Charging Demand Research Articles

Optimal solution for PV integrated with smart grid connected fast charging EV station: A case study for Jaipur

Current research study investigates the optimal solution for integrating photovoltaic (PV) solar panels with a smart grid-connected fast-charging electric vehicle (EV) station in Jaipur, India, using HOMER software for simulation. The study aims to develop a cost-effective and sustainable charging infrastructure specifically designed for e-rickshaws and personal vehicles, targeting reduced electricity costs. The findings demonstrate that a hybrid Solar and Grid-integrated EV charging station significantly lowers operational costs while promoting renewable energy adoption. The optimized Solar + grid system, compared to a traditional grid-only model, achieves substantial savings, with an average annual energy bill reduction of ₹26,17,806. Despite an initial capital expenditure of ₹50,22,895, the system's financial viability is affirmed by a rapid payback period of 1.9 years (simple) or 2.1 years (discounted) and an impressive internal rate of return (IRR) of 51.3%. Over a 25-year project lifespan, the system is projected to generate total savings of ₹6,54,45,150. The analysis considers various financial metrics, including monthly and annual energy costs, demand charges, and fixed charges, providing a comprehensive overview of the economic benefits. The results underline the importance of incorporating solar power into EV charging stations to reduce overall energy expenses and enhance long-term sustainability. This study offers valuable insights for policymakers, energy managers, and stakeholders, highlighting the economic and environmental advantages of solar-integrated charging solutions in urban settings.

A novel energy management of public charging stations using attention-based deep learning model

Electricity grids are complex systems that must balance the supply and demand of electricity in real-time. However, with the increasing adoption of electric vehicles (EVs), managing the grid’s stability has become more challenging. EV charging can cause spikes in electricity demand, leading to peak demand periods that strain the power grid’s infrastructure. With the help of load forecasting, this effect on the grid can be mitigated by predicting the charging demand of electric vehicles in advance. This will help utilities adjust their energy supply in real-time, ensuring enough energy is available to meet demand, and preventing overloads or under utilization of the grid. Moreover, the EV charging demand is influenced by a wide range of factors, including charging station locations, weather, and time of day. Therefore, advanced deep learning models are required to learn these complex relationships and identify patterns in EV charging demand, enabling utilities to make more informed decisions. In this research, an attention-based deep learning approach is proposed for more accurate prediction of EV load demand. This novel approach integrates attention mechanisms with traditional deep learning models like LSTM and GRU, allowing the model to dynamically weight the importance of different features and focus on the most relevant information. The outcomes are compared to conventional deep learning and machine learning algorithms. To test the efficacy of the proposed framework, an actual ACN dataset for public EV charging stations is utilized.

Mechanism design of EVs fast charging rights for enhanced vehicle-to-grid regulation

With the advancement of policy initiatives and technological developments, vehicle-to-grid (V2G) interactions have emerged as a critical focus within power system research. Despite numerous current studies on V2G, the exploration of incentive mechanisms to encourage user participation in these systems remains comparatively underdeveloped. This study proposes an innovative demand response (DR) incentive mechanism for electric vehicles (EVs) grounded in the regulation of fast charging powers via strategically assigning and allocating virtual points defined as fast charging right (FCR). The primary objective of this mechanism is to meet the power regulation requirements of DR while simultaneously balancing the grid’s response costs and accommodating the charging demands of EV users. Through simulation and comparative analysis with the existing pilot DR incentive mechanisms, the results demonstrate that the proposed mechanism outperforms the current model in three critical aspects: power regulation efficacy, cost-effectiveness, and user experience. In addition, this paper investigates the performance of the incentive mechanisms in both online and offline scheduling environments. The findings reveal that the FCR mechanism maintains robust power regulation performance even under online scheduling conditions.

Unraveling the effect of electricity price on electric vehicle charging behavior: A case study in Shenzhen, China

Estimating price elasticity of demand for electric vehicle charging contributes to the accurate determination of charging price, thereby improving electric vehicle adoption and energy sustainability. However, few studies have studied the impact of electricity price on electric vehicle charging behavior, especially the demand spillover effect caused by price fluctuations. To fill the gaps, on a citywide dataset of public charging piles in Shenzhen, China, first, correlation coefficients and hypothesis tests are used to determine the relationship between charging demand and price. A learning model incorporating two-layer graph attention, temporal pattern attention, and knowledge-embedded meta-learning is developed for accurate spatio-temporal regression. Impulse response analysis is conducted to unravel several noteworthy phenomena: (1) public charging demand is inelastic to electricity price, with an average elasticity of −0.76, and distinction between different functional areas and times is revealed; (2) negative price impulses marginally change the elasticity, while positive ones make electric vehicle charging users more price sensitive, and (3) the spillover effects caused by price increases and decreases bring 89.48% and 53.88% of its local demand changes to neighbors, respectively, with a scope of 3.45 kilometer. These findings provide policy implications for promoting electric vehicle charging to facilitate renewable energy transition.

Flexibility justice: Exploring the relationship between electrical vehicle charging behaviors, demand flexibility and psychological factors

The adoption of electric vehicles (EVs) is transforming the landscape of energy consumption. While the technical and economic dimensions of EV adoption are increasingly well understood, the aspect of justice in demand flexibility remains underexplored. This study examines the complex relationship between flexibility in EV charging behaviors and the influence of socio-psychological and justice factors. We explore a range of demographic and social-psychological variables including charging anxiety, environmental concerns, perceived cost-saving perception, perceived privacy, and trust in utility providers. Our results reveal that these variables positively influence the changes in charging habits, including time-shifting and load-reduction. This study also uncovers disparities in charging behavior adjustments across various demographics groups. For instance, White respondents are more likely to charge their EVs during off-peak hours than their non-White counterparts and homeowners show a greater intention to reduce EV charging load during peak hours compared to renters. Additionally, high-income individuals exhibit a stronger willingness to shift charging times to off-peak, with White respondents within the high-income group being the most likely to reduce the amount of charging load during peak hours. Conversely, low-income White respondents are less inclined to make such adjustments. These disparities are likely tied to socioeconomic status, as more vulnerable groups often face greater constraints in adjusting their schedules. Therefore, it is imperative that policies prioritize flexibility justice by addressing the specific needs and behaviors of vulnerable groups, aiming to mitigate the additional burdens resulting from their limited flexibility.

Optimization of the power–transportation coupled power distribution network based on stochastic user equilibrium

With the increasing penetration of electric vehicles (EVs) in road traffic, the spatial and temporal stochasticity of the travel pattern and charging demand of EVs as a mode of transportation and an electrical load have generated different degrees of congestion impacts on both the power grid and the transportation network. Based on this, this paper proposes a power–transportation-coupled distribution network optimization strategy based on stochastic user traffic equilibrium theory. First, a stochastic user equilibrium-mixed traffic flow allocation model that expresses user non-completely rational path selection behavior is established, and the traffic flow equilibrium solution is obtained using an improved method of successive algorithm and mapped to charging loads. Second, a distribution network power flow optimization model under the coupled power–transportation architecture is established to optimize the operation state of the distribution network by combining distributed resources such as energy storage, demand response loads, wind power, photovoltaic, and gas turbines.

High efficiency three‐dimensional wireless power transfer system using cylindrical transmitting coil

AbstractThree‐dimensional (3D) wireless power transfer (WPT) is an attractive methodology to improve the charging safety and flexibility of portable electronic equipment and to meet the charging demand brought by the increase of electronic equipment. Herein a 3D WPT system using a cylindrical transmitting coil excited by a single power source is proposed to improve flexibility and efficiency. Specifically, the coil size is designed and the circuit model is analyzed. A method for determining inductor parameters of Inductor‐Capacity‐Capacity compensation is designed to achieve high efficiency. The power transmission performance of the WPT system is validated through an experimental platform. The measured maximum power is more than 50 W, and the maximum DC‐DC efficiency is up to 82.3%. The system can simultaneously charge multiple loads with different receiving coils at different positions. The proposed WPT system can improve the flexibility of equipment charging without control, and provide a reference for the design of portable equipment high efficiency wireless chargers.

Grid-integrated solutions for sustainable EV charging: a comparative study of renewable energy and battery storage systems

IntroductionThe integration of electric vehicles (EVs) into the power network challenges the 1) grid capacity, 2) stability, and 3) management. This is due to the 1) increased peak demand, 2) infrastructure strain, and 3) intermittent charging patterns. Previous studies lack comprehensive integration of renewable energy and battery storage with EV charging.MethodsTo address these challenges, this study explores the effectiveness of incorporating renewable energy resources (RERs) and battery energy storage systems (BESS) alongside the traditional grid. The proposed study utilizes the HOMER Grid® and conducted a comprehensive analysis.ResultsThe proposed study compares two grid integrated scenarios: 1) Case-1 (grid and photovoltaic (PV) systems), and 2) Case-2 (grid, PV systems, and BESS). Both these scenarios are compared against a Base case relying solely on grid power. The evaluation employed techno-economic analysis while focusing on 1) net present cost (NPC), 2) cost of energy, and 3) annualized savings. Additionally, the proposed study analyzed 4) seasonal variations in EV charging demand, 5) grid interactions, 6) PV production, and 7) the operation of BESS in both summer and winter. The comparative analysis reveals that the Base case incurs a net present cost (NPC) of $546,977 and a cost of energy (COE) of $0.354 per kWh. In contrast, Case-1, which integrates a 100 kW PV system, shows a significantly lower NPC of -$122,962 and a reduced COE of -$0.043 per kWh, with annualized savings of $61,492. Case-2, incorporating both the 100 kW PV system and a BESS with a capacity of 9.8 kWh, has a higher NPC of $309,667 but a COE of $0.112 per kWh and provides annual savings of $51,233 compared to the Base case.DiscussionSeasonal analysis highlights that Case-2 achieves the lowest carbon emissions in summer, ranging from 2.0 to 2.5 tons, while Case-1 shows the lowest emissions in winter, ranging from 3.2 to 3.4 tons. This model 1) reduces operational costs, 2) minimizes carbon emissions, while 3) making it compelling for future energy systems in increasing EV adoption.

An Analysis of the Electric Vehicle Charging Demand in Lanzhou (China)

An Analysis of the Electric Vehicle Charging Demand in Lanzhou (China)

Privacy-preserving coordination of power and transportation networks using spatiotemporal GAT for predicting EV charging demands

The gradual replacement of conventional-fuel vehicles by electric vehicles (EVs) in recent years provides a growing incentive for the collaborative optimization of power distribution networks (PDNs) and urban transportation networks (UTNs). However, the implementation of a centralized optimization model for PDNs and UTNs is currently unrealistic due to the requirement for establishing information privacy barriers between the two independently operated networks. The present work proposes a predict-then-optimize (PTO) framework that combines Spatiotemporal Graph Attention Network (STGAT) with enhanced queueing theory to achieve accurate prediction of EV charging demands. Building upon this, it efficiently coordinates optimization between the PDN and UTN, while preserving the information privacy of both independently operated networks. Specifically, the proposed STGAT model predicts vehicle flow by simultaneously exploiting the dynamic temporal and spatial dependencies in the historical traffic data of the target UTN. It then converts the predicted flow into spatiotemporal EV charging demands using an enhanced queuing model that considers the service capacity constraints of charging stations (CSs) and the behavior of EV users. Subsequently, the predicted EV charging demands are integrated into a multi-period PDN scheduling model. The results of numerical computations based on an IEEE 33-bus PDN and real-world traffic flow datasets demonstrate that the scheduling results provided by the proposed approach differ by only 0.5% compared to results obtained when applying actual EV charging demands.

Empowering E-mobility: Day-ahead dynamic time of use tariff for electric vehicle charging

The greenhouse gases and preservation of the environment have been the most prioritized concern in many countries and international organizations, leading to set aims and initiatives for lowering the dependency on fossil fuels and minimizing carbon emissions. As a part of many initiatives, great emphasis is given to promoting electric vehicles (EVs) over their traditional carbon fuel-based counterpart. To support large-scale EV adoption, a dynamic tariff scheme is required that addresses complex issues like user comfort, savings, and utility revenue. In this research work, a dynamic pricing scheme is modeled for the centralized EV charging stations (EVCS) on a residential feeder. This pricing scheme integrates the existing static time of use (ToU) pricing to an hourly dynamic ToU tariff that varies according to the demand level in the residential feeder. The proposed tariff plan provides a day-ahead dynamic structure with a probable price variation range to aid the EV owners and the day-ahead charging mechanism to schedule the EVs beforehand. The developed scheme calculates the returns and adjusts the tariff including the demand charge and the recovery component on a daily, monthly, and yearly basis for the unsatisfactory investment revenue. Also, user convenience is ensured by setting the minimum possible tariff as well as satisfying the minimum revenue target with the integration of particle swarm optimization (PSO) in designing the pricing structure. The simulation results demonstrate the effectiveness of the proposed dynamic pricing scheme maintaining a trade-off between the interests of all parties involved. Moreover, the simulation outcome of the test case indicates that EV owners can save more than 32 % on charging bills if they plan correctly. On the other hand, the utility can earn more than 2.4 times the existing rate during the peak time of the residential feeder.

Electric Vehicle Charging Load Demand Forecasting in Different Functional Areas of Cities with Weighted Measurement Fusion UKF Algorithm

The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting.

User-needs-oriented shared DC charging resources optimal configuration and operation for improving EV penetration in old residential communities

The charging limitation of electric vehicles (EVs) in old residential communities (ORCs) derived from insufficient electricity capacity and parking spaces significantly impacts residents' quality of life and impedes urban sustainability. Considering economic and social factors, ORCs are likely to persist worldwide for the foreseeable future. To tackle these challenges, this paper proposes the scheme of shared DC fast charging stations to address the EV charging issues in ORCs. Specific methods involve minimizing the charging station spaces and charging infrastructure investment, while optimizing the photovoltaic (PV) and energy storage capacity. Additionally, a coordinated charging strategy based on dynamic price is designed to guide users in choosing the initial charging time, enhancing EV charging infrastructure utilization and EV penetration. This work is predominantly driven by human needs, integrating surveys on human behavior related to charging station locations and charging times. This case study is based on a real-life ORC in Shenzhen, China. By utilizing the proposed dynamic-pricing coordinated charging strategy tailored to the station capacity, the maximum EV penetration rate is to be achieved with satisfying EV charging demand. Furthermore, the implementation of dynamic pricing effectively guides users to adjust charging behavior, achieving the peak-shaving and valley-filling performance of load curves.

A two-layer planning method for location and capacity determination of public electric vehicle charging stations

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

Data-driven vulnerability analysis of shared electric vehicle systems to cyberattacks

Disruptions to electric vehicle charging infrastructure can hinder consumers’ confidence and impact the performance of shared electric vehicle (SEV) systems. This study focuses on SEV systems’ vulnerability to emerging cyberattacks at the system level utilizing city-scale SEV trajectory data in Beijing, China. A data-driven simulation model is developed to account for realistic charging demand–supply interactions extracted from the data. The results show that among different types of disruptions, cybersecurity threats present unique challenges to SEV systems. Compared with attacking the most highly utilized charging stations, attacking popular ones on the city’s outskirts is more likely to significantly increase stress on the charging infrastructure and reduce the accessibility of SEVs. Meanwhile, the interactions between SEVs and infrastructure could either amplify the impacts by triggering cascading failures or relieve the adversarial effects. These findings provide insights into designing policies and solutions to enhance the resilience of SEV systems against cyberattacks.

A DQN based approach for large-scale EVs charging scheduling

This paper addresses the challenge of large-scale electric vehicle (EV) charging scheduling during peak demand periods, such as holidays or rush hours. The growing EV industry has highlighted the shortcomings of current scheduling plans, which struggle to manage surge large-scale charging demands effectively, thus posing challenges to the EV charging management system. Deep reinforcement learning, known for its effectiveness in solving complex decision-making problems, holds promise for addressing this issue. To this end, we formulate the problem as a Markov decision process (MDP). We propose a deep Q-learning (DQN) based algorithm to improve EV charging service quality as well as minimizing average queueing times for EVs and average idling times for charging devices (CDs). In our proposed methodology, we design two types of states to encompass global scheduling information, and two types of rewards to reflect scheduling performance. Based on this designing, we developed three modules: a fine-grained feature extraction module for effectively extracting state features, an improved noise-based exploration module for thorough exploration of the solution space, and a dueling block for enhancing Q value evaluation. To assess the effectiveness of our proposal, we conduct three case studies within a complex urban scenario featuring 34 charging stations and 899 scheduled EVs. The results of these experiments demonstrate the advantages of our proposal, showcasing its superiority in effectively locating superior solutions compared to current methods in the literature, as well as its efficiency in generating feasible charging scheduling plans for large-scale EVs. The code and data are available by accessing the hyperlink: https://github.com/paperscodeyouneed/A-Noisy-Dueling-Architecture-for-Large-Scale-EV-ChargingScheduling/tree/main/EV%20Charging%20Scheduling.

A Vehicle-to-Grid planning framework incorporating electric vehicle user equilibrium and distribution network flexibility enhancement

The rapid surge in electric vehicle (EV) adoption, coupled with advancements in charging technologies, emphasizes the critical necessity for expanding EV recharging infrastructure. Simultaneously, the Distribution Network (DN) encounters escalating challenges in meeting charging demand during peak traffic periods. Consequently, there is a mounting demand for the deployment of innovative Vehicle-to-Grid (V2G) technologies to augment the DN’s flexibility in power dispatch and alleviate travel costs for EV users. Hence, this paper proposes an EV-user-equilibrium-(UE)-constrained V2G planning framework that enhances flexibility in the DN. The framework aims to ascertain the optimal placement and capacity of EV charging stations (EVCSs) and V2G charging piles within the Transportation Network (TN). It takes into account the equilibrium condition stemming from competitive EV charging and routing behaviors alongside the optimal expansion of DN energy resources to accommodate the electricity supplied by the V2G piles. This study commences by analyzing EV drivers’ travel decisions, considering the influence of charging and V2G pile locations and sizes. Subsequently, we tackle the Traffic Assignment Problem with User Equilibrium (TAP-UE) model to characterize the steady-state traffic flow distribution of EVs. Following this, we formulate the optimization model for the Coordinated Power and Transportation Network (CPTN), which encompasses the optimal expansion of DN facilities and traffic flow regulation under UE conditions. To mitigate the computational complexity associated with the V2G planning model, we introduce a series of linearization methods to obtain a manageable Mixed-Integer Linear Programming (MILP) solution. Finally, to validate the efficacy of our proposed planning framework, we apply it to two test systems, including a real-world case study. Through these case studies, we explore the necessity and potential benefits of V2G technologies.

Harmonics mitigation in distribution networks comprising smart online electric vehicles chargers based on equal sharing algorithm

This study focuses on analyzing the distribution network's performance due to the scheduled integration of electric vehicles (EVs). In this context, an online equal sharing scheduled algorithm is utilized to monitor and manage the charging process of EVs inside the charging stations (CSs). By this way, the planned charging demand may aid in minimizing the power stresses at the point of common coupling (PCC). Afterwards, the penetration of CSs into practical distribution networks is investigated and resolved from power quality perspectives. Meanwhile, voltage and current harmonic distortions of 3.3 % and 8.3 %, respectively resulted at the PCC due to installing these harmonic injection sources. It is worth mentioning that diverse topologies of passive filters are interrogated in this research for harmonics mitigation purposes. Single tuned passive filters demonstrate their superiority over other architectures in harmonics suppression with the simplest and most cost-effective solution. Additionally, the overloading stresses over the installed package substation of about 7 % are also alleviated permitting the CS to charge with full capacity. Finally, this work presents a forward step in the fields of EVs smart charging and harmonics mitigation in practical distribution gids.

Optimal charging of electric vehicles in smart stations and its effects on the distribution network using meerkat optimization algorithm

The proliferation of electric vehicles (EVs) presents a significant challenge and opportunity for the energy sector. This study proposes a novel approach for optimizing EV charging within smart stations, considering its impact on the distribution network. Leveraging the Meerkat Optimization Algorithm (MOA), we address the complex optimization problem of balancing EV charging demands with grid constraints. Navigating distribution grid energy management complexities, including renewable resources and dynamic demand, is challenging. We introduce a sophisticated optimization model tailored for grid operations, featuring meticulous formulations for energy management. The model optimizes battery usage, EV energy management, compensator utilization, and distributed generation dispatch. Through extensive simulations, we demonstrate the approach's effectiveness in minimizing charging costs, reducing grid congestion, and enhancing overall system performance. The multi-objective function minimizes energy losses, power purchases, load curtailment, distributed generation, and battery/EV expenses over 24 h. Simulations validate a significant reduction in the distribution grid's operating cost. This research highlights the potential of advanced optimization techniques in smart charging infrastructure to facilitate widespread EV adoption while ensuring grid reliability and efficiency. Incorporating electric vehicles (EVs) into the system yields significant improvements across performance indicators compared to scenarios without EVs. Results indicate a 19 % reduction in the objective function value, with a notable 74 % decrease in energy purchase and a 60 % reduction in energy losses. Additionally, load shedding decreases by approximately 75 %, while voltage deviation decreases by around 44 %. Importantly, no PV or WD curtailment is observed with EV integration, showcasing its compatibility with renewable energy generation profiles and emphasizing its potential to enhance system efficiency, reliability, and sustainability.

Bayesian optimization for battery electric vehicle charging station placement by agent-based demand simulation

The optimal placement of electric vehicle charging infrastructure (EVCI) is critical for meeting the charging demand of battery electric vehicles (BEVs). However, the questions of how to coordinate the stochastic charging demand and placement of charging infrastructure in an economically effective way and how to solve such a problem on a large real-world scale remain unanswered. This study addresses these questions by developing an agent-based charging station placement model (ACPM) that incorporates demand uncertainty by simulating the behaviors of BEV users. A solution algorithm based on random embedding Bayesian optimization (REMBO) is proposed and shown to significantly improve computational efficiency and solve the ACPM on a large real-world scale. The model and algorithm are applied to a case study of the Atlanta metropolitan area. In the study, the algorithm finds an optimal placement using only 2% of the runtime of existing benchmark methods. This enables the use of the algorithm on a more complex, real-world scale. Results from the case study indicate that the optimal placement of EVCI uses level-2 and direct-current fast charging (DCFC) roughly equally and distributes these relatively evenly throughout the region. This optimal EVCI placement has significant economic benefits because it can improve the expected net present value (NPV) by 50% to 100% relative to common benchmark charging placement practices, such as placing the EVCI uniform randomly or weighted by the number of parked vehicles. Finally, a sensitivity analysis demonstrates that factors such as the size of the BEV market, charging preferences, and charging price are shown to have significant impacts on the optimal placement and profitability of an EVCI project.