High Penetration Of Electric Vehicles Research Articles

Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles

With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

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.

Application of Demand-Side Management (DSM) for evaluation and optimization of Electric Vehicle’s charging cycles

Electric vehicles (EVs) have been known as potential transportation alternatives for internal combustion engines thanks to lower carbon emissions and high energy efficiency. The combination of EVs with other renewable energy sources (RES) is a viable solution for eliminating the emission on the road and during the electricity generation process, reducing the reliance of EVs energy demand on the non-renewables. However, the higher penetration of EVs and unpredictable charging dynamics could burden the power system tremendously. This study proposes a demand-side management (DSM) strategy for an EVs-integrated residential grid with the assistance of grid-tied rooftop photovoltaic (PV) systems. The main objectives are to minimize the peak and fluctuation load while ensuring the EVs’ effective charging and vehicle-to-grid (V2G) scheme based on time-of-use (TOU) tariff and real-time pricing (RTP). The EVs’ traveling distances, driving periods, initial state-of-charge (SOCs), and plugging states are considered. One-day simulation in different scenarios was carried out to evaluate the strategies. The results indicate that the proposed model can reduce load demand, flatten the load curve, and reduce electricity costs, maintaining the system’s stability. The approach can be an indicator for load aggregators and utilities to manage PEVs as DSM tools in the system.

Factors affecting electric vehicle acceptance, energy demand and CO2 emissions in Pakistan

This work aims to investigate the factors accelerating electric vehicle (EV) acceptance at the consumer end in Pakistan and analyzes the implications for policymakers for a fast-track EV transition. The study further investigates the high EV penetration scenario resulting from the technology acceptance model (TAM's 80% EV) and its impact on energy demand and CO2 emissions. The study design used a quantitative analysis method with the survey as an instrument for data collection regarding EV acceptance. The model under investigation was adapted from the famous Technology-Acceptance Models (TAMs) and modified with other significant predictors evidenced in the literature. Correlation and stepwise regression were performed with a multicollinearity check for model hypothesis testing. Out of six predictors, only four factors were significant in accelerating the EV transition. Financial policies were found to be highly significant, followed by environmental concern, facilitating conditions and perceived ease of use. The research then used exponential smoothing forecasts for transport demand and developed an EV penetration scenario based on modified TAM results. The results highlight the significant increase in transport demand and the opportunity for Pakistan to limit passenger transport emissions to 36.6 ​MT instead of 61.6 ​MT by 2040.

Stochastic user equilibrium based spatial-temporal distribution prediction of electric vehicle charging load

As the number of electric vehicles (EVs) connected to the grid increases, the EV electricity demand rises dramatically, affecting the grid’s planning and operation and deepening the coupling of the power and transportation systems. Therefore, accurate spatial–temporal distribution prediction of EV charging load is vital for both power system and coupled power-transportation system studies. This paper proposes a novel method based on stochastic user equilibrium (SUE) for predicting the accurate spatial–temporal distribution of EV charging load synchronized with traffic states. A prediction framework of EV charging load based on SUE and trip chain is proposed, which can effectively reflect the actual behavior of EVs in synchronous traffic states. Then, the extended logit-based SUE and equivalent mathematical model are proposed to obtain more detailed traffic states with intersection turning flows and delays. Meanwhile, the unified reachability and charging models are established to ensure that the trip chain is reachable and the charging characteristics are suitable for different EV types. Finally, the method of the successive averages (MSA) and the Dijkstra-based K-shortest paths algorithms are integrated to solve the proposed framework iteratively with stable convergence. Test results on a realistic traffic network show that the proposed method can effectively reflect the charging and trip characteristics of different EV types while ensuring reachability. And it can also accurately predict the overall and individual EV travel costs and total charging loads in detailed synchronous traffic states. In particular, even in the case of high EV penetration with higher peak-to-valley differences and charging demand, the convergence of the prediction is still stable with even more remarkable prediction effectiveness, especially during peak load hours. Furthermore, the quantitative analysis based on proposed criticality indexes reveals that traffic network failures will affect the network-wide traffic states and EV charging loads with different node-level impact characteristics, which should be considered in joint power-transportation restoration scheduling.

Hierarchical Optimization for User-Satisfaction-Driven Electric Vehicles Charging Coordination in Integrated MV/LV Networks

The growing uptake of electric vehicles (EVs) will likely require management schemes to enable their connection into distribution systems. While most of the existing approaches are developed from the operator’s perspective considering EV aggregated demands at the medium-voltage (MV) level, individual users’ comfort and the particularities associated with low-voltage (LV) networks need to be considered to holistically assess the EV effects in an integrated MV/LV network. This article proposes a two-level hierarchical optimization framework for the EV charging coordination (EVCC) that maximizes users’ satisfaction, while avoiding operational grid issues in the whole distribution system. The framework is tailored for unbalanced distribution systems with high penetration of EVs and introduces a novel index to measure charging priority-based EV user satisfaction. To reduce the computational burden, the EVCC problem is disaggregated into an upper level for MV network operation, and a lower level for LV network and individual EV scheduling, using mixed-integer linear programming models. This framework is later embedded in a dynamic scheduling approach that copes with unexpected EV arrivals. Benefits (increased overall user satisfaction and reduced strain over distribution assets) are demonstrated via case studies in a 459-node three-phase network in which solutions were achieved under a 60-s threshold.

Tactical Participation of a Multienergy System in Distributionally Robust Wholesale Electricity Market Under High Penetration of Electric Vehicles

This study puts forward a bilevel optimization framework to evaluate the tactical price-setter strategy of a multienergy system (MES) in the wholesale electricity market (WEM). The MES is integrated with smart electric vehicle (EV) parking lots and the compressed air energy storage system (CAES). As the master-level player, the MES operator deploys coordinated EV charging strategies and CAES flexibilities to minimize the operational costs and submit the best offer/bid in WEM. At this stage, the WEM operator (follower-level player) collects offers/bids from MES and other market participants to clear the WEM with the optimal public interest and announce the market-clearing price. The vehicles were congregated in electric vehicle fleets via K-means clustering according to their uncertain specifications, such as daily travelled miles and arrival/departure times. Afterward, the stochastic programming scenarios were generated for each fleet according to their user-based probability distribution functions. However, more volatile parameters, e.g., wind speed, were handled by the data-driven distributionally robust optimization method, which is embodied by an ambiguity set within the Wasserstein ball. The proposed approach is simulated in two 24-bus and 39-bus IEEE standard test systems.

Hierarchically coordinated optimization of power distribution systems with soft open points and electric vehicles

The integration of large-scale electric vehicles (EVs) into power distribution systems (PDSs) causes low voltage and line congestion problems. EV charger converters can improve the voltage profile by compensating the reactive power, whereas soft open points (SOPs) can transfer active power between branches and compensate reactive power, which can help balance the branch power. Therefore, this study proposes a coordinated day-ahead (DA) and real-time (RT) optimization model for PDSs with SOPs and high penetration of EVs. In addition, directly controlling large-scale EVs is difficult for the distribution systems operator. Thus, hierarchical management of EVs is performed by using an EV aggregator (EVA) model. First, on the basis of the probability distribution of the state of EVs of each EVA, a DA optimization model with the minimum operation cost is established. A dynamic SOC boundary model is proposed to optimize active and reactive power of EVAs and EVs in RT stage. The original nonlinear problems of the DA and RT models are linearized by convex relaxation and linearization, which significantly improves the solving efficiency. Finally, the case studies demonstrate that SOPs can promote the integration of EVs, and the hierarchical coordinated optimization strategy not only can reduce the operation cost of the PDS, but also control active and reactive power of EVs in real time.

Applications of Clustering Methods for Different Aspects of Electric Vehicles

The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management.

An aggregator-based dynamic pricing mechanism and optimal scheduling scheme for the electric vehicle charging

High penetration of electric vehicles (EVs) in an uncontrolled manner could have disruptive impacts on the power grid, however, such impacts could be mitigated through an EV demand response program. The successful implementation of an efficient, effective, and aggregated demand response from EV charging depends on the incentive pricing mechanism and the load shifting protocols. In this study, a genetic algorithm-based multi-objective optimization model is developed to generate hourly dynamic Time-of-Use electricity tariffs and facilitate the decision making in load scheduling. As an illustrative example, a case study was carried out to examine the effect of applying demand response for EVs in Beijing, China. With the assumptions made, the maximum peak load can be reduced by 9.8% and the maximum customer savings for the EVs owners can reach 11.85%, compared to the business-as-usual case.

A Decentralized Dynamic Pricing Model for Demand Management of Electric Vehicles

Transportation electrification is considered a green alternative to internal combustion vehicles. However, higher penetration of electric vehicles (EVs) can cause several technical challenges to the power systems, including local equipment overloading. This is especially challenging for distribution systems due to the direct connection of EVs with them. To mitigate equipment overloading, by managing EV loads locally, a dynamic pricing model is proposed in this study. First, a satisfaction function is devised for EVs considering the sensitivity of different EV owners to the battery state of charge and urgency of recharge. Then, a welfare function is developed for the EV fleet operator (EFO) considering the net load of EVs and the electricity price of the upstream grid. To this end, a welfare maximization problem is formulated considering the welfare of EVs and EFO. The problem is then decomposed into the EFO sub-problem and EV sub-problem to preserve privacy. Finally, a distributed mechanism is developed to solve the sub-problems iteratively without revealing private information. The performance of the proposed method is analyzed for a residential apartment complex in terms of load management and convergence for different day types (working days and holidays). Simulation results have shown the efficacy of the proposed method in mitigating the overloading of transformers during peak load hours.

Electric Vehicles Under Low Temperatures: A Review on Battery Performance, Charging Needs, and Power Grid Impacts

Electric vehicles (EVs) are gaining mainstream adoption as more countries introduce net-zero carbon targets for the near future. Lithium-ion (Li-ion) batteries, the most commonly used energy storage technology in EVs, are temperature sensitive, and their performance degradates at low operating temperatures due to increased internal resistance. The existing literature on EV-power grid studies assumes that EVs are used under “perfect temperatures" (e.g. 21 Celsius) and temperature-related issues are ignored. In addition, most of the countries/regions with high EV penetration (e.g. Norway, Canada, northern parts of the US and China, etc.) experience harsh cold months, making it extremely critical to understand EV performance and consequently their impacts on the electrical power networks. In this paper, we present a systematic review of the literature that considers the combined investigation of Li-ion battery technology and power networks, with a focus on their operation under suboptimal weather conditions. More specifically, we review: (i) the impact of low temperatures on the electrochemical performance of EV batteries in parking, charging and driving modes, (ii) the challenges experienced by EVs during charging and associated performance degradation, and (iii) the additional impacts of EV charging on the power networks. Our analysis shows that there are serious research gaps in literature and industry applications, which may hinder mass EV adoption and cause delays in charging station roll-out.

Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review

The high penetration of electric vehicles (EVs) will burden the existing power delivery infrastructure if their charging and discharging are not adequately coordinated. Dynamic pricing is a special form of demand response that can encourage EV owners to participate in scheduling programs. Therefore, EV charging and discharging scheduling and its dynamic pricing model are important fields of study. Many researchers have focused on artificial intelligence-based EV charging demand forecasting and scheduling models and suggested that artificial intelligence techniques perform better than conventional optimization methods such as linear, exponential, and multinomial logit models. However, only a few research studies focused on EV discharging scheduling (i.e., vehicle-to-grid, V2G) because the concept of EV discharging electricity back to the power grid is relatively new and evolving. Therefore, a review of existing EV charging and discharging-related studies is needed to understand the research gaps and to make some improvements in future studies. This paper reviews EV charging and discharging-related studies and classifies them into forecasting, scheduling, and pricing mechanisms. The paper determines the linkage between forecasting, scheduling, and pricing mechanism and identifies the research gaps in EV discharging scheduling and dynamic pricing models.

Coordinated Control of Electric Vehicles and PV Resources in an Unbalanced Power Distribution System

Improving air quality, reducing greenhouse gas emissions, and achieving independence from fossil fuels have led most countries towards deploying solar photovoltaics (PV) in the power distribution grid and electrifying the transportation fleet. Internal combustion engine (ICE) vehicles are, in particular, one of the main culprits of injecting greenhouse gas emissions into the atmosphere, making electric vehicles (EVs) an important tool in combating climate change. Despite their considerable environmental and economic benefits, the integration of PVs and EVs can introduce unique operational challenges for the power distribution grid. If not coordinated, high penetration of PVs and EVs can result in variety of power quality issues, such as instances of overvoltage and undervoltage, frequency fluctuations, and/or increased losses. This paper proposes a mixed-integer multi-objective nonlinear optimization model for optimal energy dispatch in a power distribution grid with high penetration of PV and EV resources. The model proposed here is an extension of the traditional voltage and var optimization (VVO) into a comprehensive and coordinated control of voltage, active power, and reactive power. A modified version of the IEEE 123-bus test distribution system is used to demonstrate the effectiveness of the proposed solution.

A novel methodology for the planning of charging infrastructure in the scenario of high EV penetration

This article presents a novel methodology for distribution network expansion planning (DNEP) considering the inclusion of electric vehicles (EVs), especially, electric bus (EB) charging loads. The proposed methodology addresses network congestion through an optimum time of charging, cost optimization, new charging infrastructure, and minimization of losses under a set of technical and physical constraints, which represents practical uncertainties. Along with load flow analysis, selection of the number of ports and technology at the host charging station is obtained through the application of response surface methodology. The proposed methodology provides coordinated planning for the development of EB charging station infrastructure that takes into account the effects of both the power dispersion framework and transportation framework. The effectiveness of the proposed methodology is investigated by applying it to the 69-node IEEE modified distribution test system considering three charging technologies, viz. fast charging, ultra-fast charging, and battery swapping. The results of the proposed model are compared with the direct statistical method, and it revealed that the right selection of technology for EB charging and the right planning of the charging infrastructure can effectively optimize the cost of EV charging infrastructure and thereby catalyze the decarbonization of the transportation sector.

A deep learning approach for prediction of electrical vehicle charging stations power demand in regulated electricity markets: The case of Morocco

The transport sector is a prominent source of increasing fuel consumption and greenhouse gas (GHG) emissions. Electric vehicle (EV) is deemed an appealing solution for those problems. However, due to the variation in charging demands, the high penetration of electric vehicles may cause different problems to the distribution network and its reliability. Therefore, several approaches are employed to predict the EVs charging demand and avoid the corresponding challenges. This paper compares the performance of four well-known deep learning models, namely artificial neural networks (ANN), recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), in predicting charging demand for EV users after a charging session begins. We use a dataset consisting of 2000 observations of charging events collected from two public charging stations in Morocco. According to numerical data results, the first layer of the GRU regression approach marginally beats the other three methods in estimating power charging needs. Specifically, the GRU regression model has an RMSE and MAPE of 1.39% and 0.50% in the training stage and 2.90% and 0.76% in the testing stage, respectively. These findings can assist the National Office of Electricity and Water in Morocco in ensuring the reliability of grid utility in the short run and guiding them to construct additional charging stations in the long run.

Utility-scale energy storage system for load management under high penetration of electric vehicles: A marginal capacity value-based sizing approach

To determine a trade-off between the battery energy storage system (BESS) size and corresponding benefits in managing the load of distribution systems under high penetration of electric vehicles (EVs), a marginal capacity value-based sizing approach is proposed in this study. First, the yearly load profiles of EVs are estimated considering different factors, such as EV parameters, driver behavior, day types, and customers at each secondary distribution transformer. Then, a utilization index is proposed to quantify the benefits of increasing the BESS size. To this end, an iterative method is proposed to determine the BESS size with a given utilization target. To expedite the convergence speed, a binary and interpolation search-inspired algorithm is proposed, which can determine the BESS size within a limited number of iterations. The performance of the proposed BESS sizing approach is compared with three other approaches, i.e., optimization-based sizing approach, metaheuristic sizing approach, and enumeration-based sizing approach. The obtained results are tested and validated using the data of a real distribution system. Finally, a sensitivity analysis of different user defined factors such as granularity level in BESS size and utilization target levels is carried out. Simulation results have shown that the proposed method can significantly reduce the BESS size while having minute impact on the peak shaving.

Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040

Electric vehicles (EVs) are expected to grow massively in the coming years, and grid integration of a large number of them could challenge electricity-system infrastructure. This paper aims at describing a methodology to study the technical and economic impacts on power systems of mass EV charging for several EV-owner connection behavior profiles (systematic, when necessary, when convenient) and the range of recharge modes available (uncontrolled, time-of-use tariff, smart unidirectional charging, and vehicle-to-grid). This framework is applied to a case study at hourly resolution of high penetration of electric vehicles and renewable energy sources in Europe at the 2040 time-horizon, in line with the ‘National Trends Scenario’ grid mix under the pan-EU ENTSO-E Ten-Year Network Development Plan. Results show that the European electricity system can accommodate large EV growth and that widespread adoption of smart charging in France can significantly reduce operational electricity system costs by up to 1.1 G€ and reduce carbon emissions by up to 3.2 MtCO2 per year. Multiple EV smart charging modes are also compared and the parameters have the largest impact on EV flexibility are identified, including gas prices, smart charging adoption, weekly flexibility, and mid-day charging.

A novel optimization method for high-penetration electric vehicles integration to electrical distribution network

With the implementation of China’s “double carbon” strategy, the usage scale of electric vehicles has grown rapidly. Therefore, how to maintain the ideal voltage and energy curve in the distribution network with high penetration of electric vehicles is a challenging problem. In this paper, the random behaviors of fast charging, ordinary charging and discharging modes of electric vehicles have been analyzed and the mathematical formulation of the algorithm has been presented. Then, the strategy of coordinated charging-discharging stack of electric vehicles is proposed. The improved particle swarm optimization algorithm based on mixed real number and binary vector is used to solve the optimization model. The results of several case studies have also been presented in this paper to show that optimum capacitor switching and transformer tap adjustment solutions can be found to minimize the total operation cost including energy consumption, power quality and reactive power compensation equipment action cost. The paper demonstrates that the impact of high-penetration electric vehicles on the energy and voltage control of the distribution network has been solved. The proposed EV coordinated stacking method can make electric vehicles charge and discharge in an orderly queue, and ensure that the line flow does not exceed the limit. Through the proposed control strategy, the voltage curve is obviously improved, and the cost of the distribution system with large-scale EVs can be effectively reduced.

Mitigation of transformers’ loss of life in power distribution networks with high penetration of electric vehicles

This paper presents a fuzzy-logic-based framework for aging mitigation of distribution transformers. At the first stage, a model for loading estimation of a transformer installed in the head of a power distribution network was developed in the MATLAB-Simulink environment. The model considers several factors that negatively affect the transformer's operation and lead to overheating of its windings, including high ambient temperatures, harmonic voltage distortions generated by nonlinear loads, reverse power flows, overloads due to high penetration of plug-in electric vehicles. At the second stage, a tuning algorithm was developed and measures aimed at optimizing the transformer's load level and power factor were elaborated. The developed model analyzes the parameters and factors that affect the normal operation of the transformer, and warns of the presence of dangerous factors that pose a threat to reliability and can lead to malfunction. Moreover, the effectiveness of photovoltaic generation units, shunt capacitor banks and battery energy storage systems, installed at the secondary voltage side, for distribution transformers' lifetime preserving were analyzed and discussed. The proposed measures allowed to reduce the transformer's loss of life more than 7 times, compared to the initial scenario.