Electric Vehicles Penetration Levels Research Articles

A hierarchical optimization approach to maximize hosting capacity for electric vehicles and renewable energy sources through demand response and transmission expansion planning

Within the scope of sustainable development, integrating electric vehicles (EVs) and renewable energy sources (RESs) into power grids offers a number of benefits. These include reducing greenhouse gas emissions, diversifying energy sources, and promoting the use of green energy. Although the literature on hosting capacity (HC) models has grown, there is still a noticeable gap in the discussion of models that successfully handle transmission expansion planning (TEP), demand response (DR), and HC objectives simultaneously. Combining TEP, DR, and HC objectives in one model optimizes resource use, enhances grid stability, supports renewable and EV integration, and aligns with regulatory and market demands, resulting in a more efficient, reliable, and sustainable power system. This research presents an innovative two-layer HC model, including considerations for TEP and DR. The model determines the highest degree of load shifting appropriate for incorporation into power networks in the first layer. Meanwhile, the second layer focuses on augmenting the RES and EVs’ hosting capability and modernizing the network infrastructure. System operators can choose the best scenario to increase the penetration level of EVs and RESs with the aid of the proposed model. The proposed model, which is formulated as a multi-objective mixed-integer nonlinear optimization problem, uses a hierarchical optimization technique to identify effective solutions by combining the particle swarm optimization algorithm and the crayfish optimizer. When compared to traditional methods, the results obtained from implementing the proposed hierarchical optimization algorithm on the Garver network and the IEEE 24-bus system indicated how effective it is at solving the presented HC model. The case studies demonstrated that integrating DR into the HC problem reduced peak load by 10.4–23.25%. The findings also highlighted that DR did not impact the total energy consumed by EVs throughout the day, but it did reshape the timing of EV charging, creating more opportunities for integration during periods of high demand. Implementing DR reduced the number of projects needed and, in some cases, led to cost savings of up to 12.3%.

Integration of Electric Vehicles and Renewable Energy in Indonesia’s Electrical Grid

As the global transition toward sustainable energy gains momentum, integrating electric vehicles (EVs), energy storage, and renewable energy sources has become a pivotal strategy. This paper analyses the interplay between EVs, energy storage, and renewable energy integration with Indonesia’s grid as a test case. A comprehensive energy system modeling approach using PLEXOS is presented, using historical data on electricity generation, hourly demand, and renewable energy, and multiple scenarios of charging patterns and EV adoption. Through a series of scenarios, we evaluate the impact of different charging strategies and EV penetration levels on generation capacity, battery storage requirements, total system cost, renewable energy penetration, and emissions reduction. The findings reveal that optimized charging patterns and higher EV adoption rates, compared to no EVs adoption, led to substantial improvements in renewable energy utilization (+4%), emissions reduction (−12.8%), and overall system cost (−9%). While EVs contribute to reduced emissions compared to conventional vehicles, non-optimized charging behavior may lead to higher total emissions when compared to scenarios without EVs. The research also found the potential of vehicle to grid (V2G) to reduce the need for battery storage compared to zero EV (−84%), to reduce emissions significantly (−23.7%), and boost penetration of renewable energy (+10%). This research offers valuable insights for policymakers, energy planners, and stakeholders seeking to leverage the synergies between EVs and renewable energy integration to pursue a sustainable energy future for Indonesia.

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.

ADMM-Based Hierarchical Single-Loop Framework for EV Charging Scheduling Considering Power Flow Constraints

This paper presents a three-layer hierarchical distributed framework for optimal electric vehicle charging scheduling (EVCS). The proposed hierarchical EVCS structure includes a distribution system operator (DSO) at the top layer, electric vehicle aggregators (EVAs) at the middle layer, and electric vehicles (EVs) charging stations at the bottom layer. A single-loop iterative algorithm is developed to solve the EVCS problem by combining the alternating direction method of multipliers (ADMM) and the distribution line power flow model (DistFlow). Using the single-loop structure, the primal variables of all agents are updated simultaneously at every iteration resulting in a reduced number of iterations and faster convergence. The developed framework is employed to provide charging cost minimization at the EV charging stations level, peak load shaving at the EVAs level, and voltage regulation at the DSO level. In order to further improve the performance of the optimization framework, a neural network-based load forecasting model is implemented to include the uncertainties related to non-EV residential load demand. The efficiency and the optimality of the proposed EVCS framework are evaluated through numerical simulations, conducted for a modified IEEE 13 bus test feeder with different EV penetration levels.

Carbon-Aware Operation of Resilient Vertical Farms in Active Distribution Networks

Emerging utilization of Electric Vehicles (EVs) in Distribution Networks (DNs) decreases the power quality in the DN. The main quest of this research is to find out Can EVs and Photovoltaic (PV) systems support the DN and mitigate the voltage magnitude violations due to utilizing high penetration level EV? This article proposes a novel volt/VAR optimization (VVO) model by leveraging the reactive power support of EVs and PVs as Distributed Energy Resources (DERs) for the DN. The proposed VVO model contains the second-order conic relaxed form of the AC power flow equations. Thus, the presented VVO problem is a Mixed-Integer Second-Order Cone Programming (MISOCP) problem. To obtain the ability to support the DN, Smart Inverters (SIs) are utilized to connect EVs and PV systems to the DN. Thus, to add the PV and EV systems to the convexified form of the VVO problem, the P-Q characteristics and the model of SIs working at different operating points are presented based on experimental results. The proposed model of SIs allows the participation of DERs in the reactive power compensation to mitigate voltage issues of DN due to utilizing high penetration level EVs. Besides, the proposed model of SIs enables DERs to increase their dispatchability. The performance of the proposed model is examined in the case studies using the IEEE 33-bus system and the 123-bus system. It is illustrated in the case studies that the solar dispatchability increases by 12% when the proposed volt/VAR model is utilized for IEEE 33-bus system. Besides, it is illustrated in the case studies that the voltage profile of buses remains in the desired operating range when the proposed model is leveraged, even if the penetration level of EVs increased to 40%.

Investigating Investment Plans for Expanding Battery and Electric Vehicle Production in Europe

There has been significant EV sales growth in Europe, benefiting from its policies for promoting electric vehicles (EVs) and investments in manufacturing. This study investigates the investment announcements for EV and battery production announced by manufacturers and compares them to four scenarios with different EV penetration levels in Europe. This study projects the required capacities and estimates the investment needs to meet different EV sale targets in each scenario. The investigations show that, for Europe to achieve 60% new EV sales by 2030 and to be on track for 100% by 2035, its 4.8 million planned production capacity of EVs would fall short of the needed 9.2 million in 2030. The gap could close to 2.0 million when tentative announcements are counted. The results for batteries indicate that tentative plans are adequate and firm plans can satisfy most scenarios by 2030. More investments into EV production, along with policy support and incentives, are needed for more rapid scenarios.

EXPLORING CHARGING-DISPATCH APPROACHES AND VEHICLE-TO-GRID TECHNOLOGIES FOR ELECTRIC VEHICLES IN DISTRIBUTION NETWORKS

Various electric vehicle charging and discharging strategies (EVs) and V2G technologies are discussed in this article as their impacts on energy distribution networks. The V2G application that can be used on vehicles offers many benefits, as demonstrated. Features such as active power regulation, reactive power support, load balancing, and current harmonic filtering are incorporated into this technology.Although V2G technology has many benefits, there are also several challenges. These challenges include reduced battery life, communication overhead between EVs and grids, and changes in distribution network infrastructure. The article briefly discusses the effects of electric vehicle penetration levels,charging profiles, and various other aspects of controlled charging and discharging from a performance perspective. This includes over loading, deteriorating power quality, and power loss. A comprehensive analysis of controlled and uncontrolled charging–discharging methods, delayed charging discharging methods, indirect controlled discharging methods, bidirectional charging–discharging methods, and intelligent scheduling is presented in this study. Several challenges and issues regarding electric vehicle applications are discussed from an aggregator’s perspective. Analysis shows that Li-ion batteries can be recharged 2000–4000 times, and a mass-produced Li-ion battery costs $200–$500 per kWh.Degradation costs of batteries at 80% discharge depth are estimated to be $130 per MWh at 300 kWh investment cost. 10% of peak capacity could come from PEVs in the 20% range. Around 87.5% of PEVs are properly charged. Key Words: Electric vehicles(EV),penetration,overloading, Communication,Deteriorating,Degradation.

Driving change: Electric vehicle charging behavior and peak loading

Electric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.

Evaluation of hosting capacity of the power grid for electric vehicles – A case study in a Swedish residential area

The deployment of electric vehicles (EVs) is growing significantly in recent years. The increasing EV charging loads pose great stress on power grids in Sweden, as many existing power grids are not designed to host such large shares of new electric loads. Hence, studies investigating the impact of EV charging are needed. This study conducts a case study based on an existing Swedish residential power grid using real-life EV charging data to estimate the local grid hosting capacity (HC) for EVs. A combined time-series and stochastic HC assessment method is used with voltage deviation, cable loading and transformer loading as the performance indices. Uncertainty in EV charging locations and individual charging behaviour have been considered via Monte Carlo simulations. The power grid HC is analysed and compared under three charging strategies and four EV penetration levels. Study results show that a charging strategy based on low electricity prices gave lower HC due to simultaneous EV loads compared to the other two strategies: charging directly after plugging in the EV and an even charging load through the plug-in session. This implies the need for coordinated charging controls of EV fleets or diversified power tariffs to balance power on a large scale.

Probabilistic scenario analysis of integrated road-power infrastructures with hybrid fleets of EVs and ICVs

Electric Vehicles (EVs) are key contributors to the reduction of CO2 emissions. However, reliance on EVs must come with the guarantee that the integrated road-power infrastructure is capable of providing adequate mobility serviceability, even in case of disruption due to accidents or disturbances due to traffic jams. In this paper, we propose a probabilistic scenario analysis framework to quantify service losses in terms of delays that vehicles (both EVs and Internal Combustion Vehicles (ICVs)) may incur due to different car accident scenarios. The framework is based on modelling the System of Systems (SoS) comprised by road network, electric power system and vehicles, with graph theory and Finite State Machines (FSMs), respectively, and then embedding the model within a probabilistic scenario analysis, wherein meaningful disruption scenarios are sampled, service losses are measured (specifically as the ratio between the increase in travel time spent along the origin-destination routes on the road network following a disruption, and the corresponding travel time in nominal traffic conditions), and the economic losses and transport reliability of the infrastructure are assessed. To exemplify the application of the framework, we consider a benchmark road-power infrastructure in New York state travelled by a mixed fleet of EVs and ICVs, with different EVs penetration levels and under car accidental scenarios of different magnitudes. By using the insightful graphical representation of the results in terms of traffic volume across different road sections, the framework allows comparing alternative road-power infrastructure designs (e.g., critical roads, optimal gas and charging station locations, power network structure and topology, …) with respect to travel times, economic service losses and transport reliability considering different nominal and disruption scenarios under different EVs penetration levels service.

Impact of electric vehicles on low‐voltage residential distribution networks: A probabilistic analysis

AbstractThe past two decades have seen a rapid increase in electric vehicles (EVs) for several reasons, such as policy directives to reduce carbon emissions in the transport sector and technology advancements in the EV industry. However, this has increased the load demand on the power grid, especially in the low‐voltage (LV) network, as most EVs are charged at EV owner premises. This paper investigates the impact of EVs on the LV residential distribution network using a probabilistic modelling framework. Probability distribution functions for EV charging power are derived using the United Kingdom (UK) EV dataset. The study has investigated multiple EV penetration levels, different probability distribution functions for EV charging representation, vehicle‐to‐grid (V2G), solar photovoltaic (PV) generation, and the volt‐var capability of the solar‐PV inverter. The results have shown that as EV penetration increases in the distribution network, there is a significant increase in transformer loading and a decrease in the steady‐state voltage levels. V2G has positively impacted the distribution network. A case study carried out on a real LV feeder with solar‐PV generation has shown how PV generation and volt‐var functionality of the PV inverter help reduce the impact of EV charging and V2G.

Assessment and mitigation of the influence of rising charging demand of electric vehicles on the aging of distribution transformers

The world is currently moving towards more environmentally friendly modes of transport. As a result, the sales of electric vehicles (EVs) are growing exponentially, and the new charging-associated loads are influencing the operation of the power distribution networks and their elements. Given the stochastic nature of the additional load from EVs, it is difficult to predict such a load with analytical methods. In this regard, a fuzzy-logic-based tool to assess the influence of the charging load of EVs on the transformers’ aging was developed in the MATLAB-Simulink environment, and the impact of different EV penetrations was simulated and assessed. The fuzzy-logic-based model incorporates the influence of ambient temperature, power quality, and overloads. It comprises a diagnostic part, which warns a user about possible issues by providing actual information about the transformer's state, and a tuning part, which is aimed at optimizing the transformer's load level and the power factor at the secondary voltage side. Moreover, the effectiveness of photovoltaic generation units, shunt capacitor banks and battery energy storage systems, installed at the secondary voltage side, for distribution transformers’ service life extension were analyzed. The results show that, for high EV penetration levels, a combined grid reinforcement strategy allows to reduce the loss of life of the transformer by around 3–5 times.

Adaptive thermal model for loading of transformers in low carbon electricity distribution networks

The uptake of low carbon technologies, particularly Electric Vehicles (EVs) and Heat-Pumps (HPs), at the low voltage (LV) distribution network, in the quest of cutting down on greenhouse gas (GHG) emissions in the transportation and residential sectors, has the potential to cause general load increase and may lead to higher and longer peak load demand. This development can, as hinted in previous studies, pose a real challenge of capacity overloading to transformers at the LV distribution network of electricity system. Prolonged periods of transformer overloading could lead to premature transformer failure and shortens transformer's life expectancy. A direct solution to addressing transformer overloading is the upgrading of the transformer capacity. However, the number of LV distribution transformers in electricity system to be upgraded and the resources needed for such operation make the solution less desirable to the Distribution Network Operators (DNOs). Therefore, it is important to develop cost-effective solutions for the optimal utilization of the existing transformer capacity. Adaptive thermal loading of transformers is one of such solutions. This paper focusses on the Adaptive Thermal Loading (ATL) of transformers in LV distribution networks with considerable penetration level of EVs and HPs. The thermal model of a 500-kVA, 11/0.415-kV (no load), 50-Hz, Dyn11, ONAN mineral oil filled, free breathing, ground mounted transformer serving a real and typical urban LV network in the United Kingdom (UK) is developed based on IEC 60,076–7:2005 standard and used as the case study. A method of adaptive thermal loading of the transformer is presented to examine its capacity performance when serving the future load of the LV network following the integration of projected uptake figures of EVs and HPs for the years 2020, 2030, 2040 and 2050 into the network. Given the load and temperature forecasts of a day, the method aims at optimizing, considering the real and present conditions of the operating environment, the overall daily transformer capacity utilization that gives maximum daily return on investment without undermining reliability of supply and normal life expectancy of the transformer. Results show improved performances of the transformer when the adaptive thermal loading method is used.

Optimal routing and power management of electric vehicles in coupled power distribution and transportation systems

With the increasing penetration level of electric vehicles (EVs) in the transportation network, it is necessary to control the charging and discharging process of EVs to avoid operational issues in the power grid. Therefore, in this paper, a two-stage framework is presented for EVs optimal routing and their active and reactive power control in the distribution grid. In the first stage, the EVs mobility in the transportation system is taken into consideration. On this basis, EV daily trips in the transportation network are modelled using the trip chain method. Also, the Dijkstra algorithm is utilized for optimal routing with the shortest travel time between the origin and the destination node. Furthermore, the effects of ambient temperature, traffic congestion and road type on the EVs energy consumption are assessed. In the second stage, optimal active and reactive power exchange of EVs with the distribution grid is carried out. Thus, a mixed-integer linear programming (MILP) model is proposed that simultaneously considers EV owners’ and the distribution network operator's benefits as the optimization goals. The EV battery degradation cost due to the discharging of active power is also integrated into the objective function of the optimization problem. The proposed framework is implemented on a standard IEEE 33-bus system coupled with a 30-node transportation network. The simulation results show that the presented method can reduce the loss cost of the distribution grid and benefit EV owners compared to different charging strategies. In addition, with the battery technology development, the proposed framework can significantly improve the distribution system operation, decrease environmental issues, and reduce EV owner’s costs.

Impact Assessment of Electric Vehicle Charging in an AC and DC Microgrid: A Comparative Study

This paper presents an in-depth comparison of the benefits and limitations of using a low-voltage DC (LVDC) microgrid versus an AC microgrid with regard to the integration of low-carbon technologies. To this end, a novel approach for charging electric vehicles (EVs) on low-voltage distribution networks by utilizing an LVDC backbone is discussed. The global aim of the conducted study is to investigate the overall energy losses as well as voltage stability problems on DC and AC microgrids. Both architectures are assessed and compared to each other by performing a power flow analysis. Along this line, an actual low-voltage distribution network with various penetration levels of EVs, combined with photovoltaic (PV) systems and battery energy storage systems is considered. Obtained results indicate significant power quality improvements in voltage imbalances and conversion losses thanks to the proposed backbone. Moreover, the study concludes with a discussion of the impact level of EVs and PVs penetration degrees on energy efficiency, besides charging power levels’ impact on local self-consumption reduction of the studied system. The outcomes of the study can provide extensive insights for hybrid microgrid and EV charging infrastructure designers in a holistic manner in all aspects.

Distribution Network Expansion Planning Approach for Large Scale Electric Vehicles Accommodation

The increased power demand incurred by electric vehicles (EVs) has imposed new challenges on the distribution network. One significant responsibility of the distribution network operator (DNO) is to design and expand the distribution network periodically to accommodate the ever-increasing demand. The previous researches mainly use relaxation techniques to enhance the problem tractability, which would involve additional binary variables and incur computation burden to the optimization problem, or even worse, may lead to an intractable problem. This paper develops a mixed-integer non-linear programming (MINLP) model considering various network constraints for the distribution network expansion planning under a high penetration level of EVs. The stochastic characteristics of demands are addressed through set of scenarios. A distributed biased min-consensus algorithm based approach is proposed to solve the MINLP model. Finally, comparison tests are conducted on three different scales of distribution networks to validate the effectiveness of the proposed approach. Simulation results demonstrate that the computation time of the proposed approach is reduced by 42.46% on average for different systems compared with that of the traditional shortest path algorithm based approach.

Primary frequency control by fuzzy-based participation controller for plug-in electric vehicles

ABSTRACT The increasing penetration level of electric vehicles (EVs) shows that they will enter into the category of distributed energy sources in the near future. Major upgradation is required in the conventional power system to handle upcoming challenges due to electrification in the transportation sector and generate opportunities for the power system. The energy storage capability of batteries and advancement in fast switching converters enable EVs to support the grid with different ancillary services, e.g. primary frequency control (PFC). However, an effective charging strategy/controller is required to support the grid with PFC without violating constraints from grid and EVs. This paper proposes a novel fuzzy-based controller to decide EV’s availability for PFC with an optimized participation level considering different operational modes and EV charging demand. The controller computes the participation level of EVs based on real-time inputs from EVs, magnitude of frequency deviation and availability of primary reserve. The Spanish power system model has been employed to show the proposed controller’s efficacy and compare it with other recently reported controllers. The MATLAB/Simulink platform has been used to perform frequency and power response analysis for four different defined cases. Results show that EVs with the proposed fuzzy-based controller can effectively support the grid with PFC service without violating the grid’s and EV’s limits.

Data-Driven Matching Protocol for Vehicle-to-Vehicle Energy Management Considering Privacy Preservation

To address the increasing concern on environmental issues, electric vehicles (EVs) have been attracting more attention. On the one hand, the high penetration level of EVs will bring potential risks to the electricity grids. On the other hand, EVs can enhance the flexibility of the grids via vehicle-to-grid (V2G) technology. With the advancement of digitalization and communication technologies, the concept of vehicle-to-vehicle (V2V) has emerged, which can provide an alternative charging way for EVs to relieve the charging overload problem in the power systems. In this article, we present a data-driven matching protocol for V2V energy management. First, deep reinforcement learning (DRL) is utilized to learn the long-term reward of the matching action based on the formulated Markov decision process (MDP) at an offline stage. To protect the private information of the EV owners, a federated learning framework is proposed where the cooperation between different EV aggregators can be realized without sharing sensitive information. At the online matching stage, a matching optimization model is established and is converted into a bipartite graph problem to enhance the computation efficiency. The proposed methodology is verified in case studies. Simulation results show that the proposed methodology can help EV owners save costs and increase revenues while hedging the trading risks. Besides, the local energy self-sufficiency is increased, indicating that less burden is brought to the electricity network.

Adaptive Integrated Planning of Electricity Networks and Fast Charging Stations Under Electric Vehicle Diffusion

The proliferation of electric vehicles (EVs) is an inevitable trend to realize transportation electrification. However, the increasing charging demand of EVs also brings challenges to future smart grids. To cope with the EV diffusion, this paper presents an adaptive fast charging station (FCS) strategy that considers the integrated network planning and quality of service (QoS) guarantee. First, based on the EV diffusion model and the proposed EV-integrated traffic assignment model, the charging demands at different regions and stages are forecasted. Based on the predicted spatial and temporal EV charging demand, a multistage stochastic distribution expansion planning model is presented, where the distribution network assets and FCSs are jointly considered. To guarantee the quality of the fast-charging services under EV diffusion, a QoS assessment is conducted based on the average waiting time and the average number of dissatisfied leaves. Finally, the proposed planning model allows several alternatives at both the distribution and transmission levels. The cost of electricity network expansion planning is considered as the adaptive cost arisen from the EV diffusion and the increase of the other electrical loads. We aim to find a planning strategy that can adapt to different penetration levels of EVs. The proposed framework and methodology are verified on the IEEE 24-bus power system encompassing 6 IEEE 33-bus distribution networks. It is found out that the proposed strategy shows superiority in both QoS and total costs.

Energy storage for mitigating grid congestion caused by electric vehicles: A techno-economic analysis using a computationally efficient graph-based methodology

This paper studies the impact of electric vehicle charging on congestion in low-voltage networks and the economic feasibility of energy storage as an alternative to conventional network upgrades. To this end, we propose a computationally efficient graph-based methodology and employ it on a GIS data set of over 6,500 low-voltage networks. We provide insights regarding the types of networks that are most congested due to electric vehicle charging, the typical location of overloaded network components, and the types of networks in which a storage-based upgrade is profitable. A main result is that the number of networks in which it is profitable to use energy storage grows exponentially as the cost of energy storage decreases, regardless of the electric vehicle penetration level and charging power. Our findings also show that using energy storage to alleviate congestion can incur substantial savings, conditioned with a cost reduction of stationary energy storage to 200 US$/kWh and below.