Uncontrolled Charging Of Electric Vehicles Research Articles

Fuzzy logic-based estimation model of distribution transformers aging

Currently, we are moving towards more ecological modes of transport. As a result, the demand for electric vehicles is growing exponentially, and new loads, related to charging, can negatively affect the operation of electrical grids and their elements. Concurrent and uncontrolled charging of electric vehicles can cause a blackout. Considering the stochastic nature of the additional load from electric cars, it is difficult to predict such a load by analytical methods. The fuzzy logic approach can be used to formalize the tasks with ambiguity. In this paper, a fuzzy-based block diagram of the algorithm was developed in the MATLAB-Simulink environment for evaluating the impact of the charging load of electric vehicles on the aging of transformers, as well as a model was built and the effect of various penetrations of electric cars was evaluated. The fuzzy logic-based model includes the effects of ambient temperature, power quality, and overloads. It contains a diagnostic part that warns the dispatcher of the power distribution network about possible problems, providing up-to-date information about the state of the transformer. In addition, the efficiency of photovoltaic generators to increase the service life of distribution transformers was analyzed. The results show that photovoltaic power plants are not effective enough for high levels of electric vehicle penetration, and more advanced strategies should be used.

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.

Hosting capacity of distribution networks for controlled and uncontrolled residential EV charging with static and dynamic thermal ratings of network components

AbstractThe ongoing electrification of road transportation sector, which is expected to continue to strongly increase over the next years, will result in the connection of a significant number of electric vehicle (EV) chargers in LV and MV distribution networks, particularly in residential applications with on‐board (“slow”) EV chargers. In order to evaluate loading limits of existing distribution networks for the maximum number of EV chargers that can be safely connected (commonly denoted as a network EV “hosting capacity”, HC), this paper introduces a general approach to determine one commonly used network design parameter (after‐diversity maximum demand, ADMD) and one new parameter (maximum daily energy demand, MDED), which are both obtained from the load profiles of maximum per‐hour demands for uncontrolled residential EV charging. The presented approach uses actual EV charging data from the UK as the inputs in Monte Carlo simulations to generate daily EV charging profiles for arbitrary numbers of EVs, enabling to identify related ADMD, MDED and per‐hour maximum demand values, as well as their seasonal variations. The assessed ADMD, MDED and hourly maximum EV charging demands for uncontrolled EV charging are then combined with available UK residential daily load profiles before the EVs are connected (“pre‐EV demands”), where their combined coincidental and noncoincidental maximum demands are evaluated against the static thermal rating (STR) and dynamic thermal rating (DTR) loading limits of network components (transformers and overhead lines), taking into account relevant weather/ambient conditions. This is denoted as a network HC for uncontrolled EV charging. Finally, evaluating the resulting per‐hour maximum demand values against the STR and DTR loading limits and MDED values allows to select one particular scheduling method for controlled EV charging, which gives the absolute maximum number of EVs that can be safely connected in the considered network, that is, maximum network HC for fully controlled EV charging. The presented approach is illustrated on the example of the IEEE 33‐bus test network (modelled using typical UK network components), for the pre‐EV residential demands taken from the recordings at a UK MV substation, and for ambient data taken from a UK Met Office weather station. Obtained results allow to evaluate the range of network EV HC values for uncontrolled and controlled EV charging, that is, lower and upper HC limits, which can be correlated with the commonly used allocations of the firm and non‐firm network HC, respectively.

Smart charging strategy for electric vehicles based on marginal carbon emission factors and time-of-use price

As the penetration of electric vehicles (EVs) has grown rapidly in recent years, the stability and safety of electric grids are inevitably affected. Uncontrolled EV charging can reduce electric grid efficiency and increase greenhouse gas emissions (GHG). This occurs when EV charging aligns with peak demand, requiring the activation of fossil fuel-powered plants to meet the electricity demand. The time-of-use (TOU) price is a traditional management strategy for smoothing the demand power curve, but it does not consider carbon emissions. This study proposes a smart charging strategy based on an improved local search genetic algorithm (LS-GA) that considers both the TOU price and marginal emission factors (MEF). Over 47,000 charging events from 45 charging stations in Tianjin, a major city in North China, were used to simulate the charging pattern and demand. Four different charging strategies are considered in the study. The results indicate that the proposed smart charging strategy can reduce the cost and carbon emissions by up to 27% and 16% compared with uncontrolled charging, respectively. This study demonstrates the significant potential of using optimization methods in the EV smart charging problem.

Development of improved reinforcement learning smart charging strategy for electric vehicle fleet

Due to its environmental and energy sustainability, electric vehicles (EV) have emerged as the preferred option in the current transportation system. Uncontrolled EV charging, however, can raise consumers; charging costs and overwhelm the grid. Smart charging coordination systems are required to prevent the grid overload caused by charging too many electric vehicles at once. In light of the baseload that is present in the power grid, this research suggests an improved reinforcement learning charging management system. An optimization method, however, requires some knowledge in advance, such as the time the vehicle departs and how much energy it will need when it arrives at the charging station. Therefore, under realistic operating conditions, our improved Reinforcement Learning method with Double Deep Q-learning approach provides an adjustable, scalable, and flexible strategy for an electric car fleet. Our proposed approach provides fair value which solves the overestimation action value problem in deep Q-learning. Then, a number of different charging strategies are compared to the Reinforcement Learning algorithm. The proposed Reinforcement Learning technique minimizes the variance of the overall load by 68 % when compared to an uncontrolled charging strategy.

Multi-Objective Allocation of EV Charging Stations and RESs in Distribution Systems Considering Advanced Control Schemes

Renewable energy sources (RESs) and electric vehicle charging stations (EVCSs) have been extensively incorporated into distribution systems. Due to the stochastic nature of RES generation and electric vehicles (EVs), risky operational challenges might impact the grid. Therefore, this paper proposes a multi-objective planning framework to optimally allocate EVCSs in conjunction with RESs. Specifically, the proposed RESs and EVCSs planning framework considers three sub-objectives to be minimized, i.e., voltage deviations, energy losses, and EVs owners' dissatisfaction. Further, active power curtailment of RESs is precluded while considering the diverse operational constraints of the grid, RESs, and EVCSs. To provide further benefits, advanced control schemes of the interconnecting RES inverters, grid-to-vehicle (G2V), and vehicle-to-grid (V2G) schemes are considered in the proposed framework. A two-level approach is developed to solve this holistic framework with competing sub-functions to obtain the Pareto-optimal solutions. The outer optimization level precisely optimizes the RES locations and sizes, along with the optimal places of the EVCS. On the other hand, the inner level determines the optimal EVCS charging considering stochastic EV power, RES inverter reactive power, and the time-of-use energy tariff. The proposed framework has been tested on the 69-bus distribution system. The simulation results reveal the efficacy of the proposed RES and EVCS planning framework. The total voltage deviation and energy losses achieved by the proposed framework are reduced by 96% and 74%, respectively, with respect to the uncontrolled charging of EVs, while the owners' satisfactions are met.

The Impact of V2G Charging/Discharging Strategy on the Microgrid Environment Considering Stochastic Methods

Although electric vehicles (EVs) play a vital role in realizing remarkable features, however, the integration of a huge number of EVs leads to grid congestion as well. As a result, uncontrolled charging might give rise to undervoltage and complex congestion in the electric grid. The reasons for the uncontrolled charging of EVs have been investigated in the recent past to mitigate the effects thereof. It is very challenging to achieve controlled charging due to different constraints at the customer end; therefore, it is better to take the benefits of power prediction schemes for the charging and discharging of EVs. The power prediction scheme is based on a practical power forecast system that exploits the needs of various patterns, and the current research focuses on considering users’ demands. The primary objective of this study is to develop an effective and efficient coordination system for the charging and discharging of EVs by exploiting a smart algorithm that intelligently tackles the possible difficulties to attain optimum power requirements. In this context, a model is proposed based on stochastic methods for analyzing the impact of vehicle-to-grid (V2G) charging and discharging in the microgrid environment. A Markov model is used to simulate the use of EVs. This method works well with the Markov model because of its ability to adjust to random changes. When considering an EV, its erratic travel patterns suggest a string of events that resemble a stochastic process. The proposed model ensures that high power requirements are met during peak hours in a cost-effective manner. In simpler words, the promising features of the proposed scheme are to meet electricity/power demands, monitoring and the efficient forecasting of power. The outcomes revealed an effective power system, EV scheduling, and power supply without compromising the electric vehicle’s presentation of the EV owner’s tour schedule. In terms of comprehensiveness, the developed algorithm exhibits a significant improvement.

Coordinated integration of distributed energy resources in unit commitment

Distributed energy resources, such as electric vehicles and roof-top solar panels have become increasingly popular and essential components of power distribution systems. However, their growth poses significant challenges to system operation, such as bidirectional power flow, intermittent power generation, increased peak demand, and unexpected frequency/voltage fluctuation. To tackle these challenges, we developed a vehicle-to-grid model, which incorporated dynamic electric vehicles usages, such as driving times, driving distance, and charging/discharging locations. The machine learning method of principal component analysis and XGBoost were used to develop the solar energy prediction model. We also developed a novel unit commitment model to coordinate and aggregate the distributed energy resources in power generation economic dispatch. Electric vehicle charging was used as elastic demand and electric vehicle discharging was used as power generation sources. The solar energy was considered as a negative load. Simulation results showed that uncontrolled electric vehicle charging and solar energy could negatively impact the power systems. For example, the load ramping rate was increased by 384%. Simulation results also showed that the proposed models could mitigate the negative impact and improve energy efficiency. For example, the peak demand, load ramping rate and average generation cost were reduced by 21%, 79%, and 27%.

Data-Driven Study of Low Voltage Distribution Grid Behaviour With Increasing Electric Vehicle Penetration

In this paper, the impact of Electric Vehicle (EV) uncontrolled charging with four levels of EV penetration in overall 21 real low voltage distribution grids in two seasons are analysed. The employed real grid data is provided by distribution system operators from three European countries: Austria, Germany and the Netherlands. At least six grids in each country were considered and they are categorised into three types, namely rural grids, suburban grids and urban grids. The EV charging data used in this study is based on real measurements or surveys. The seasonal and the weekday-weekend factors are also considered in the EV charging impact research. Three key congestion indicators, the transformer loading, line loading and node voltage as well as several other evaluation indexes are studied. The results reveal that the majority of the simulated grids had no or minor moments of mild overloading while the rest grids had critical issues. Among all the grids, suburban grids are most vulnerable to massive EV integration. Out of the evaluated grids, those who are located in Germany have the highest redundancy for high EV penetration accommodation. Overall, the impact of uncontrolled EV charging depends on the combination of EV charging demand as well as the grid inherent features.

Stochastic analysis of future scenarios for battery electric vehicle deployment and the upgrade of the electricity generation system in Spain

The feasibility of electrifying transport while reducing energy coming from non-renewable sources remains unknown. This study analyzes the current and future impacts caused by uncontrolled electric vehicle charging activities on the electrical grid, in terms of the possibility of having an undetermined number of electric vehicles plugged in at the same time. The results obtained show that charging battery electric vehicles could heavily impact the daily request of electric power, and the peak time for electricity consumption would become unmanageable. In particular, the Spanish grid is vulnerable due to the government's intention to achieve a gradually greater proportion of renewable energy through the closure of fossil fuel and nuclear power stations. This study proposes and evaluates a set of scenarios by developing a simplified stochastic model that considers the uncertainty involved in the problem. Electromobility can only be successful if the power system is considered. The evolution from the current situation to a series of future electrified mobility scenarios will heavily impact not only the annual energy consumption but also the daily electric power supply. The results show that the development of a charging network that allows vehicle batteries to be recharged during working hours seems to be the most suitable solution, while the uncontrolled development and management of a fast-recharging network would be the worst strategy.

Simulating the Impacts of Uncontrolled Electric Vehicle Charging in Low Voltage Grids

Across the world, the impact of increasing electric vehicle (EV) adoption requires a better understanding. The authors hypothesize that the introduction of EV’s will cause significant overloading within low voltage distribution grids. To study this, several low voltage networks were reconstructed based on the literature and modelled using DigSilent Powerfactory, taking into account the stochastic variability of household electricity consumption, EV usage, and solar irradiance. The study incorporates two distinct usage scenarios—residential loads with varying EV penetrations without and with distributed grid tied generation of electricity. The Monte-Carlo simulation took into account population demographics and showed that in urban networks, EV introduction could lead to higher cable loading percentages than allowed, and in rural networks, this could lead to voltage drops beyond the allowed limits. Distributed generation (DG) in the form of solar power could significantly offset both these overloading characteristics, as well as the active and reactive power demands of the network, by between 10–50%, depending on the topology of the network.

A novel communication‐free charge controller for electric vehicles using machine learning

Despite many advantages, wide-spread integration of electric vehicles (EVs) in the power systems is challenging. Large-scale uncontrolled EV charging load may lead to under-voltage violations, higher power losses, overloading of transformers and transmission lines. In this research work, a machine learning-based communication-free EV charge control strategy is developed to mitigate the issues caused by the uncontrolled EV charging. Another notable feature of the proposed controller is to ensure the fairness among the EVs available at different locations in the distribution system. Moreover, two indices are proposed, that is system utilisation index (SUI) and system average voltage violation duration index (SAVVDI), to assess the performance of any EV charge controller. In the proposed control structure, a nodal voltage and the voltage-to-load sensitivity are measured at each load node which are fed to the EV charge controller. In fact, an upstream node is generally less sensitive to the changes in load, since it is closer to the feeding point, as compared to a downstream node. The output of the charge controller is the charging rate of an EV. In order to validate the robustness of the proposed controller, light and heavy loading conditions are considered which mimic the daily, monthly, yearly and seasonal load variations. Simulation results prove that the proposed controller effectively improves the voltage profiles and ensures fairness among the EVs connected at various charging points in the distribution system. Moreover, it effectively utilises the power system resources.

Electric Vehicle Charging with Battery Scheduling and Multicriteria Optimization using Genetic Algorithm

The existing charging infrastructure needs expansion and upgrade with the growing fleet of electric vehicles (EV). The electric grids are largely affected by the uncontrolled charging cycles. To overcome this drawback, the hybrid charging stations are incorporated with battery storage and renewable energy sources. The power necessary from the grid can be buffered using a battery and renewable source attached to the charging station thereby avoiding the grid constraints and peaks. It has been a challenge to trace the origin of the battery’s energy till date. The battery energy storage and a simple photovoltaic system is incorporated in a hybrid EV charging station. Uncontrolled EV charging and its adverse effects can be overcome by this technology by accurately calculating the share of renewable energy derived from the battery. Multi-attribute utility theory is used for optimizing the EV charging level and scheduling the battery charging and discharging. Minimizing battery degradation and charging cost while maximizing the renewable energy from the battery and PV sources are the major criteria of optimization. Multicriteria optimization function is used along with the genetic algorithm optimization scheme to address the optimization issues. Optimal capacity of the battery and optimization strategy is affected by the preferences in decision making.

Simultaneous Distributed Generation and Electric Vehicles Hosting Capacity Assessment in Electric Distribution Systems

This work presents a strategy, from the perspective of the distribution system operator (DSO), that aims at simultaneously estimating the maximum penetration levels of renewable-based distributed generation (DG) and electric vehicles (EVs) that can be accommodated into an electric distribution system (EDS). To estimate such capacity, operational resources such as generation curtailment and controllable features of EVs can be managed to ensure the safe operation of the EDS and avoid infeasible operational conditions. Through a multi-period representation, the proposed strategy models the variability in demand consumption and DG power production. In addition, driving patterns of EV owners and energy requirements of EVs, obtained through probability density functions, are incorporated in this representation. Inherently, the problem is represented as an optimization model, and to determine its solution, an algorithm based on the metaheuristics greedy randomized adaptive search and tabu search (GRASP-TS) is developed. The applicability of the planning strategy is assessed on a 33-bus EDS under different test conditions and the numerical results show that higher penetrations of EVs and renewable-based DG can be accommodated without impacting the safe operation of the EDS. The results also demonstrate that by controlling the power draw by EV aggregators, an increase of 9% can be obtained in the DG installed capacity compared to the case of uncontrolled charging of EVs. In addition, the scalability of the proposed approach is studied using two distribution systems, the 83-bus system and the 135-bus system, where the results show that the convergence of the algorithm is achieved in a few iterations.

Renewable Energy Tracking and Optimization in a Hybrid Electric Vehicle Charging Station

The increasing electric vehicle fleet requires an upgrade and expansion of the available charging infrastructure. The uncontrolled charging cycles greatly affect the electric grid, and for this reason, renewable energy sources and battery storage are getting incorporated into a hybrid charging station solution. Adding a renewable source and a battery to the charging station can help to “buffer” the power required from the grid, thus avoiding peaks and related grid constraints. To date, the origin of the energy coming from the battery has not been traced. In this paper, a solution of the hybrid electric vehicle charging station coupled with the small-scale photovoltaic system and battery energy storage is proposed to eliminate the adverse effects of uncontrolled electric vehicle charging, with the exact calculation of renewable energy share coming from energy stored in the battery. The methodology for the multicriteria optimization of the charging/discharging schedule of a battery and electric vehicle charging level is based on multi-attribute utility theory. The optimization criteria include the minimization of charging costs, maximization of renewable energy (from both the solar plant and the battery), and the minimization of battery degradation. The problem is solved using a genetic algorithm optimization procedure adapted to the multicriteria optimization function. The methodology is tested on an illustrative example, and it is proven that the decision-maker’s preferences greatly affects the choice of the optimal strategy and the optimal battery capacity.

Optimised controlled charging of electric vehicles under peak power‐based electricity pricing

This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real-time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power-based distribution tariffs for small-scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power-based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware-in-the-loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

Should we reinforce the grid? Cost and emission optimization of electric vehicle charging under different transformer limits

With high electric vehicle (EV) adoption, optimization of the charging process of EVs is becoming increasingly important. Although the CO2 emission impact of EVs is heavily dependent on the generation mix at the moment of charging, emission minimization of EV charging receives limited attention. Generally, studies neglect the fact that cost and emission savings potential for EV charging can be constrained by the capacity limits of the low-voltage (LV) grid. Grid reinforcements provide EVs more freedom in minimizing charging costs and/or emissions, but also result in additional costs and emissions due to reinforcement of the grid. The first aim of this study is to present the trade-off between cost and emission minimization of EV charging. Second, to compare the costs and emissions of grid reinforcements with the potential cost and emission benefits of EV charging with grid reinforcements. This study proposes a method for multi-objective optimization of EV charging costs and/or emissions at low computational costs by aggregating individual EV batteries characteristics in a single EV charging model, considering vehicle-to-grid (V2G), EV battery degradation and the transformer capacity. The proposed method is applied to a case study grid in Utrecht, the Netherlands, using highly-detailed EV charging transaction data as input. The results of the analysis indicate that even when considering the current transformer capacity, cost savings up to 32.4% compared to uncontrolled EV charging are possible when using V2G. Emission minimization can reduce emissions by 23.6% while simultaneously reducing EV charging costs by 13.2%. This study also shows that in most cases, the extra cost or emission benefits of EV charging under a higher transformer capacity limit do not outweigh the cost and emissions for upgrading that transformer.

Optimization of Electric Vehicle Charging Scheduling in Urban Village Networks Considering Energy Arbitrage and Distribution Cost

Electric vehicles (EV) replacing the internal combustion engine vehicle may be the solution for the particulate matter (PM) 2.5 pollution issue. However, the uncontrolled charging of EVs would challenge the power system operation. Therefore, it is necessary to implement some level of control over the EV charging procedure, especially in the residential network. In this paper, an optimization of EVs charging scheduling considering energy arbitrage and the distribution network cost of an urban village environment is presented. The optimized strategy focuses on decreasing the loss of EV owners’ energy arbitrage benefit, introduced as the penalty cost. Also, peak demand, power loss, and transformer aging are included in the estimation of the cost function for the distribution network. The optimization problem is solved using the genetic algorithm. As a case study, data from the urban village in Udon Thani, Thailand, are utilized to demonstrate the applicability of the proposed method. Simulation results show a reduction in the loss of energy arbitrage benefit, transformer peak load, power loss and the transformer loss of life. Therefore, the application of the optimized EV charging can prolong transformer lifetime benefiting both the EV owner and the distribution system operator.

An Autonomous Charge Controller for Electric Vehicles Using Online Sensitivity Estimation

Despite their sustainable benefits, large-scale adoption of electric vehicles (EVs) into the distribution system is challenging. Uncontrolled charging of EVs could increase voltage violations, system power losses, and feeder overloads. In this article, an autonomous EV charge control strategy is proposed to control EV charging in a way that mitigates their negative impacts. To ensure fair contribution from EVs connected to different nodes, both the local nodal voltage and sensitivity of voltage to load changes are used as inputs to the proposed controller. This is so because these two inputs have a complementary relationship. That is, nodes with lower voltages are generally more sensitive to load changes than those with higher voltages. In addition, a novel approach for locally estimating the voltage sensitivities online is proposed. This ensures the robustness of the proposed control strategy to changes in loading conditions and system configurations without the need for communication. An EV-rich test distribution system is modeled in the DIgSILENT PowerFactory environment and used to validate the proposed control strategy. The test results demonstrate the effectiveness and robustness of the proposed strategy considering different loading conditions, system reconfiguration, distributed generators, and voltage control devices.

Potential Impacts of Transportation and Building Electrification on the Grid: A Review of Electrification Projections and Their Effects on Grid Infrastructure, Operation, and Planning

This paper highlights the main impacts of transportation and building electrification on electricity grid infrastructure, operations, and planning. We focus on which electric technologies are expected to grow, how electrification will impact electricity demand and grid activities, and which solutions might improve the integration of these technologies. Recent industry reports show increasing projections of electrification technologies in the coming decades. Studies show that increased demand from electrification will likely require grid capacity expansion and make grid operations and planning more challenging; however, multiple non-wires solutions exist to ensure integration is efficient and cost-effective. Electrification of space heating and especially transportation is expected to have the largest impact on electricity demand in the near future. Uncontrolled electric vehicle charging poses a significant challenge to grid operations, but control strategies create an opportunity for improved efficiency. Other insights include a high level of uncertainty in electrification adoption and potential benefits of combining electrification technologies with renewable generation and energy storage.