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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]

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]

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]

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]

• 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]

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]

• 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]

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]

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]