Electric Vehicle Charging Load Research Articles

Optimal Economic Analysis of Battery Energy Storage System Integrated with Electric Vehicles for Voltage Regulation in Photovoltaics Connected Distribution System

The integration of photovoltaic and electric vehicles in distribution networks is rapidly increasing due to the shortage of fossil fuels and the need for environmental protection. However, the randomness of photovoltaic and the disordered charging loads of electric vehicles cause imbalances in power flow within the distribution system. These imbalances complicate voltage management and cause economic inefficiencies in power dispatching. This study proposes an innovative economic strategy utilizing battery energy storage system and electric vehicles cooperation to achieve voltage regulation in photovoltaic-connected distribution system. Firstly, a novel pelican optimization algorithm-XGBoost is introduced to enhance the accuracy of photovoltaic power prediction. To address the challenge of disordered electric vehicles charging loads, a wide-local area scheduling method is implemented using Monte Carlo simulations. Additionally, a scheme for the allocation of battery energy storage system and a novel slack management method are proposed to optimize both the available capacity and the economic efficiency of battery energy storage system. Finally, we recommend a day-ahead real-time control strategy for battery energy storage system and electric vehicles to regulate voltage. This strategy utilizes a multi-particle swarm algorithm to optimize economic power dispatching between battery energy storage system on the distribution side and electric vehicles on the user side during the day-ahead stage. At the real-time stage, the superior control capabilities of the battery energy storage system address photovoltaic power prediction errors and electric vehicle reservation defaults. This study models an IEEE 33 system that incorporates high-penetration photovoltaics, electric vehicles, and battery storage energy systems. A comparative analysis of four scenarios revealed significant financial benefits. This approach ensures economic cooperation between devices on both the user and distribution system sides for effective voltage management. Additionally, it encourages trading activities of these devices in the power market and establishes a foundation for economic cooperation between devices on both the user and distribution system sides.

Integration of smart charging of large-scale electric vehicles into generation and storage expansion planning: a case study in south china

This paper studies how to integrate the smart charging of large-scale electric vehicles (EVs) into the generation and storage expansion planning (GSEP), while analyzing the impact of smart charging on the GSEP of a real power system in south China. For this purpose, a random simulation-based method is first developed to provide the tractable formulations of the adjustable charging load and reserve provision from EVs. This method avoids the unrealistic assumption that EVs drive and charge every day, which often exists in prior relevant approaches. Based on the random simulation, this paper proposes a novel GSEP optimization model which incorporates the weekly adjustable charging load of EVs. In the proposed model, the total charging load of EVs can be co-optimized with the investment and operational decisions of various generation and storage units. This GSEP model is applied to a provincial power system in south China. The numerical results show that the implementation of smart charging can significantly alter the decisions of GSEP. As the participation rate of smart charging improves from 0% to 90%, there is an additional 1,800 MW installation in wind and solar power, while the need to build new batteries is noticeably reduced; also, depending on the level of EV uptake, the annualized total system cost decreases by 5.11%–7.57%, and the curtailment of wind and solar power is reduced by 10.34%–19.64%. Besides, numerical tests reveal that the traditional assumption that EVs drive and charge every day can mislead the evaluation of adjustable charging load and overestimate the daily charging power peak by averagely 24.72%.

Multi-objective optimal coordination of electric vehicle charging, power grid, energy storages and renewables

Considering that the grid connection of variable renewable energies (VREs) and the disorderly charging loads of large-scale electric vehicles (EVs) will adversely affect the power grid stability, the optimization strategy of EV charging and grid-connected scheduling are investigated, in which energy storage system is added to balance the demand and supply of the power grid. First of all, considering the profit of EV charging station, the charging cost of EV users and power loss, a multi-objective optimal scheduling model of EV charging, power grid, pumped hydro-storage (PHS) and wind farm is constructed, which is improved from the aspect of wind farm wake. Then, the multi-objective optimization algorithm based on genetic algorithm is used to solve the model to realize the optimal charging of EVs. Finally, the IEEE-13 power grid is used to verify the effectiveness of the proposed multi-objective optimal scheduling model. Combined with a specific example for simulation analysis, the results show that the model can achieve orderly charging of EVs, ensuring the safety of the power grid and promote the use of VREs. In addition, the optimization algorithm can further improve the profit, and reduce the charging cost and power loss.

Electric Vehicle Charging Load Demand Forecasting in Different Functional Areas of Cities with Weighted Measurement Fusion UKF Algorithm

The forecasting of charging demand for electric vehicles (EVs) plays a vital role in maintaining grid stability and optimizing energy distribution. Therefore, an innovative method for the prediction of EV charging load demand is proposed in this study to address the downside of the existing techniques in capturing the spatial–temporal heterogeneity of electric vehicle (EV) charging loads and predicting the charging demand of electric vehicles. Additionally, an innovative method of electric vehicle charging load demand forecasting is proposed, which is based on the weighted measurement fusion unscented Kalman filter (UKF) algorithm, to improve the accuracy and efficiency of forecasting. First, the data collected from OpenStreetMap and Amap are used to analyze the distribution of urban point-of-interest (POI), to accurately classify the functional areas of the city, and to determine the distribution of the urban road network, laying a foundation for modeling. Second, the travel chain theory was applied to thoroughly analyze the travel characteristics of EV users. The Improved Floyd (IFloyd) algorithm is used to determine the optimal route. Also, a Monte Carlo simulation is performed to estimate the charging load for electric vehicle users in a specific region. Then, a weighted measurement fusion UKF (WMF–UKF) state estimator is introduced to enhance the accuracy of prediction, which effectively integrates multi-source data and enables a more detailed prediction of the spatial–temporal distribution of load demand. Finally, the proposed method is validated comparatively against traffic survey data and the existing methods by conducting a simulation experiment in an urban area. The results show that the method proposed in this paper is applicable to predict the peak hours more accurately compared to the reference method, with the accuracy of first peak prediction improved by 53.53% and that of second peak prediction improved by 23.23%. The results not only demonstrate the high performance of the WMF–UKF prediction model in forecasting peak periods but also underscore its potential in supporting grid operations and management, which provides a new solution to improving the accuracy of EV load demand forecasting.

Multi-step electric vehicles charging loads forecasting: An autoformer variant with feature extraction, frequency enhancement, and error correction blocks

The extensive integration of electric vehicles (EVs) into power grids has raised safety concerns. Meanwhile current EVs charging loads forecasting models mainly focus on single-step predictions, neglect load sparsity and underutilize residuals, limiting their accuracy. To tackle these issues, a multi-step short-term forecasting model, specifically designed to predict charging loads at public EVs charging stations using historical data, is proposed. The model begins with a preprocessing phase to handle missing data and outliers. Subsequently, an Autoformer variant is intruduced, which initially integrates feature extraction block leveraging the Temporal Convolutional Network (TCN) to adeptly capture temporal dynamics. This is further enhanced with a Discrete Cosine Transform (DCT)-based frequency enhancement block to learn sparsity and periodicity features of EVs charging loads by frequency mapping. In addition, an advanced error correction block, comprising Bayesian optimization algorithm, Convolutional Neural Networks, Multi-Head Self-Attention, and Gated Recurrent Units (BCMG), is incorporated to refine initial prediction results. Above all, the TCN-DCT enhanced Autoformer-BCMG (TDformerBCMG) model is proposed. Finally, a comprehensive experimental analysis is conducted on ACN-data for EVs charging load forecasting in 1–3 h steps. It is demonstrated that TDformerBCMG not only is effective and superior in each component, but also achieves a reduction in Mean Absolute Error by 20.94 % to 74.05 % compared to existing models. Moreover, stability is evidenced across 50 repeated trials, with the standard deviation for the Coefficient of Determination varying between 8.5844e-3 and 3.1580e-2. Further discussion on application analysis is provided. Consequently, TDformerBCMG offers a viable methodology and establishes a new benchmark for power system implementation.

Research on Machine Learning-Based Method for Predicting Industrial Park Electric Vehicle Charging Load

To achieve global sustainability goals and meet the urgent demands of carbon neutrality, China is continuously transforming its energy structure. In this process, electric vehicles (EVs) are playing an increasingly important role in energy transition and have become one of the primary user groups in the electricity market. Traditional load prediction algorithms have difficulty in constructing mathematical models for predicting the charging load of electric vehicles, which is characterized by high randomness, high volatility, and high spatial heterogeneity. Moreover, the predicted results often exhibit a certain degree of lag. Therefore, this study approaches the analysis from two perspectives: the overall industrial park and individual charging stations. By analyzing specific load data, the overall framework for the training dataset was established. Additionally, based on the evaluation system proposed in this study and utilizing both Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) algorithms, a framework for machine learning-based load prediction methods was constructed to forecast electric vehicle charging loads in industrial parks. Through a case analysis, it was found that the proposed solution for the short-term prediction of the charging load in industrial park electric vehicles can achieve accurate and stable forecasting results. Specifically, in terms of data prediction for normal working days and statutory holidays, the Long Short-Term Memory (LSTM) algorithm demonstrated high accuracy, with R2 coefficients of 0.9283 and 0.9154, respectively, indicating the good interpretability of the model. In terms of weekend holiday data prediction, the Multilayer Perceptron (MLP) algorithm achieved an R2 coefficient of as high as 0.9586, significantly surpassing the LSTM algorithm’s value of 0.9415, demonstrating superior performance.

Assessing optimized time-of-use pricing for electric vehicle charging in deep vehicle-grid integration system

The expansion of electric vehicles (EVs) and renewable energy (RE) are the two major strategies countries are adopting to achieve energy transition. However, the discrepancy between intermittent RE generation and EV charging load may impose burden on the RE-dominant power grid of the future. For the issue, Time-of-Use (ToU) pricing for EV charging has been widely discussed or adopted as its potential is well acknowledged by previous studies. While ToU mechanism has been primarily based on drivers' price-responsive behavior, this research highlights that EV charging decisions are influenced by various factors beyond price, such as time of a day, charger accessibility and waiting time. Here, we conducted the discrete choice experiment to measure drivers' preferences for EV charging, and developed an EV charging behavior model which incorporated drivers' situational-responsiveness as well as price-responsiveness. Also, the model was used to design optimal ToU tariffs to minimize net-load variation. The results showed that strategic ToU tariffs can shift EV charging load, but achieving desirable load shifts requires a significant price gap. Additionally, combining ToU pricing with strategic deployment of charging infrastructure can effectively shift EV charging load, reducing RE curtailments by 22.14%, and LNG generation and carbon emissions by 10.12%, compared to the Business-as-Usual (BaU) scenario with current tariff rates. Thus, this study highlights the importance of flexible EV charging pricing and the importance of considering charging infrastructure deployment when designing EV ToU pricing.

Planning a Hybrid Battery Energy Storage System for Supplying Electric Vehicle Charging Station Microgrids

This paper presents a capacity planning framework for a microgrid based on renewable energy sources and supported by a hybrid battery energy storage system which is composed of three different battery types, including lithium-ion (Li-ion), lead acid (LA), and second-life Li-ion batteries for supplying electric vehicle (EV) charging stations. The objective of this framework is to determine the optimal size for the wind generation systems, PV generation systems, and hybrid battery energy storage systems (HBESS) with the least cost. The framework is formulated as a mixed integer linear programming (MILP) problem, which incorporates constraints for battery ageing and the amount of unmet load for each year. The system uncertainties are managed by conducting the studies for various scenarios, generated and reduced by generative adversarial networks (GAN) and the k-means clustering algorithm for wind speed, global horizontal irradiation, and EV charging load. The studies are conducted for three levels of unmet load, and the outputs are compared for these reliability levels. The results indicate that the cost of hybrid energy storage is lower than individual battery technologies (21% compared to Li-ion, 4.6% compared to LA, and 6% compared to second-life Li-ion batteries). Additionally, by using HBESS, the capacity fade of LA batteries is decreased (for the unmet load levels of 0, 1%, 5%, 4.2%, 6.1%, and 9.7%, respectively), and the replacement of the system is deferred proportional to the degradation reduction.

A novel framework for assessing the smartness and the smart readiness level in highly electrified non-residential buildings: A Norwegian case study

This work presents a new operational framework to measure the smartness and smart readiness of highly electrified buildings. The framework seeks to enhance legacy systems and controls of existing buildings and establish minimum criteria for future constructions to ensure they interact effectively with users and the grid, aiming for a clean energy transition. To this end, we develop two modified complementary assessments, one based on the method indicated by the Smart Readiness Indicator (SRI), proposed by the European Union (EU), and the other following the Smart by Powerhouse scheme, introduced by a Norwegian consortium of stakeholders focused on developing future proof climate buildings. The proposed structure is implemented in ten non-residential buildings in Norway with different energy systems, typologies, and construction dates. The results of this study demonstrate that energy flexibility quantification plays a crucial role in correctly implementing the framework in highly electrified buildings. Therefore, the dynamic impact of having Electric Vehicle Charging (EVC) and other electrical-dependent loads must be considered in the assessment. With the proposed modifications, the EVC weight in the flexibility score now varies from 24.0 to 43.6%, up from the original 5%. Overall, the pilot buildings have a smart readiness level between 21.6% and 31.7%, with mostly automated smartness levels. Nevertheless, the study also emphasizes the need to differentiate current HVAC (Heating, Ventilation, and Air Conditioning) technologies and their efficiencies.

Impact of bi-directional electric vehicle and demand response on residential distributed PV capacity planning based on TOU pricing

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect.

Economic and low-carbon dispatch of industrial integrated energy system with EV load based on Stackelberg game framework

Industrial integrated energy systems (IESs) and electric vehicles (EVs) provide new solutions for addressing the increasing challenges of the energy crisis and environmental pollution. With the increasing number of EVs and smart charging stations in industrial IES, the uncoordinated charging load of EVs imposes significant pressure on IES. Therefore, a well-designed dispatch scheme is crucial for reducing the economic cost for both parties, alleviating the energy supply pressure on IES, and promoting the development of a low-carbon society. To this end, given the load characteristics of EVs in industrial IES, we propose a dispatch framework based on the Stackelberg game theory, which includes the leader and the follower. The leader IES is responsible for formulating both unit dispatch and demand response plans, as well as determining the charging pricing for the smart charging station. The follower smart charging station optimizes EVs charging power by minimizing the charging cost in order to protect the interest of EV owners. Additionally, we introduce the carbon emission flow model into charging station pricing to shift the responsibility for carbon emissions from the generation side to the EV load side. Considering that the two-layer game model is difficult to solve, we use the Karush–Kuhn–Tucker condition and duality theorem to transform it into an equivalent single-layer optimization problem, which is easily solved. Simulation results demonstrate that the proposed game framework effectively reduces the economic cost of IES and the charging cost of EVs, alleviates the pressure from charging load, and reduces the carbon emissions of industrial IES.

Research on electric vehicle load forecasting considering regional special event characteristics

With the rise of electric vehicles and fast charging technology, electric vehicle load forecasting has become a concern for electric vehicle charging station planners and operators. Due to the non-stationary nature of traffic flow and the instability of the charging process, it is difficult to accurately predict the charging load of electric vehicles, especially in sudden major events. In this article, We proposes a high-precision EV charging load forecasting model based on mRMR and IPSO-LSTM, which can quickly respond to the epidemic (or similar emergencies). Firstly, the missing data in the original EV charging load data are supplemented, and the abnormal data are corrected. Based on this, a factor set is established, which included five epidemic factors, including new confirmed cases, the number of moderate risk areas, the number of high risk areas, epidemic situation and epidemic prevention policies of the city, and other factors such as temperature. Secondly, the processed load data and other data in the influencing factor set are normalized, and the typical characteristic curve is established for personalized processing of the relevant data of epidemic factors, so as to improve the sensitivity of load response to epidemic changes and the ability to capture special data (peak and valley values and turning points of load). Then the maximum relevant minimum redundancy (mRMR) is used to select the optimal feature set from the set of influencing factors. Then, the processed load data and its corresponding optimal selection are input into the IPSO-LSTM model to obtain the final prediction result. Finally, taking the relevant data of EV charging load in a city in China from November 2021 to April 2022 (the city experienced two local epidemics in December 2021 and March 2022 respectively) as an example, the model is evaluated and compared with other models under the forecast period of 1 h. Meanwhile, the performance of the model under different foresight periods (2 h, 4 h, 6 h) is compared and analyzed. The results show that the model has good stability and representativeness, and can be used for EV charging load prediction under the COVID-19 pandemic.

Load Frequency Control and Optimization of Load Allocation for Multi-Area Power System with Electric Vehicle Charging Load

The integration of electric vehicles (EVs) into power systems has introduced new challenges for load frequency control due to the additional charging load they impose. This research article investigates the design and analysis of automatic load frequency control in a two-area power system, considering the presence of EV charging load. The study employs Artificial Neural Network (ANN) based PI control to manage both traditional load demand and the dynamic charging requirements of EVs. Maintaining a stable power system frequency and balancing generation with the EV charging load have become crucial tasks. Automatic Generation Control (AGC) or Load Frequency Control (LFC) systems need to adapt and account for the variability and uncertainty associated with EV charging patterns. The integration of ANN-based PI control provides an intelligent and adaptive approach to address these challenges. Using MATLAB, a power system model is simulated to evaluate the effectiveness of the proposed control scheme. This investigation conducts a comparative analysis of the system's frequency responses under various scenarios, including different EV charging load profiles. It highlights the benefits and challenges of utilizing ANN-based PI control to manage the combined load of traditional demand and EV charging. Moreover, the load distribution among the distribution stations varied from 0.08% to 12.50% when compared between particle swarm optimization and genetic algorithm, respectively. By considering the dynamic interaction between power system operation and EV charging, this research aims to enhance the reliability, efficiency and sustainability of power systems in the context of evolving transportation trends and the increasing electrification of vehicles.

Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain

Block-FeDL: Electric Vehicle Charging Load Forecasting using Federated Learning and Blockchain

Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Electric Vehicle Charging Load Optimization Strategy Based on Dynamic Time-of-Use Tariff

Assessment of the carbon emissions reduction potential in Thailand’s power sector with high penetration of variable renewable energy sources and electric vehicles in the year 2030

Abstract The power sector has substantial carbon emissions reduction potential that could achieve the new nationally determined contribution target in 2030 by increasing low-carbon technologies, e.g. variable renewable energy sources and electric vehicles (EVs). Therefore, two approaches were suggested in this work. In the first approach, Thailand’s power sector was modelled by using PLEXOS software to find the impact of the high penetration of variable renewable energy (RE) and EV charging load for the projected year 2030. The second approach proposed a demand–response and energy storage system solution with carbon pricing in the model to assess the targets. As a result, the electricity demand from charging EVs will cause a new peak demand at night, while the high variable RE penetration will cause curtailment in the power system due to excess supply at noon. Therefore, Thailand’s power sector has the potential for carbon emissions reduction by 45% with clean energy technologies, which could increase to 68% with carbon pricing, easily achievable by the nationally determined contribution target in 2030. However, these benefits could only be derived if various entities involved in the energy regulatory, transport and power sectors coordinate to implement the required technological and financial policies.

Count the charging load forecast of the battery remaining life and ownership

In order to solve the accuracy of EV charging load prediction, the residual life reduction and the exact value of EV ownership caused by battery aging are taken into consideration. The charging load curve of the EV is simulated based on the Monte Carlo simulation method. Finally, based on the actual data of a certain region, the charging load of electric vehicles within a day is obtained. The case analysis shows that the proposed charging load prediction model of electric vehicles has high accuracy, which also shows that the large-scale grid connection of electric vehicles will bring new challenges to the power system. This method provides data support for the subsequent formulation of electric vehicle charging strategy and load side peak shifting and valley filling.

Demand-Side and Utility-Side Management Techniques for Increasing EV Charging Load

Electricity authorities need capacity assessment and expansion plans for efficiently charging the growing Electric vehicle (EV) fleet. Specifically, the distribution grid needs significant capacity expansion as it faces the most impact to accommodate the high variant residential EV charging load. Utility companies employ different scheduling policies for the maintenance of their distribution transformers (hereinafter, XFR). However, they lack scenario-based plans to cope with the varying EV penetration across locations and time. The contributions of this paper are twofold. First, we propose a customer feedback-based EV charging scheduling to simultaneously minimize the peak load for the distribution XFR and satisfy the customer needs. Second, we present a deep reinforcement learning (DRL) method for XFR maintenance, which focuses on the XFR’s effective age and loading to periodically choose the best candidate XFR for replacement. Our case study for a distribution feeder shows the adaptability and success of our EV load scheduling method in reducing the peak demand to extend the XFR life. Furthermore, our DRL-based XFR replacement policy outperforms the existing rule-based policies. Together, the two approaches provide a complete capacity planning tool for efficient XFR maintenance to cope with the increasing EV charging load.

A hybrid electric vehicle load classification and forecasting approach based on GBDT algorithm and temporal convolutional network

Due to the participation of large-scale electric vehicles (EVs) in Vehicle-to-Grid (V2G) services, V2G dispatch centers need to predict the charging and discharging (C&D) loads of electric vehicles in a short time period. This study proposes a novel machine learning based approach for EV load forecasting in power supply systems facing high resource uncertainty. This method takes advantage of both Gradient Boosting Decision Tree (GBDT) algorithm and Time Convolutional Network (TCN) model. This study considers the service decisions of EV users and uses the GBDT algorithm to classify the EV discharge load dataset, with 92% accuracy. Also, the TCN model is used to capture local temporal features and predict the future C&D loads. In comparison with other baseline models, e.g. CNN-BILSTM, LSTM, PSO-BP, the stability of the TCN model is superior in real data charging load forecasting, and it is shown that the TCN model has the smallest error. The feasibility of the proposed GBDT-TCN hybrid model is verified by numerical cases,and achieves the RMSE of discharging forecasting less than 6.23%.

Mixed-integer second-order cone programming method for active distribution network

Developing a novel type of power system is an important means of achieving the “dual carbon” goals of achieving peak carbon emissions and carbon neutrality in the near future. Given that the distribution network has access to a wide range of distributed and flexible resources, reasonably controlling large-scale and adjustable resources is a critical factor influencing the safe and stable operation of the active distribution network (ADN). In light of this, the authors of this study propose a mixed-integer second-order cone programming method for an active distribution network by considering the collaboration between distributed, flexible resources. First, Monte Carlo sampling is used to simulate the charging load of electric vehicles (EVs), and the auto regressive moving average (ARMA) and the scenario reduction algorithms (SRA) based on probability distance are used to generate scenarios of the outputs of distributed generation (DG). Second, we establish an economical, low-carbon model to optimize the operation of the active distribution network to reduce its operating costs and carbon emissions by considering the adjustable characteristics of the distributed and flexible resources, such as on-load tap changer (OLTC), devices for reactive power compensation, and EVs and electric energy storage equipment (EES). Then, the proposed model is transformed into a mixed-integer second-order cone programming (SOCP) model with a convex feasible domain by using second-order cone relaxation (SOCR), and is solved by using the CPLEX commercial solver. Finally, we performed an arithmetic analysis on the improved IEEE 33-node power distribution system, the results show that ADN’s day-to-day operating costs were reduced by 47.9% year-on-year, and carbon emissions were reduced by 75.2% year-on-year. The method proposed in this paper has significant effects in reducing the operating cost and carbon emissions of ADNs, as well as reducing the amplitude of ADN node voltages and branch currents.