High Penetration Of Electric Vehicles Research Articles

Multisectoral Emission Impacts of Electric Vehicle Transition in China and India.

Transitioning to electric vehicles (EVs) is a central strategy for reducing carbon dioxide and air pollutant emissions. Although the emission impacts of reduced gasoline combustion and increased power generation are well recognized, the impacts of growing EV manufacturing activities remain understudied. Here, we focus on China and India, two of the fastest-growing EV markets. Compared to a 2030 baseline scenario, we find that national emissions of air pollutants could increase in certain high EV penetration scenarios as a result of the emission-intensive battery material production and manufacturing processes. Notably, national sulfur dioxide emissions could increase by 16-20% if all batteries have nickel- and cobalt-based cathodes and are produced domestically. Subnational regions that are abundant in battery-related minerals might emerge as future pollution hotspots. Our study thus highlights the importance of EV supply chain decisions and related manufacturing processes in understanding the environmental impacts of the EV transition.

Study of an Impedance Function for Mixed Traffic Flows Considering the Travel Time–Cost Characteristics of Long-Distance Electric Vehicle Trips

To quantify the travel time and cost characteristics of mixed traffic involving electric vehicles (EVs) and fuel-powered vehicles on roads, in this paper, we comprehensively consider three factors affecting road impedance: queue length, waiting time, and service rate. Initially, a time characteristic function and a cost characteristic function for mixed traffic impedance are constructed. From the perspective of travel time, we consider the impact of EV penetration on the actual road capacity and introduce a capacity coefficient to modify the BPR (Bureau of Public Roads) road impedance function. Given that different types of vehicles might need to wait at charging stations, we employ queuing theory to calculate the queuing time at these stations and construct an impedance model that considers travel time. From the perspective of travel costs, we account for the energy consumption costs and road usage fees for different types of vehicles. The energy consumption cost for travel mileage is obtained by multiplying the unit mileage energy consumption cost of mixed traffic by the travel mileage. For road usage fees, we adopt the conventional method of multiplying the per-kilometer rate for each vehicle type by the travel mileage, thus constructing an impedance model that incorporates travel costs. Finally, in the numerical analysis section, based on the vehicle travel mileage, we categorize travel into short-, medium-, and long-distance trips for analysis. With the constructed mixed traffic impedance model, we conduct a detailed analysis of the travel time and cost characteristics of mixed traffic over different travel distances. We explore the specific impacts of the electric vehicle penetration rate, traffic flow volume, and travel mileage on road impedance. The results indicate that as the penetration rate of electric vehicles increases, the total energy consumption of the transportation system significantly decreases. Moreover, at high electric vehicle penetration rates, although an increase in traffic flow leads to higher traffic impedance and longer travel times, the overall travel costs are reduced. This demonstrates that increasing the penetration rate of electric vehicles positively contributes to reducing the energy consumption and costs of transportation systems.

Suitable Site Selection of Public Charging Stations: A Fuzzy TOPSIS MCDA Framework on Capacity Substation Assessment

The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population’s range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves.

Load redistribution attack for power systems with high penetration of EVs

This paper provides insights for cyber defenders of power systems with high penetration of electric vehicles (EVs) by proposing a novel consecutive attack model based on load redistribution for price control in both transmission networks (TNs) and distribution networks (DNs). The target of the attack is to induce massive EV users in DNs to charge simultaneously and cause a spike of loads in the TN at peak hours. The problem is formulated as a multi-slot bi-level optimization problem, where the upper level describes the hacker behavior and attacking constraints. The lower level explains the TN operator and DN operators’ behaviors and the relationship between the local marginal price and retail charging price. The bi-level problem is converted into an equivalent single-level mix-integer linear problem and is solved based on greedy algorithm. Simulations on IEEE 30-bus system prove the effectiveness of the attack strategy.

Unlocking Flexible Electric Vehicle Charging via New Rate Design

A high penetration of electric vehicles (EVs) will deeply impact the management of electric power systems. The risk of not providing adapted EV pricing signals can lead to inefficient investments in grid infrastructure. To avoid costly grid reinforcements and to ensure proper guidance for EV charging, a solution allowing customers to access EV-only rates without installing a separate meter, which we refer to as submetering, is an attractive option for EV owners and grid operators. We develop a game-theoretical model expressed and treated as a mathematical program with equilibrium constraints (MPEC) to capture the interaction between a national regulatory authority (NRA) designing these tariffs and heterogeneous agents. This framework represents a stylized regulatory setup applicable to several European countries. First, we analyse the conditions in which EV-only tariffs can be applied for domestic charging by comparing different energy profiles. Second, we study the impact of EV charging on different tariff structures to identify the most efficient way of recovering network costs. We found that the adoption of submetering under a pure volumetric tariff can bring yearly gains varying from [Formula: see text] to [Formula: see text] to consumers with EV. Finally, we derive policy implications from the results.

Optimal Allocation of Fast Charging Stations on Real Power Transmission Network with Penetration of Renewable Energy Plant

Because of their stochastic nature, the high penetration of electric vehicles (EVs) places demands on the power system that may strain network reliability. Along with increasing network voltage deviations, this can also lower the quality of the power provided. By placing EV fast charging stations (FCSs) in strategic grid locations, this issue can be resolved. Thus, this work suggests a new methodology incorporating an effective and straightforward Red-Tailed Hawk Algorithm (RTH) to identify the optimal locations and capacities for FCSs in a real Aljouf Transmission Network located in northern Saudi Arabia. Using a fitness function, this work’s objective is to minimize voltage violations over a 24 h period. The merits of the suggested RTH are its high convergence rate and ability to eschew local solutions. The results obtained via the suggested RTH are contrasted with those of other approaches such as the use of a Kepler optimization algorithm (KOA), gold rush optimizer (GRO), grey wolf optimizer (GWO), and spider wasp optimizer (SWO). Annual substation demand, solar irradiance, and photovoltaic (PV) temperature datasets are utilized in this study to describe the demand as well as the generation profiles in the proposed real network. A principal component analysis (PCA) is employed to reduce the complexity of each dataset and to prepare them for the k-means algorithm. Then, k-means clustering is used to partition each dataset into k distinct clusters evaluated using internal and external validity indices. The values of these indices are weighted to select the best number of clusters. Moreover, a Monte Carlo simulation (MCS) is applied to probabilistically determine the daily profile of each data set. According to the obtained results, the proposed RTH outperformed the others, achieving the lowest fitness value of 0.134346 pu, while the GRO came in second place with a voltage deviation of 0.135646 pu. Conversely, the KOA was the worst method, achieving a fitness value of 0.148358 pu. The outcomes attained validate the suggested approach’s competency in integrating FCSs into a real transmission grid by selecting their best locations and sizes.

Towards sustainable urban living: A holistic energy strategy for electric vehicle and heat pump adoption in residential communities

Electric vehicles (EVs) and heat pumps (HPs) are key in reducing carbon emissions from transportation and domestic heating, yet their adoption may increase peak load demands on electrical networks. One of the aims of this research is to assess the potential impact of uncontrolled EV charging on community-scale distribution networks, exploring how this could stress the existing electrical infrastructure. It also explores the role of EVs in Vehicle-to-Grid (V2G) and smart charging applications, aiming to enhance community distribution systems. The study investigates the maximum stabilisation level achievable under various scenarios, highlighting the importance of smart energy management in integrating renewable energy and addressing uncertainties in the modelling process. Additionally, this study discusses the proposed systems' scalability, consumer behaviours' impact on the suggested energy solutions, and the potential implications of recent technological advancements for simulated communities. The research employs a sophisticated, integrative approach, combining stochastic methods with several robust energy software. Key findings suggest that uncontrolled EV charging can lead to grid capacity issues at high EV penetration levels, particularly during colder months. While smart charging and V2G technologies can moderate peak loads in many scenarios, achieving 100 % sustainable technology integration requires enhanced energy management or increased network capacity, especially in winter. Wind and solar power integration demonstrates strategic complementarity, particularly in winter, enhancing the reliability and stability of the community grid. It is also observed that peak solar generation hours misalign with the community's highest demand times, posing challenges for solar energy utilisation in EV charging in residential-based areas.

MAGE: Microgrids with Advanced Grid Efficiency through Battery-Aware EV management

Higher penetration of Electric Vehicles (EVs) yields a positive impact by curbing pollution. However, this rapid proliferation of EVs presents several challenges to the efficient management of EVs and the existing grid infrastructure due to the very high energy requirement of newly adopted EVs, which can also act as energy resources. This work delves into the problem of intelligent charging–discharging of EVs in a single offline microgrid framework, which can partially cater to the energy needs of other EVs (V2V, vehicle-to-vehicle), grid (V2G, vehicle-to-grid), building (V2B, vehicle-to-building), etc. This approach also preserves the battery’s health by limiting the number of charging–discharging cycles. Hence, a battery degradation-aware energy scheduler named MAGE (Microgrids with Advanced Grid Efficiency through Battery-Aware EV Management) is formulated to minimize the microgrid’s operational cost (MOC). The MOC is comprised of — the energy procurement cost, the cost of surplus energy sales, and penalties for unsatisfied demands. A formulation based on Mixed-Integer Quadratic Programming is proposed to ascertain an optimal solution. Heuristic algorithms are designed to identify a reasonable solution quickly. A comprehensive experimental analysis of our proposed approach exhibits its potential in optimizing MOC and ensures grid efficiency by reducing the peak load and fattening the load curve.

Distributed Energy Resource Deployment to Mitigate Adverse Effects of High EV Penetration in Unbalanced Distribution System by Analytical Sensitivity-Based Optimization

The rapid integration of electric vehicles (EVs) into distribution systems in response to environmental concerns poses challenges for power system operators. This article addresses the operational difficulties faced by unbalanced distribution systems (UDS) when incorporating three-phase EV loads. We propose a novel method for supporting EV integration and enhancing UDS operational indicators, including voltage unbalance, bus voltage stability, and bus voltage deviation. Our approach utilizes analytical loss sensitivity index (ALSI)-based meta-heuristic wild horse optimization for the optimal allocation of distributed energy resources (DERs) and capacitor bank during EV load connections. The ALSI is employed to identify potential DER locations, streamlining the computation process and reducing search space. Tested on a modified 123-bus UDS, the proposed method demonstrates a 76.71% reduction in total active power loss and a 96.43% reduction in total voltage deviation. The results underscore the effectiveness and applicability of the proposed approach. While showcasing promising outcomes, the article also discusses the potential limitations and implications for practical implementation.

Enhancing Resilience With Electric Vehicles Charging Redispatching and Vehicle-to-Grid in Traffic-Electric Networks

Electric vehicles (EVs) can be viewed as both electric loads and a type of mobile storage resource. Effective management and utilization of EVs can yield significant benefits for networks. This paper proposes a novel resilience enhancement method for coupled traffic-electric networks with a high penetration of EVs, incorporating two specific strategies in response to contingencies. The first strategy involves rerouting and reselecting charging stations for power-deficient EVs, while the second entails redispatching power-sufficient EVs to the most suitable nearby charging stations to provide grid support through vehicle-to-grid technology. A carefully designed pricing mechanism, which includes imposing additional road congestion tolls and charging station charging fees, is incorporated to facilitate rerouting and charging station reselection for EVs under the user equilibrium principle. Furthermore, a formulation incorporating the two proposed resilience enhancement strategies for coupled traffic-electric networks is presented. This formulation enables coordination between the two networks to capitalize on flexibility derived from sector coupling. A series of linearization techniques are then introduced to transform the original problem into a mixed-integer quadratically constrained programming model. Numerical results based on a coupled network with 20 traffic roads and 21 electric buses are presented to demonstrate the effectiveness of the proposed resilience enhancement method.

A Bi-level optimization strategy for electric vehicle retailers based on robust pricing and hybrid demand response

The high penetration of electric vehicles (EVs) poses both opportunities and challenges for power systems. EV retailers, playing a critical role in the demand response mechanism, face market risks caused by uncertainties in electricity consumption and prices. To address the operational issues of large-scale EVs and tap the potential of EV retailers, a temporal and spatial domain-based optimization strategy is proposed, which is implemented on bi-level (referring to transmission and distribution grid networks). First, a physical scheduling model of the grid is established, consisting of a novel hybrid demand response mechanism considering the incentives of EV retailers and the retail electricity price of EV users, and a new robust retail electricity pricing strategy handling the uncertainties of EV behavior and the electric market. Then, a bi-level optimization strategy is presented: at the upper level, in the transmission network, based on the robust pricing strategy, a unit commitment model that coordinates the hybrid demand response with other distributed energy resources is designed to optimize load periods of EVs in the time domain; at the lower level, in the distribution network, an optimal power flow model is proposed to spatially dispatch the location of EV loads. The impacts of retail price profile, EV penetration, hybrid demand response mechanism, and EV load location are analyzed in ten tests using the IEEE 33 distribution network. Simulation results indicate that the robust pricing strategy can effectively handle uncertainties, the integration of the hybrid demand response mechanism into scheduling can ensure the benefits of all participants, and the bi-level optimization strategy can accommodate distributed energy resources temporally and spatially.

A convex multi-objective distributionally robust optimization for embedded electricity and natural gas distribution networks under smart electric vehicle fleets

The indisputable environmental concerns have forced the imminent proliferation of renewable energy sources (RES) and electric vehicles (EV). However, the high penetration of such uncertain and variable sources, can pose significant challenges for maintaining supply–demand balance in electrical distribution networks (EDNs). To address these challenges, this paper presents a distributionally robust optimization (DRO) method for multi-objective scheduling in integrated electricity and natural gas distribution networks (IENGDNs). The proposed approach aims to minimize environmental-economic objectives while taking into account the high penetration of EVs and RESs. The impact of a smart EV charging strategy is evaluated to reduce operating costs and maximize the use of RESs. Additionally, demand response programs (DRPs) are used in the EDN to prevent overlapping of peak load hours between the EDN and natural gas distribution network (NGDN). Linepack technology is also used to store natural gas in NGDN pipelines, which increases the short-term flexibility of the entire IENGDNs. The proposed problem is mathematically structured as a second-order conical programming (SOCP) model to benefit from the reliable and efficient convex optimization solution. The simulations were conducted on a 123-EDN and a 40-NGDN systems. Different simulation cases show that the proposed economic-environmental framework can bring down the total emissions by 10.02%.

Analysis of voltage control using V2G technology to support low voltage distribution networks

AbstractThe decarbonization of the power generation and transport sector encourage the analysis of connection of distributed energy resources (DER), such as electric vehicles (EVs), to the electrical system, as well as the evaluation of their impact on smart cities. A better understanding of the negative impacts on the power systems will lead to propose mitigation measures and eventually revolutionize the way distributed generation works. This paper aims at modelling and evaluating the impact of EVs on a real distribution network. The energy system chosen operates at 60 Hz, 34.5 kV (medium voltage) and 0.208 kV (low voltage) and it is simulated using PSCAD/EMTDC. To reproduce realistic user consumption profiles, dynamic load profiles based on EV owners behaviour have been simulated. The vehicle‐to‐grid (V2G) technology is modelled to mitigate the impacts of high penetration of EVs by supporting the network from undervoltage. The results show the importance of active management in modern power systems, especially considering the increase in DER penetration expected for the coming years. This work shows the benefits of implementing V2G technology while highlighting the challenges involved in a real case.

Comparative analysis of unidirectional and bidirectional electric vehicle charging stations (EVCS) optimal configuration in an IEEE 37-bus feeder system using Genetic Algorithm

Various power system problems and challenges may arise in the future due to the large scale of deployment of electric vehicles (EVs). Hence, the proper placement of EV charging stations (EVCS) effectively mitigates the impact of high EV loads connected to the grid. The research intends to explore and analyze differences between the regulation effectiveness of unidirectional and bidirectional charging technologies by utilizing different comparison evaluation indices. Moreover, considering their penetration level, this study tackles the impact analysis of EV and EVCS integration through time. Specifically, this paper aims to identify the optimal EVCS sites in an IEEE 37-bus test feeder system to minimize power loss brought by EV integration. Through MATLAB R2022b simulation and OpenDSS power flow analysis, the EVCS are optimally located near the supply bus. The findings show a direct relationship between the EV penetration level and system power loss. Due to the EV technology growth, there is an observed voltage profile degradation of up to 1.7094 p.u. The paper also highlights that although EV bidirectional charging technology (BCT) might reduce the load on the grid in the next few years of low penetration compared to unidirectional charging technology (UCT), it will give no significant difference due to the rapid increase of load connected during its high EV penetration.

Impact of electric vechicles on power transmission grids

This paper presents a methodology for assessing the impact of electric vehicles (EVs) on the power transmission grid of the Costa Rica Power System. The methodology considers penetration scenarios, user preferences, charging habits, and expected fleet growth. Using ETAP software, the study simulates power flow, demand behavior, and voltage levels in the presence of high penetration of electric vehicles. The analysis covers a 15-year horizon and focuses on voltage and demand profiles in 2025, 2030, and 2040. The results indicate a decline in voltage profiles that reaches dangerous levels after 2030, primarily in the distribution grid, and an increase in demand by ▪ for 2040 in the most severe scenario. The analysis also reveals several key findings (a) the identification of problems in the electrical infrastructure starting in 2030 and a major insufficiency in accommodating the increase in EVs by 2040; (b) the need to evaluate stability in transmission grids considering loadability and voltage; (c) the necessity of investing in electrical infrastructure, driven by public policies, to meet future energy requirements and strengthen transmission networks; (d) the significance of accounting for both EV growth and electric infrastructure improvements in system analysis; and (e) the anticipation that the system's performance will fall within the extreme demand values presented in the analysis. The study emphasizes the importance of considering a broader range of scenarios and variability in parameters, especially user charging behaviors, to enable decision-makers to plan for the challenges and opportunities associated with the widespread adoption of EVs in a country's power grid.

Interaction among Multiple Electric Vehicle Chargers: Measurements on Harmonics and Power Quality Issues

The electric vehicle (EV) market is growing rapidly due to the necessity of shifting from fossil fuel-based mobility to a more sustainable one. Smart charging paradigms (such as vehicle-to-grid (V2G), vehicle-to-building (V2B), and vehicle-to-home (V2H)) are currently under development, and the existing implementations already enable a bidirectional energy flow between the vehicles and the other systems (grid, buildings, or home appliances, respectively). With regard to grid connection, the increasingly higher penetration of electric vehicles must be carefully analyzed in terms of negative impacts on the power quality; and hence, the effects of electric vehicle charging stations (EVCSs) must be considered. In this work, the interactions of multiple electric vehicle charging stations have been studied through laboratory experiments. Two identical bidirectional DC chargers, with a rated power of 11 kW each, have been supplied by the same voltage source, and the summation phenomenon of the current harmonics of the two chargers (which leads to an amplification of their values) has been analyzed. The experiment consisted of 100 trials, which considered four different combinations of power set-points in order to identify the distribution of values and to find suitable indicators for understanding the trend of the harmonic interaction. By studying the statistical distribution of the Harmonic Summation Index, defined in the paper, the impact of the harmonic distortion caused by the simultaneous charging of multiple electric vehicles has been explored. Based on this study, it can be concluded that the harmonic contributions of the electric vehicle charging stations tend to add up with increasing degrees of similarity of the power set-points, while they tend to cancel out the more the power set-points differ among the chargers.

A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration

The widespread adoption of electric vehicles (EVs) poses challenges associated with charging infrastructures and their impact on the electrical grid. To address these challenges, smart charging approaches have emerged as a key solution that optimizes charging processes and contributes to a smarter and more efficient grid. This paper presents an innovative multi-objective optimization framework for EV smart charging (EVSC) using the Dynamic Hunting Leadership (DHL) method. The framework aims to improve the voltage profile of the system in addition to eliminating voltage violations and energy not supplied (ENS) to EVs within the network. The proposed approach considers both residential EV chargers and parking stations, incorporating realistic EV charger behaviors based on constant current charging and addressing the problem as a mixed integer non-linear programming (MINLP) problem. The performance of the optimization method is evaluated on a distribution network with varying levels of EV penetration connected to the chargers in the grid. The results demonstrate the effectiveness of the DHL algorithm in minimizing conflicting objectives and improving the grid’s voltage profile while considering operational constraints. This study provides a road map for EV aggregators and EV owners, guiding them on how to charge EVs based on preferences while minimizing adverse technical impacts on the grid.

Clustering-based optimal operation of charging stations under high penetration of electric vehicles

With the rapid growth of technologies related to electric vehicles (EVs), the market penetration of EVs has increased significantly in recent years and has resulted in an increase in the charging load demand for charging stations. In addition, the uncertainty in EVs’ schedules introduces various difficulties for optimal operation of charging stations. Operation of a charging station with consideration of individual EV behaviors may result in computational complexity and lead to infeasibility. Therefore, this study proposes an operational strategy to reduce the computational burden while maintaining EV schedules for charging stations under high penetration of EVs. The proposed method consists of two major stages, i.e., clustering of EVs and optimizing the operation of each cluster. In the first stage, EVs with similar attributes and behaviors are grouped into different clusters, where EVs in the same cluster share similar schedules and operational constraints. This significantly reduces the computational burden as well as the complexity of optimization modeling. In the second stage, instead of considering the behavior of hundreds of EVs, the optimal operation of each EV cluster is considered. In addition, the proposed method allows cooperative operation among EV clusters via vehicle-to-vehicle (V2V) services. The proposed cooperative operation strategy not only reduces the imported power from the utility grid during peak price intervals, but also significantly reduces the amount of energy storage required in the charging stations. This results in the minimization of the investment and operation costs of the entire system. Finally, the effectiveness of the proposed method is verified for an EV charging station with over 180 EVs.

Local demand management of charging stations using vehicle-to-vehicle service: A welfare maximization-based soft actor-critic model

Transportation electrification has the potential to reduce carbon emissions from the transport sector. However, the increased penetration of electric vehicles (EVs) can potentially overload the distribution systems. This becomes prominent in locations with multiple EV chargers and charging stations with many EVs. Therefore, this study proposes a welfare maximization-based soft actor critic (SAC) model to mitigate transformer overload in distribution systems due to the high penetration of EVs. The demand of each charging station is managed locally to avoid network overload during peak load hours in two steps. First, a welfare maximization-based optimization model is developed to maximize the welfare of electric vehicle owners by performing vehicle-to-vehicle(V2V) service. In this step, the sensitivity of EV owners to different parameters (energy level, battery degradation, and incentives provided by fleet operators) is considered. Then, a deep reinforcement learning-based method (soft-actor critic) is trained by incorporating the welfare value (obtained from the welfare maximization model) in the reward function. The total power demand (at the transformer level) and transformer capacity are also included in the reward function. The agent (fleet operator) learns the optimal pricing strategy for local demand management of EVs by interacting with the environment. Each electric vehicle responds to the action (price) by deciding the amount of power they are willing to charge/discharge (V2V) during that interval. Training is performed offline, and the trained model can be used for real-time demand management of different types of charging stations. The simulation results have shown that the proposed method can successfully manage the demand of different charging stations, via V2V, without violating the transformer capacity limits.

Multi-agent based energy community cost optimization considering high electric vehicles penetration

This paper proposes a multi-agent based solution to minimize the energy cost of an energy community, in day-ahead, that includes high penetration of electric vehicles. The proposed approach employs a structure of agents, including a central coordinator and energy management agents. To minimize the energy cost and to optimize the energy balance of the energy community, the proposed approach considers the energy demand and supply (i.e., photovoltaic generation), the battery storage systems’ charge and discharge actions, and the charging and discharging schedules of electric vehicles, including the possibility of vehicles to charge and discharge energy in public charging stations. The optimization problem is formulated as a mixed-integer linear programming problem, which is solved by an open-source solver and compared with the use of a commercial solver. The simulation results show that the proposed day-ahead approach can significantly reduce the cost of the energy community while ensuring a reliable and stable operation. Comparing the proposed solution with a centralized implementation, it is possible to significantly reduce the optimization time from hours to few seconds. Overall, the proposed multi-agent based solution provides a promising solution for the optimization of energy communities with high penetration of electric vehicles.