Electric Vehicle Charging Facilities Research Articles

Multi-Encoder Spatio-Temporal Feature Fusion Network for Electric Vehicle Charging Load Prediction

Electric vehicles (EVs) have been initiated as a preference for decarbonizing road transport. Accurate charging load prediction is essential for the construction of EV charging facilities systematically and for the coordination of EV energy demand with the requisite peak power supply. It is noted that the charging load of EVs exhibits high complexity and randomness due to temporal and spatial uncertainties. Therefore, this paper proposes a SEDformer-based charging road prediction method to capture the spatio-temporal characteristics of charging load data. As a deep learning model, SEDformer comprises multiple encoders and a single decoder. In particular, the proposed model includes a Temporal Encoder Block based on the self-attention mechanism and a Spatial Encoder Block based on the channel attention mechanism with sequence decomposition, followed by an aggregated decoder for information fusion. It is shown that the proposed method outperforms various baseline models on a real-world dataset from Palo Alto, U.S., demonstrating its superiority in addressing spatio-temporal data-driven load forecasting problems.

Subsidizing residents or companies? An equilibrium-based analysis of subsidy strategies for EV charging facilities

This study examines the charging location choice behavior of residents in relation to employer-provided charging facilities and investigates the impact of different government subsidy strategies for charging facility construction on urban spatial structure and traffic-related air pollution. Using a monocentric two-zone city model, we analyze the residential distribution and travel mode choice of urban residents under various subsidy strategies from the perspective of social planners. The government can optimize urban resource allocation through transport infrastructure investments and implementation of emission taxes to maximize the so-called residents’ utility. We observe that despite the distorting effect of employer-provided charging facilities on travel mode choice, residents still experience an enhanced utility due to the positive environmental impact resulting from reduced pollution levels associated with electric vehicles (EVs) compared to gasoline vehicles (GVs). We provide numerical evidence to show that government subsidies, whether directed towards companies or residents, can effectively enhance the adoption of EVs and improve utility. In low-density cities, it is advisable for the government to encourage and subsidize residents in building charging facilities. Conversely, in high-density cities, prioritizing subsidies for companies would be more beneficial in improving utility and expanding the coverage of charging facilities. Furthermore, in cities where resident environmental awareness is relatively low, it is recommended that the government increase subsidies to promote EV adoption.

Matching analysis of new energy vehicle charging demand and charging infrastructure power supply capacity: A case study of China's capital Beijing

The random charging behavior of new energy vehicles (NEVs) will bring new challenges to the matching between electric vehicle charging facilities (EVCF) and NEVs. In order to explore whether the power supply capacity of urban EVCF can meet the charging requirements of NEVs, using the progression of NEVs in Beijing as a basis, initially, the Monte Carlo simulation (MCS) approach simulates the power demand trajectory for NEVs in the region. Subsequently, to forecast the ownership trends of NEVs across three distinct scenarios from 2021 to 2030, the study employs Grey correlation analysis combined with the bidirectional long short-term memory model (GRA-BiLSTM), facilitating the determination of NEVs’ charging needs. Second, the charging supply of EVCF for the next 10 years is derived by analyzing different development scenarios with three growth rates of EVCF and three combinations of fast and slow pile ratios. The findings indicate a discrepancy between the rate of increase in ownership of NEVs and the rate of increase in charging infrastructure in Beijing between 2021 and 2030. Even under a scenario of high growth in NEV ownership, the balance between supply and demand for charging capacity is not achieved, resulting in suboptimal utilization of charging infrastructure.

Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm

Abstract Battery electric vehicles have been developing rapidly in recent years due to their advantages of energy saving and emission reduction. However, the development of battery electric vehicles is still in its infancy due to the relatively immature charging technology and the lack of infrastructure development. In many researches, there have been many solutions to adopt remote monitoring systems in battery electric vehicles to monitor and collect them in real time for statistical analysis and fault diagnosis. However, the current monitoring systems are carried out statistically and it is difficult to extract much valuable information from the data. In this paper, after analyzing the construction mode of battery electric vehicle charging facilities, the battery electric vehicle charging facility system platform using embedded system was analyzed. Several functions were simulated to meet the problems encountered by users in using it, studying the battery electric vehicle charging facility layout optimization model based on data mining algorithm. Focusing on the coverage problem in the siting problem, the State-of-Charge (SOC) of the battery charge state was analyzed. Taking residential areas as an example and 4 weeks as a cycle, it was found that during the first to fourth weeks, the negative power at 1:00, 20:00 and 24:00 was higher. At the same type of time point, different charging thresholds had a significant impact on the charging load demand of users. The larger the charging threshold of battery electric vehicles appeared, the earlier the peak moment and the higher the peak load. The battery state of the battery electric vehicle in the work area is 60 % at 1:00, and it reaches a relatively full state of 90 % at 8:00. In different regions, the allocation of charging thresholds for battery electric vehicles was different, and the Markov theory was derived from the analysis of the coverage problem to enable fast and effective siting of charging piles while satisfying charging convenience and high utilization of charging facilities to complement the research on the optimization model of battery electric vehicle charging facility layout.

Rural Photovoltaic Storage and Charging Integrated Charging Station Capacity Allocation Strategy based on Tariff Compensation Mechanism

Background: In order to help the carbon peaking and carbon neutrality goals, the current new energy vehicle to the countryside policy for the local use of renewable energy and demand-side carbon reduction provides a good opportunity but also requires rural townships and villages of electric vehicle charging infrastructure planning ahead. However, due to the current low rural electric vehicle ownership, the charging price compensation mechanism is not yet perfect, resulting in the planning period of electric vehicle growth and the willingness to respond to the tariff compensation policy is difficult to accurately assess. Methods: This paper proposes a rural photovoltaic storage and charging integrated charging station capacity allocation strategy based on the tariff compensation mechanism. Firstly, we construct a spatial-temporal dynamic distribution model of rural EV charging load coupled with distribution network-transportation network, and on this basis, we consider the rural EV charging time-sharing tariff and tariff compensation policy incentive, and amend the EV charging load transfer model; and then we construct an optimization planning model of charging station with the goal of minimizing the cost of construction, operation and maintenance, and maximizing the charging benefit of the integrated charging station in the rural area, and obtain the optimal synergistic planning scheme under the tariff compensation mechanism in the planning period. Results: The optimal collaborative planning scheme under the electricity price compensation mechanism is obtained, and the correctness and validity of the proposed optimal planning method of the rural optical storage charging station under the electricity price compensation mechanism is verified by the example, which is of positive significance in the promotion of the charging facilities to go to the countryside in an appropriate manner, and in the stimulation of the willingness of the rural consumers in the townships to purchase vehicles. Conclusion: (1) The spatial and temporal distribution and transfer model of rural EV charging loads with price incentives is constructed by taking into account the preferential charging price policies such as rural time-sharing tariff and tariff compensation mechanism. By comparing the operating revenues of optical storage-charging integrated charging stations with and without timesharing tariffs and tariff compensation policies, we verified the incentive effect of multiple types of price incentives for the over-planning of rural electric vehicle charging facilities. (2) The proposed optimal configuration method of rural photovoltaic, storage and charging integration charging station can realize the in-situ utilization of rural renewable energy, tap the price competitiveness of photovoltaic, storage and charging integration, and weaken the cost of electricity consumption. By comparing the optimized configuration scheme with and without joint planning, it is verified that the moderate configuration of in-situ photovoltaic and energy storage equipment on the basis of the planning of charging piles brings benefits far exceeding the investment cost, and has a great role in increasing the operational efficiency of rural charging facilities.

Policy analysis of electric vehicle infrastructure supporting sustainable energy use

The Presidential Regulation of the Republic of Indonesia 55 of 2019 concerning the Acceleration of the Battery-Based Electric Vehicle (EV) Program for transportation aims to provide direction, foundation, and legal certainty in the implementation of the acceleration of the battery-based electric motor vehicle program in Indonesia, one of which is the strategy of providing electric charging infrastructure. This policy had not fully significant impact on the provision of electric vehicle charging infrastructure, wherein 2021, the provision of EV charging facilities has reached approximately only 74 charging facilities, spread across 188 locations; this condition is still far from the target set by the government, expected about 168 charging facilities units in 2021-2031. The research evaluates existing policies as a reference for preparing policy recommendations to accelerate the provision of EV charging infrastructure. Research has shown that the adoption of electric vehicles is constrained, one of which is the need for more charging station availability of charging stations and their distribution. The government needs to provide incentives for domestic manufacturers of charging stations and battery swapping stations and instruct state-owned enterprises to accelerate the adoption of electric to provide charging station infrastructure.

An evaluation model for smart grids in support of smart cities based on the Hierarchy of Needs Theory

Smart cities depend highly on an intelligent electrical networks to provide a reliable, safe, and clean power supplies. A smart grid achieves such aforementioned power supply by ensuring resilient energy delivery, which presents opportunities to improve the cost-effectiveness of power supply and minimize environmental impacts. A systematic evaluation of the comprehensive benefits brought by smart grid to smart cities can provide necessary theoretical fundamentals for urban planning and construction towards a sustainable energy future. However, most of the present methods of assessing smart cities do not fully take into account the benefits expected from the smart grid. To comprehensively evaluate the development levels of smart cities while revealing the supporting roles of smart grids, this article proposes a model of smart city development needs from the perspective of residents’ needs based on Maslow’s Hierarchy of Needs theory, which serves the primary purpose of building a smart city. By classifying and reintegrating the needs, an evaluation index system of smart grids supporting smart cities was further constructed. A case analysis concluded that smart grids, as an essential foundation and objective requirement for smart cities, are important in promoting scientific urban management, intelligent infrastructure, refined public services, efficient energy utilization, and industrial development and modernization. Further optimization suggestions were given to the city analyzed in the case include strengthening urban management and infrastructure constructions, such as electric vehicle charging facilities and wireless coverage.

A study of the impact of voltage sag on sensitive loads at low-voltage distribution areas

Under the background of the dual carbon goal and the new power system, the power system becomes more complex and diverse, and voltage sag events caused by short-circuit faults, overloads, switching operations, large motor starting, and distributed generation access are more frequent. On the other hand, with the increasing sensitivity of low-voltage distribution network electrical equipment to power quality, especially for precision process control equipment using computers, power electronics, and automation technology, new energy generating units, and electric vehicle charging facilities, voltage sag accidents will cause difficult-to-estimate losses in equipment production and operation, and frequent voltage sag events have become one of the main power quality problems affecting reliable power supply and normal equipment operation. To address this problem, this paper analyzes the impact of voltage sag on low-voltage substation loads from a mechanism perspective, establishes a voltage sag tolerance curve for electronic appliances through experimental research, and simulates and verifies the effectiveness and correctness of the method in this paper using a 3 MW-rated induction generator connected to the distribution network as an example.

Accelerating Two-Phase Multiobjective Evolutionary Algorithm for Electric Location-Routing Problems

Electric location-routing problem is a challenging problem consisting of the optimization of electric vehicle routing and charging facility location, simultaneously. Existing algorithms generally adopt the two-phase search strategy to alternately optimize the routing and the location. However, they are usually criticized for the inefficiency as the problem scale increases. In order to improve the search efficiency in each phase, we propose an accelerating two-phase multiobjective evolutionary algorithm, where the learning method is used to mine the useful information from the historical search process to generate the high-quality routing and location offspring. To be specific, in the routing optimization phase, an interpolation method is developed to extract the frequent visiting orders existing in the historical best solutions. These frequent visiting orders are used to create potential routing offspring that can accelerate the convergence toward the optimal solutions. In the location optimization phase, a surrogate model is used to approximatively represent the relationship from routing to location, which can directly output a promising location scheme for a given routing offspring and thus reduce the optimization time. Experimental results on different scales of test instances demonstrate the competitiveness of the proposed algorithm in comparison with several state of-the-art algorithms, including four widely used heuristic algorithms and two multiobjective evolutionary algorithms.

Low-Carbon Charging Facilities Planning for Electric Vehicles Based on a Novel Travel Route Choice Model

Promotion of charging facilities (CFs) can ameliorate range anxiety and facilitate long-distance travel by electric vehicles (EVs). In this context, a multistage low-carbon EV CFs planning model is proposed in this paper for the coupled transportation and power systems. This model not only takes into account the construction costs of newly-built CFs and their adverse impacts on the distribution system, but also considers the carbon emissions of CFs and the penalty due to the inconvenience for EVs to be recharged. In addition, a rational travel route choice model is significantly important to accurately evaluate the service capability of CFs to be constructed and thereby to obtain an optimal CF planning result. Therefore, a novel travel route choice model developed in this work allows EV drivers to take detours according to the CF locations to charge their EVs multiple times and then finish their trips. Carbon emission flow (CEF) model is innovatively employed to precisely calculate CFs’ carbon emission amount from the perspective of consumption side. Subsequently, uncertainties involved in CF planning, i.e., distribution and growth rate of traffic flow and electric load, popularity of different types of EVs, as well as location of the connected node of clean electricity, are fully considered to obtain a robust CF planning scheme that can achieve a good performance in the current stage and exhibit robustness for the uncertainties in the future stage. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model. The impacts of future carbon price and EV cruising range on the planning results are also comprehensively evaluated.

Feasibility analysis of solar PV system in presence of EV charging with transactive energy management for a community-based residential system

In this world, with enhancing environmental issues of greenhouses and fossil fuels-based systems in societies and assumptions of sustainable solutions includes Renewable Energy Technologies (RET) and Electric Vehicles (EV) are increased in the residential and commercial buildings. Moreover, Renewable Energy Resources (RES) have an exceptional milestone towards the world to supply energy from the energy market to the consumers without any scarcity and the EV charging facilities among the residential building is the most eminent research domain in the developing countries. This paper investigates the feasibility of solar energy for around 320 buildings include residential load with EV charging is considered for the selected location Vistara Residential Enclave at Madurai. Additionally, the optimal solution is performed using HOMER software for the proposed solar Photo Voltaic (PV), Battery Energy Storage System (BESS), grid extension, V2H process for increase the efficient energy management. Instead of, three scenarios are considered for analyse the technical, economic and environmental basis. Moreover, in the first one, off-grid/battery support system for only residential load using solar energy. Then, the second scenario is evaluated with on-grid/battery backup system by solar PV for household loads only. Finally, in the third scenario, on-grid/battery system for residential load as well as EV charging with considered V2H performance. Meanwhile, the multiple houses are confined to a residential site in the consideration of overall load system as well as divided into five zones. Moreover, the following these three scenarios cost and electrical parameters are predicted as with and without Transactive Energy Management System (TEMS) to overcome the peak load challenges. Therefore, the consequences of this study proposed that 36.90 % of Net Present Cost (NPC) is maximized and 51.87 % reduced Operating and Maintenance cost (O&M) between presence and absence of TEMS shows that overall system load with TEMS towards the localized grid is the best effective reliable system in an effective manner than five divided zones without TEMS.

The Influence of Passenger Car Banning Policies on Modal Shifts: Rotterdam’s Case Study

Low-emission zones (LEZs), incentivizing electric cars, park-and-ride systems, and other traffic reduction schemes, are all single measures aimed at achieving low-/zero-emission mobility. This paper aims to investigate the impact of LEZs’ passenger car banning and argues that such single measures cannot achieve significant traffic or emission reduction levels without being integrated into a well-designed policy package that ensures the sufficient provision of mobility alternatives. Featuring Rotterdam as a single case study, this paper follows a mixed methodology consisting of (1) quantitative real-time data on transport usage that mirror the users’ behavior and (2) qualitative data acquired from in-depth interviews and documents that explain the government’s intention and the users’ behavior. The results show that between 2016 and 2020, after applying the LEZ policy for restricting passenger cars and vans of Euro 3 and lower, there was a 50% decrease in the number of polluting passenger cars and vans entering Rotterdam. However, this decrease is insignificant, since the number of vehicles targeted by the policy was less than 2% of the overall vehicles that entered the LEZ area. The results also shed light on the role of systematic policy packaging in ensuring a change in user behavior. Among other initiatives, a successful LEZ implantation should be supported by inner-city parking reduction, electric vehicle charging facilities, incentive schemes, and alternative sustainable mobility options. Above all, the number of restricted vehicles must be significant. The conclusion and discussion develop a well-structured, educational, evaluative framework and recommend a comprehensive package of policy measures for cities seeking low-emission mobility. The research, however, did not consider the impact of different land-use distributions on the application of the LEZ, which can be an interesting angle for future researchers.

Electric Vehicle Charging Facility Configuration Method for Office Buildings

With the advent of advanced battery technology, EVs are gradually gaining momentum. An appropriate decision-making method for the number of charging piles is in need to meet charging needs, and concurrently, to avoid the waste of infrastructure investment. In this study, an optimal charging pile configuration method for office building parking lots is proposed. With the determination of the design period of charging facilities, a charging load prediction model is established under a collection of charging scenarios. Taking the average utilization rate of charging facilities and the average satisfaction rate of charging demand as the objective functions, the distribution of the optimal number of piles is obtained with the genetic algorithm. The benefits of the configuration method are also explored under the building demand response process. The results show that the optimal configuration of charging piles in office buildings with different volumes have similar characteristics. When the design period is 5 years and 10 years, the comprehensive indicator of the utilization rate of the charging facilities and the satisfaction rate of the charging demand can, respectively, be improved by 8.18% and 17.45%. Moreover, the reasonable scheduling strategy can realize the load regulation response with a maximum load transfer rate of 25.55%.

Evaluation of electric vehicle charging facilities by using the MCDM method

As one of the candidate carriers of future transportation, electric vehicles are widely considered an important choice to solve traffic derivative problems and environmental issues. In this paper, the evaluation of electric vehicle charging facilities is studied by employing the MCDM method. The fuzzy BWM was utilized to weight the criteria, and the MARCOS method was employed to evaluate different electric vehicle charging facilities. Eight criteria for electric vehicle charging facilities were determined, and five electric vehicle charging facilities were evaluated by using the hybrid MCDM method. The empirical results indicate that the comprehensive performance of electric vehicle charging facility EVCF#5 is best, followed by EES#3, EES#2, and EES#1, while the electric vehicle charging facility EES#4 has the worst comprehensive performance. This research can provide references for the sustainable development of electric vehicle charging facilities.

아파트 비상 동력 변압기 사용량 조사를 통한 전기차 충전시설 증설 대응의 연구

The government has mandated the installation of electric vehicle (EV) charging facilities which should have more than 2 % area in apartment buildings. As the capacity of charging facilities increases, which was not considered in the design, it is necessary to check the use of emergency power transformers that supply power to EV charging facilities for stable operation. In this paper, we confirmed the average and maximum power consumption for an hour, day, and month by investigating the load of emergency power transformers in Guri Galmea Complex 1 and 2. We compared the time limit point of transformer capacity in an apartment complex with other studies. In addition, we reviewed the difference between designed power capacity and actual usage. We expect that this research will contribute to the expansion of EV charging facilities in apartments by comparing the power consumption between the investigated apartment and other apartments to confirm the similarity of the power consumption.

A Game-Theoretic Approach to Solve Competition between Multi-Type Electric Vehicle Charging and Parking Facilities

This paper investigates the competition problem between electric vehicle charging and parking desks for different owners using a non-cooperative Bertrand game. There is growing attention on electric vehicles from both policy makers and the public charging service provider, as well as the electric vehicle owners. The interaction between different entities forms a competition (game), especially between multi-type electric vehicle charging and parking facilities. Most of the existing studies on charging platforms are about the optimization of the charging platform scheduling strategy or the game relationship between charging platforms and EV users, but there is a lack of exploration on the revenue game between charging platforms. In this paper, the competitive interactions between different charging decks are studied and analyzed using a general game-theoretic framework, specifically the Nikaido–Isoda solution. In the pricing competition model, the pricing strategies of all players and physical constraints, such as distribution line capacity, are taken into consideration. Through the case studies, it is clearly indicated that the game played between different electric vehicle charging/parking decks will always converge to a Nash equilibrium point. Both charging service providers and customers could benefit from such an open and fully competitive energy service ecosystem, which enhances the overall social welfare.

Value of different electric vehicle charging facility types under different availability situations: A South Korean case study of electric vehicle and internal combustion engine vehicle owners

The availability of charging facilities plays a critical role in electric vehicle (EV) adoption; however, as EVs continue to diffuse and the initial lack of charging availability is improving in some countries, governments must consider additional charging facility rollout policies as EVs can be charged through various types of facilities, including those at households, workplaces, and public areas. Previous studies mainly utilized real-world charging data or surveys conducted solely on EV owners for investigating these aspects. However, the opinion of conventional internal combustion engine vehicle (ICEV) owners is also important when making policy decisions for EV charging facilities. As EVs are expected to positively impact the environment, ICEV owners may also value the rollout of EV charging facilities. Moreover, some ICEV owners are potential future EV owners. Therefore, along with surveying EV owners, this study utilized the contingent valuation method for ICEV owners to estimate how their willingness to pay (valuation) changes under different facility rollout stages (initial and additional) and different types of charging facilities (public and workplace). The results showed that ICEV owners also imposed significant value for EV charging facilities. However, they imposed higher values than EV owners on public chargers during the initial diffusion stage.

Sensitivity to user mischaracterizations in electric vehicle charging

In this paper, we consider electric vehicle charging facilities that offer various levels of service for varying prices such that rational users choose a level of service that minimizes the total cost to themselves including an opportunity cost that incorporates users' value of time. In this setting, we study the sensitivity of the expected occupancy at the facility to mischaracterizations of user profiles and uncharacterized heterogeneity. For user profile mischaracterizations, we first provide a fundamental upper bound for the difference between the expected occupancy under any two different distributions on a user's impatience (i.e., value of time) that only depends on the minimum and maximum charging rate offered by the charging facility. Next, we consider the case when a user's impatience is a discrete random variable and study the sensitivity of the expected occupancy to the probability masses and attained values of the random variable. We show that the expected occupancy varies linearly with respect to the probability masses and is piecewise constant with respect to the attained values. Furthermore, we study the effects on the expected occupancy from the occurrence of heterogeneous user populations. In particular, we quantify the effect on the expected occupancy from the existence of sub-populations that may only select a subset of the offered service levels. Lastly, we quantify the variability of early departures on the expected occupancy. These results demonstrate how the facility operator might design prices such that the expected occupancy does not vary much under small changes in the distribution of a user's impatience, variable and limited user service needs, or uncharacterized early departure, quantities which are generally difficult to characterize accurately from data. We further demonstrate our results via examples.

Dynamic analysis of the investment decision of electric vehicle charging facilities and the promotion effect measurement for electric vehicles

This paper aims to analyze the deep reason why there exists hesitation when investors decide whether invest in EV charging facilities (ECFs). To this end, a series of theoretic models are built and derived, and some enlightening results are got. The main results confirm that charging facility investors are insufficiently motivated to follow a moderately aggressive investment strategy in the early stages of EV development. For stimulating ECFs’ investment, the marginal conditions in which the investors choose active or conservative investment strategies to lay out charging facilities are analyzed, and the effects under different ECFs investment strategies are quantized in terms of driving the market development of EVs. Based on the findings, relevant policy suggestions are proposed. Finally, to verify the gained results, a case study in the context of China is given.

Efficient energy flow criteria of hybrid solar battery packs grid for electric vehicle rapid-charging facility

Rapid growth in electrical transportation and a shift towards clean energy from distributed renewable energy sources are sustainable options to crack down on the demand and usage of electricity. In this paper, grid-assisted electric vehicle (EV) rapid charging facilities empowered by photovoltaic modules and battery stacks are utilised. Further, an effective interactive mechanism between EV and renewable power sources to improve system reliability – 15 efficient modes of charging are employed for the grid-connected charging station. Moreover, for long run of the charging facility, sizing components of the EV charging facilities are optimised by meta-heuristic barebones Harris hawk optimisation with a net profit of US 0.0084/kWh in criteria of no profit/loss. This is also confirmed by the swarm-style memetic grasshopper optimisation algorithm and multifarious particle swarm optimisation.