Electric Vehicle Charging Load Research Articles

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.

Computational Challenges and Approaches for Electric Vehicles

Researchers worldwide have become increasingly interested in developing computational approaches to handle challenges facing electric vehicles (EVs) in recent years. This article examines the challenges and future potential of computational approaches for problems such as EV routing, EV charging scheduling (EVCS), EV charging station (CS) placement, CS sizing, and energy or load management. In addition, a summary of the fundamental mathematical models employed to solve the EV computational problems is presented. We cover recent work on computational solutions for various EV problems utilizing single and coupled mathematical models. Finally, we also examine potential research avenues that researchers could pursue to realize the objective of environment-friendly transportation and smart grids (SGs).

Power Measurement Using Adaptive Chirp Mode Decomposition for Electrical Vehicle Charging Load

Due to nonlinear components in the charging piles of electric vehicles, harmonics and nonstationary signals in the electric vehicle charging load bring voltage and current distortion, seriously affecting the accuracy of the power-related calculation in nonsinusoidal environments. This paper proposed a new approach to calculate the active power and root mean square values from decomposed components using the adaptive chirp mode decomposition (ACMD) method on voltage and current. The advantage of the ACMD-based method is that it correctly provides the power-related quantities of harmonics or nonstationary components for the electric vehicle charging load. The performance of the proposed method was verified using synthetic signals and simulation tests. The experimental results presented better estimations for each quantity defined in IEEE Standard 1459-2010, compared with the discrete wavelet transform approach.

Comparison of mono and bifacial modules for building integration and electric vehicle charging: A case study in Sweden

Future energy systems have a major difficulty in ensuring a reliable supply of electricity without affecting the environment, and numerous innovative renewables-based solutions are being introduced to meet this issue. This paper proposes a building photovoltaic (PV) system design for residential and electric vehicle (EV) charging demand and evaluates the techno-economic and environmental performance of the system in Orebro, Sweden, as it aims to become net zero carbon economy by 2045. Literature review shows that, although many studies exist, most of them did not fully consider the techno-economic and environmental aspects of PV systems for residential and EV charging loads in the chosen location. Two different PV technologies monofacial and bifacial monocrystalline panel in three different roof slopes 15°,30° and 45° has been analyzed to find the optimized system that can meet a typical house's annual energy demand. Economic indicators such as cumulative cash flow, levelized cost of electricity (LCOE), payback period and cost of EV charging have been evaluated for the PV system without discount and with discount which affects the system's profitability. PVSyst software was used to simulate the system for energy generation. Results have shown that the bifacial PV system performed better in energy generation, which is approximately 10% higher than the monofacial panel. However, in terms of economics, Case 6, a bifacial PV system with a roof angle of 45°, shows the lowest payback period of 7.3 years. In contrast, monofacial PV system with roof slope of 30° showed LCOE of 0.8988 Swedish Krona per kilowatt hour (SEK/kWh), EV charging cost of 0.1471 Swedish Krona per kilometer (SEK/km). Environmental parameters such as greenhouse gases (GHG) reduction have been analyzed. Results showed that GHG savings due to EV was higher than PV plant as Sweden's grid emission factor is very low due to less dependency on fossil fuels. The significance of this study will enable us to understand the performance of PV systems in Swedish aspect and methods can be extended to other countries for meeting location-specific energy demand.

Charging load forecasting of electric vehicles based on sparrow search algorithm‐improved random forest regression model

AbstractIn order to solve the problem that the current charging load forecasting accuracy is not high, it is difficult to simulate the actual charging load distribution of Electric Vehicles (EVs), and it is impossible to reasonably predict the future load, a charging load forecasting model based on Sparrow Search Algorithm (SSA) improved Random Forest Regression (RFR) is proposed. The SSA is used to enhance the ability of global optimization and local exploration. Combined with the advantages of the RFR model, such as low generalization error, fast convergence speed, and few adjustment parameters, the SSA was used to optimize the parameters of the decision tree number and the number of split nodes in the RFR, and the optimal value of the parameters is obtained, so as to obtain the optimal performance of the RFR. Firstly, based on the concept of travel chain and conditional probability distribution, the user's travel habits are described. Monte Carlo simulation method was used to simulate the driving, parking, and charging behaviours of a large number of EVs in different regions, so as to obtain the charging load of EVs in different regions. Then, a charging load forecasting model based on SSA improved RFR is established. Monte Carlo simulation results are used as sample data to predict the charging load of EVs in different regions. Finally, taking a certain area as an example, the experimental results show that the charging load prediction model based on Sparrow Search Algorithm improved Random Forest Regression (SSA‐RFR) can accurately predict the charging load of EVs in different regions, and the charging load of different regional types is obviously different. Compared with the RFR model and other literature models, the SSA‐RFR model has better prediction accuracy, which verifies the feasibility and superiority of SSA‐RFR model in EVs charging load prediction.

Status and Development of Research on Orderly Charging and Discharging of Electric Vehicles

As the scale of electric vehicles continues to expand, the charging load of electric vehicles into the network has become an issue that cannot be ignored. This paper introduces the concept and development of ordered charging based on the current background of ordered charging research. The application architecture of ordered charging is summarized, and the advantages and disadvantages of centralized, distributed, and hierarchical control architectures are introduced. The current status of research on orderly charging is analyzed at four levels: steps and methods of load modeling for orderly charging, optimization objectives of orderly charging, optimization methods of orderly charging, and practical projects of orderly charging. The methods of load modeling for orderly charging are summarized, different optimization objectives of grid operation for orderly charging are introduced, and the advantages and disadvantages of different optimization algorithms are compared and analyzed. Practical projects on orderly charging illustrate the great potential of orderly charging. This paper points out four problems of communication, data security, market mechanism, and the number of charging stations that orderly charging is currently facing and proposes feasible solutions. The development prospect of orderly charging being more environmentally friendly, energy-efficient, intelligent, and safe is proposed.

Static voltage stability influence evaluation method of distribution network including electric vehicles based on LHS-PPF

The temporal and spatial distribution of electric vehicle charging load (EVCL) is closely related with the drivers’ trip behavior, which has a highly uncertainty. The access of EVCL will increase the risk of safe and stable operation of the distribution network. To evaluate the impact of EVCL access to system’s static voltage stability, we propose a novel probability evaluation method based on the combination of static voltage stability evaluation model and probabilistic power flow (PPF). Firstly, considering steady state operation, dynamic operation and ultimate operation of the distribution network, the three different static voltage stability evaluation models are established respectively. Then, the probabilistic power flow model is adopted to simulate the impact of EVCL on the power flow of the distribution network. To improve the low calculation efficiency of the PPF model based on the Monte Carlo sampling method, the Latin hypercube sampling (LHS) is used. The LHS-PPF model not only ensures calculation accuracy but also improves calculation efficiency. Finally, based on MATLAB 2020a simulation software, the proposed method is programmed in M language. The results show that the access of EVCL will obviously decrease the system anti-interference capability, and increase the risk of the system approaching limit operation state. The case studies verify the correctness and effectiveness of the proposed method.

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.

Interaction Strategy Between the Vehicle-station-grid Considering Privacy Protection

The number of electric vehicles (EVs) is increasing daily, and the charging demand for large-scale EVs has a greater impact on the distribution network, so it is urgent to study the cooperative interaction optimization strategy of the “EV-charging station-distribution grid.” In this paper, we take the charging load of EVs in the regional distribution network as the research object. We establish a multi-intelligence reinforcement learning-based optimization strategy for the interaction between EVs and the grid to obtain the optimal charging decision to minimize the peak-to-valley difference between the user-side economy and the grid-side load. The results show that the proposed EV-grid interaction optimization strategy can effectively reduce the peak-to-valley difference in charging station load and realize the efficient cooperative interaction of the “vehicle-station-grid.”