for modeling en-route charging station location problems; • Establish the solution equivalence between the proposed node-link network-based model and station-subpath metanetwork-based model; • Prove analytically the network cost monotonicity property with charging station addition, budget limit increase and distance limit increase; • Justify numerically the metanetwork-based algorithm outperforming the network-based algorithm in the branch-and-bound framework; and. • Reveal the impacts of budget limit and distance limit on individual route-and-charge choice and network performance and accessibility. In many regions across the world, the distribution density of electricity-charging stations is yet sufficient for taming the range anxiety concern of electric vehicle drivers. In such driving circumstances, making detours to find alternative charging opportunities is often an operational remedy to accomplish long-distance trips. To reduce the frequency and extra mileage of detouring, public charging infrastructures should be coordinately sited and constructed in a regional network. With the goal of minimizing the possible detour mileage, this paper describes an optimal charging station location problem for intercity highway networks and develops two approaches for modeling and solving it. Specifically, two mixed linear integer programming models are constructed in different network modeling paradigms: The first one is formed and solved on the basis of the original node-link network topology , with newly introduced integer variables such as path and subpath activation indicators to quantify the utilization status of paths and subpaths, respectively. The classic branch-and-bound algorithm encapsulating a bi-criteria label-correcting algorithm is designed for solving this network-based model. The other one is formulated and solved relying on the station-subpath metanetwork topology , as the result of a two-phase process that decomposes an individual routing and charging decision into two parts. The two phases are conducted on the node-link network and station-subpath metanetwork levels, respectively, where the first phase is done by solving a network-based, distance-constrained minimum-cost path problem, while the second phase collapses to a metanetwork-based, simple minimum-cost path problem. The findings we obtained through conducting this research are twofold: First, analytical and computational studies clearly identify the effectiveness of the two modeling and solution approaches, while the metanetwork-based approach exhibits its appealing computing advantage for solving problems of large size; second, the application of the solution approaches for a real-world network instance reveals how budget limit and distance limit impact the charging station locations, individual routing and charging decisions, and network cost and accessibility levels. [ABSTRACT FROM AUTHOR]

In this paper we propose an improved mathematical model for locating EV charging stations. We consider the successive activities of the travelers, i.e., a person with two main stops during the day should charge the vehicle in just one of the parking spots, hence avoiding double counting the demand. The model is tested for the city of Coimbra (Portugal), where there is a network of nine stations. We conclude that our solution is better than the one that was implemented in reality, moreover we are able to conclude that demand transference has a rather significant impact on the solutions. [ABSTRACT FROM AUTHOR]

The increasing need for reducing greenhouse gas emissions and the drive for green cities have promoted the use of electric vehicles due to their environmental benefits. In fact, countries have set their own targets and are offering incentives for people to purchase EVs as opposed to traditional gasoline-powered cars. Manufacturers have been hastily deploying charging stations to meet the charging requirements of the EVs on the road. This rapid deployment has contributed to the EV ecosystem's lack of proper security measures. raising multiple questions related to the power grid security and vulnerability. In this paper, we offer a complete examination of the EV ecosystem from the vulnerability to the attacks and finally the solutions. We start by examining the existing vulnerabilities in the EV ecosystem that can be exploited to control the EV charging and launch attacks against the power grid. We then discuss the non-linear nature of the EV charging load and simulate multiple attacks that can be launched against the power grid using these EVs. EV loads have high reactive power demand which can have a larger impact on the grid compared to residential loads. We perform simulations on two power grids and demonstrate that while the grid can recover after a 48 MW attack utilizing traditional residential loads, a smaller 30 MW EV load attack can completely destabilize the system. Finally, we suggest several patches for the existing vulnerabilities and discuss two methods aimed at detecting EV attacks. [ABSTRACT FROM AUTHOR]

• A new problem formulation allowing en-route charges as many times as needed for electric vehicles. • A bi-level integer programming model on the basis of subpath and path-subpath relationship. • Application of the emerging nested partitions algorithm and classic branch-and-bound algorithm. This paper addresses a new electricity-charging station location problem for congested intercity or regional networks, in which electric vehicles with a limited driving range require recharges for their long-distance trips. When the distribution of charging stations does not sufficiently cover all used routes, some drivers may take a detour to find charging opportunities (or switch to another transportation mode). The goal of this station location problem is to find, under a limited construction budget, an optimal set of charging station locations such that all vehicles finish their trips by choosing a self-optimal route with necessary charging opportunities while the overall network congestion caused by possible detours is minimized. The problem is written as a bi-level mixed nonlinear integer programming model, where the upper level of the model is set to regulate the selection of station locations subject to the construction budget while the lower level is used to characterize the equilibrium flow pattern of electric vehicles with the charging requirement. Selected exact and approximate algorithms, namely, the branch-and-bound algorithm and the nested partitions algorithm are adopted to solve this station location problem. While both algorithms imply a divide-and-conquer strategy, the branch-and-bound algorithm poses a deterministic, exact procedure that utilizes the bounding mechanism to prune impossible solution subspaces, whereas the nested partitions algorithm performs a stochastic search and selection process in terms of the optimality probability for near-optimal solutions. To get numerical insights on the algorithmic performance and solution behavior, we test these algorithms through a couple of benchmark network instances. A performance comparison of the algorithms indicates that, the branch-and-bound algorithm can quickly obtain the global optimum when the driving range is relatively low, while the nested partitions algorithm can find optimal solutions or extremely near-optimal solutions in all cases and typically spend on average only one fourth of the computing time of the branch-and-bound algorithm. The network flow solutions clearly show, compared to gasoline vehicle drivers, how an insufficient driving range or number of charging stations may significantly reduce the number of feasible paths and force electric vehicle drivers to choose more costly paths, which thus reshapes flow patterns and possibly increases congestion levels. [ABSTRACT FROM AUTHOR]

Fast charging stations for electric vehicles require a high-power demand, meaning that electricity distribution companies must define the connection locations within the distribution network to guarantee adequate power supply levels. Due to electric vehicle users' driving patterns and equipment's high costs, these stations must be concentrated in certain regions. This paper presents a methodology for assisting electricity distribution companies in identifying candidate connection points for fast charging stations to reduce new installations and network reinforcement investments. First, possible connection points are analyzed with graph theory to find the least costly connection; this strategy prioritizes the current network elements' unused capacity. As a second step, the electric distribution network is analyzed after fast-charging stations have been connected, evaluating the networks' operational limits. The methodology is applied in a Brazilian city combining spatial information with a realistic representation of the network and network total supply capability to connect new loads. Model outcomes are spatial maps that help identify suitable connection locations, determine new capacity values, and calculate the necessary investment. We compare the proposed methodology with other conventional approaches, demonstrating how the developed methodology can assist distribution companies in reducing overall investment and operational costs of fast charging stations for electric vehicles. • Two-stage method to assess EV fast-charging stations. • Novel local search algorithm based on graph theory and power-flow method. • Prioritizing unused network capacity can reduce expansion costs by up 17%. • Computational time reduces by 24% compared to conventional methods. [ABSTRACT FROM AUTHOR]

• 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]

• Optimal planning of charging stations (CS) along with capacitors (CAP). • Proposed technique is quantum-behaved Gaussian mutational DA (QGDA). • Parking lot and capacitor allocation is suggested for congestion management. In order to optimally determine the parking lot size, QGDA is utilized. In this paper, a new methodology for optimal planning of charging stations (CS) along with capacitors (CAP) using proposed technique is presented. In order to achieve a better balance between exploration and exploitation for dragonfly algorithm (DA), the quantum-behaved and Gaussian mutation strategies on the performance of DA is used. This is the novelty of our proposed work. Hence, it is named as quantum-behaved Gaussian mutational DA (QGDA). Here, parking lot and capacitor allocation is suggested for congestion management along with reactive power compensation. In order to optimally determine the parking lot size, QGDA is utilized. The effectiveness of the proposed technique is tested on adapted IEEE 34-bus distribution network. The result attained by QGDA technique is compared with existing techniques such as PSO and BBO. With PL, the proposed technique achieves the P loss and Q loss of 25.61 kW and 25.99KVar. PL and C at same node, the proposed technique achieves the P loss and Q loss of 31.09 kW and 32.88KVar. PL and C at different node, the proposed technique achieves the P loss and Q loss of 25.09 kW and 26.98KVar. Furthermore, the proposed results show that an uneven EV charging scenario can cause significant voltage unbalance that goes beyond its allowed limit of 2%. [ABSTRACT FROM AUTHOR]

• New operation approach for BES to satisfy customers' requirements and minimize the operation costs. • Investigate the integration of the operation of multiple batteries while being exchanged in BES, and arrivals of different customers over the day, and the grid power limitation. • The necessity of proposing a new operational approach for the BES is due to the need for a day ahead plan to decide charging, discharging, and replacing the batteries in stock of BES. However, the grid limit and demand charges should be considered in this model. Due to environmental and energy security concerns, low emission vehicles present a vital necessity for clean transportation. In particular, electric vehicles (EVs) are the most promising solution due to the fact that the electrical power system is the most ready infrastructure to supply their requirement. Two possible energy delivery solutions to the EVs, namely the charging stations and the battery exchange stations (BESs) are the focus of research nowadays. In this paper, a new optimal operation approach is proposed for the BESs. The proposed new model determines the optimal charging, discharging, and exchange decisions for the battery stock throughout the day taking into consideration the customers' arrivals, the variations in the grid price, the grid connection limitations, and the self-degradation of the batteries. The objective of the proposed approach is to maximize the BES owner profit while satisfying the EV owners' requests. The BES operation optimization problem is formulated as mixed-integer programming (MIP) problem and is solved as a day-ahead scheme. The performance of the BES is compared to conventional EV charging stations, where the BES shows superior customer satisfaction and higher profit. [ABSTRACT FROM AUTHOR]

The use of battery power is becoming widespread rapidly among the mega ports worldwide, owing its low emission and high energy efficiency. In this paper, a simulation approach is presented to configure the charging stations (CSs) and battery-powered automated guided vehicles (B-AGVs) at automated container terminals. The facility planning of the CSs and the operation strategies of the B-AGVs are crucial for the system performance of the terminal. A port system is modeled as a discrete event simulation model, mainly consisting of three parts: vessel generator, dispatcher, and traffic network. Two types of layout designs and two types of recharging policies are presented, to deploy the CSs and control the B-AGVs to be recharged, respectively. Extensive simulation experiments are conducted to analyze the parameter effects on the system performance. The results indicate that a decentralized CS layout and a progressive recharging policy lead to excellent performance. Some management insights are offered to gain a better understanding of the key factors when deploying a B-AGV system. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) owners enjoy many positive aspects when driving their cars, including low running costs and zero tailpipe gas emissions, which makes EVs a clean technology provided that they are sourced through renewable sources, e.g., biomass, solar power, or wind energy. However, their driving behaviour is often negatively affected by the so-called range anxiety phenomenon, i.e., a concern that an EV might not have enough driving range to reach the desired destination due to its limited battery size. The perception of range anxiety may also affect potential buyers in their decisions on whether to purchase an internal combustion engine vehicle as opposed to an EV. This paper investigates some factors that influence range anxiety through a comparative analysis of two target groups: (i) existing EV owners, and (ii) non-EV owners (i.e., potential EV owners). The specially crafted survey was used to collect range anxiety data from more than 200 participants. In particular, participants provided their perceptions on (i) the potential relationship between existing gas station infrastructure and the desired EV charging station infrastructure, and (ii) the potential relationship between range anxiety and two influencing variables, namely the current state of charge and remaining range. Concerning the existing gas station infrastructure, evidence suggests that both target groups think that the distances between gas stations could be increased. Moreover, our analysis shows that the desired distances between charging stations correspond to the distances between the existing gas stations, which indicates that both EV owners and non-EV owners have a common view on the optimal gas station and charging station topology. Furthermore, we find that the type of settlement (urban vs rural) influences preferred distances, where both target groups living in cities desire shorter distances, and that non-EV owners, as opposed to EV owners, are more prone to be affected by the state of charge and remaining range. Quantitatively, we are able to define a measure for range anxiety, which is connected with the preferred distance between two neighbouring charging stations. Throughout our analyses, we find that the mean preferred distance between two neighbouring charging stations is 7 km, but this value significantly differs based on the settlement type of a (potential) EV owner. • Comparative analysis of EV and non-EV owners regarding charging infrastructure. • Analysis of key EV-parameter (SoC and remaining range) influence on range anxiety. • Analysis of charging infrastructure preferences regarding settlement hierarchy; and. • Analysis of influence of EV ownership on range anxiety. [ABSTRACT FROM AUTHOR]

This paper presents a novel methodology to study the deployment of public smart charging stations (CS) of electric vehicles (EV) in a sprawling Latin American city. A relevant difference between developed and emerging economies is the reduced access to home charging in emerging economies, which is the case in Latin American cities. Thus, developing public charging stations represents a crucial factor in the mass adoption of EVs by road commuters. We develop herein a methodology for optimizing the deployment of smart charging stations under the sprawling phenomenon perspective. Our method comprises two steps. In step one, we applied principal component analysis (PCA) to facilitate the analysis of a sprawling city, and then we define candidates for potential locations from 'demand clusters' within an urbanized area, by K-means clustering analysis. In the second step, a stochastic programming model was employed to optimize the integration of infrastructural facilities with distributed energy resources (DERs) and EV charging stations using a collaborative strategy to minimize its energy consumption cost under demand uncertainty. We demonstrate the capabilities of this approach through a case study in the city of Lima. Experimental results reveal managerial insights for different stakeholders (i.e., government, industry, academia, and civil society) to promote policies, investment, and incentives. • Explores the EVs public charging stations deployment under the urban sprawl phenomenon perspective. • Principal component analysis (PCA) guided K-means clustering defines energy clusters. • Demonstrates the superiority of the PCA-based electromobility integration strategy. • Managerial insights are made to deploy EVs smart charging stations under uncertainty. [ABSTRACT FROM AUTHOR]

• Real-world functional implementation of smart charging technology. • Development of scalable electric vehicle-to-building integration platform. • Proven demand charge mitigation using real-time electric vehicle charging. • Extensive economic analysis on installation and operation cost of smart charging. Higher electric vehicle (EV) adoption will stress the importance of demand flexibility to achieve more economic, efficient, and reliable grid operation. Charging technologies will be paramount in shifting temporally to better fit the variable generation of wind and solar. Therefore, analysis is warranted on the benefits of EV charge scheduling with respect to installation cost, operation cost, difficulty of implementation, and grid flexibility. We tackle this by analyzing the cost savings of implementing an EV charge scheduling infrastructure to reduce demand charges and installation costs. In this paper, we analyze a case study for operation of 16 level 2 chargers and 1 fast charger for two different building types. We then evaluate various test phases for controlling building and charging loads using an adaptive charging network (ACN) algorithm to characterize the ACN's potential to reduce overall project cost. [ABSTRACT FROM AUTHOR]

The development of policies for sustainable development has led to an increase in electric vehicles (EVs). This has given rise to new problems, such as the location and sizing of charging stations over the considered traffic network. The solution of such problems requires the application of analysis techniques in order to predict the joint allocation of transportation and energy demands. In this paper, an approach is proposed that extends the User Equilibrium (UE) principle in order to determine, besides to the flow over the network links, the service requests from the drivers to the various service stations. The application of this method is preliminary for the statement of any problem related to the optimization of location and sizing of service stations. The proposed approach is applied to a case study in Genoa Municipality (Liguria Region). • A new model for charging stations and electric vehicles. • Traffic assignment and energy demands assessment. • User Equilibrium (UE) principle for the case of electric vehicles. • A real case study is considered. [ABSTRACT FROM AUTHOR]

• A method to size charging stations and schedule charging times of EV is presented. • 200.000 observed real parking events at the University of Salerno are analyzed. • A packetized energy approach allows for solving the charging scheduling problem. • The solution ensures a high QoS level and minimize the cost for the CS manager. • Method and results are useful to study the potential of V2G applications. The expected deployment of battery electric vehicles (BEVs) strongly depends on the development of an adequate charging station (CS) infrastructure that guarantees a certain level of quality of service (QoS) to the BEV users. This paper proposes a two-stage method to select the number and type of CSs in parking areas (PAs) and schedule the charging sessions of the incoming BEVs ensuring a predetermined QoS level while minimizing the cost for the CS manager. In particular, stage one solves the CS sizing problem while stage two involves a probabilistic simulation procedure able to solve the charging scheduling problem by using a packetized energy approach. We also take into account the typical charging current and voltage characteristic of a BEV battery pack, and the real statistical distribution of BEV arriving times and expected parking times. A case study based on the PA at the University of Salerno Campus is used to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, as a policy to increase penetration of electric vehicles and reduce air pollution, the number of electric taxis has increased in the transportation networks. In contrast to private electric vehicles, electric taxis need to recharge in a short time due to their constant operation and different driving patterns. Therefore, fast charging stations are required to meet the demand for recharging electric taxis and should be located at strategic places. To improve the sustainable planning of urban infrastructures, this paper presents a methodology to help in decision making for installing fast charging stations, considering the criteria of all urban planners involved in the installation process. The result of the proposal is a spatial database that identifies locations for charging stations that meet the predefined criteria. Such database can be visualized in geographic information systems of the urban planning department or the electrical service concessionaire. The proposed methodology is tested in a Brazilian medium-sized city showing the importance of this information in decision making. The proposal is compared with another spatial methodology, showing that the proposed method creates a better spatial distribution of charging stations giving better options for the main agents interested in the city. • The proposed helps to determine the location for fast charging stations (Fast-CS). • The spatio-temporal requirements of the planners are considered. • The proposal considers several aspects that influence the location of Fast-CS. • The proposal models traffic flows and the state of charge of electric taxis. • The results of the proposal can be processed by any Geographic Information System. [ABSTRACT FROM AUTHOR]