Public Electric Vehicle Charging Stations Research Articles

An Analytical Framework for Assessing Equity of Access to Public Electric Vehicle Charging Stations: The Case of Shanghai

With the unprecedented growth of electric vehicles usage, the equitable population-based provision of public charging services has become an important concern in high-density urban centers. To address sustainability concerns, this study explores an analytical framework for assessing the equity of access to public charging services. By comprehensively analyzing factors such as accessibility, the Gini coefficient, the correlation coefficient, and supply–demand matching, we investigated the unequal access to public charging stations within 24 types of sites in central Shanghai. The spatial distribution and accessibility were visualized to illustrate differences in service access. Subsequently, social equity was assessed by considering the population distribution and identifying areas of supply–demand imbalance. The results show that 81% of households share only 10% of public charging services, suggesting a generalized inequality within areas and facilities. Residents of large-scale, low-density, low-grade neighborhoods have difficulties accessing services. Nearly 66.96% of subdistricts have supply and demand conflicts. In addition, priority types of improvement were identified and directions for improvement were suggested, as well as recommendations for the integration of PCSs with exterior built places. We also found significant differences in accessibility and equity at both the district and subdistrict level. The findings of this study will help urban planners assess and locate unequal areas and provide insights and the basis for further expansion into the analysis methods adopted at different stages to achieve sustainable development.

Risk Analysis of Operational Disruptions in Public Electric Vehicle Charging Stations Using the Failure Mode and Effects Analysis (FMEA) Method

The Indonesia government is actively promoting the adoption of electric vehicles, as detailed in the 2021-2030 Electricity Supply Business Plan. The state-owned electricity provider, PLN, is responsible for establishing Public Electric Vehicle Charging Stations (PEVCS). However, several of these stations have encountered malfunctions; notably, 82 of the 567 stations are classified as Unavailable, indicating they are non-functional. Research literature points to a financial loss of $34,000 from operational issues at PEVCSs. This research aims to helps management understand and prioritize disruption that leads to failures or damages at these stations. Method used is the Failure Mode and Effects Analysis (FMEA) method along with logistic regression to examine the disruptions at PEVCSs labeled as Unavailable. The data for this research comes from a six-month historical record of PEVCS disruptions. The variables utilized for logistic regression analysis include foundational variables from the FMEA methodology—Severity, Occurrence, and Disturbance—complemented by two supplementary variables: the speed and age of the PEVCS. Result was found that three out of twelve types of disruptions have a high likelihood of failure, specifically issues with Device Communication, Connectivity, and Emergency Stop functions. A disruption is deemed likely to cause failure if its probability exceeds 50%.

Development of Public Electric Vehicle Charging Stations (SPKLU) in Makassar as an Electric Car Supporting Means

Development of Public Electric Vehicle Charging Stations (SPKLU) in Makassar as an Electric Car Supporting Means

Risk Analysis of Electricity Demand at Public Electric Vehicle Charging Stations (SPKLU): CVaR Model Approach

The electricity demand at Public Electric Vehicle Charging Stations (SPKLUs) exhibits significant volatility, which is driven by several aspects including electricity demand patterns at specific time intervals, load variability, SPKLU capacity, and other related factors. The variability of these swings can present hazards for SPKLU operators in relation to energy administration as well as operational and financial hazards. The objective of this study is to assess the risk associated with energy demand fluctuation at SPKLUs by employing the Conditional Value-at-Risk (CVaR) model technique. CVaR, or Conditional Value at Risk, is a quantitative measure of risk that calculates the predicted loss value in the most unfavorable situation. It is commonly employed to enhance the risk management approach of SPKLUs. The electricity demand at SPKLU exhibits significant volatility, with an average fluctuation of 10.15% and a standard deviation of 49.67%. The CVAR, calculated at -121.19% for a confidence interval of 1%, represents the maximum potential loss that could be experienced during worst-case electrical demand conditions, highlighting the substantial fluctuations in demand. The study initially implemented the CVaR model to analyze power demand at SPKLU, providing novel perspectives on risk reduction for critical infrastructure and proposing unique strategies for managing demand fluctuations in a reliable and efficient manner. The results also offer comprehensive insights into risk exposure and facilitate the formulation of well-informed and strategic risk management plans.

A novel approach to evaluating the accessibility of electric vehicle charging infrastructure via dynamic thresholding in machine learning

The spatial deployment of urban public electric vehicle charging stations (PEVCSs) plays a pivotal role in the widespread adoption of electric vehicles (EVs). However, with the rapid advancements in EV technology and battery capabilities, substantial improvements in both range and charging efficiency have emerged and are expected to continue experiencing sustained growth. This situation underscores the urgent necessity of establishing dynamic metrics to reconsider the existing static charging infrastructure, aiming to ameliorate the current severe spatial imbalances and supply–demand disparities encountered in the deployment of PEVCSs. In this study, we harnessed and analyzed 84,152 sets of authentic data, fine-tuned through geospatial-aggregation technology, and ensured anonymity. Our findings bridged users’ residential and occupational patterns with their charging propensities. Comparing these with the spatial distribution of current charging stations revealed that Beijing and Shenzhen’s infrastructure aligned with the cities' economic, educational, and residential zones, epitomizing a synergy in provisioning. However, certain areas experienced either a demand–supply imbalance or an oversupply. To address these challenges, we introduced the Charging Access Reachability Index (CARI) using machine learning techniques. This dynamic metric serves as a tool for quantifying the effective coverage range of charging facilities. Its adaptive threshold holds potential as a crucial indicator enabling the dynamic transition towards more efficient and resilient charging infrastructure.

Assessing the spatial distributions of public electric vehicle charging stations with emphasis on equity considerations in King County, Washington

This study assessed the availability and accessibility of public electric vehicle (EV) charging stations at the census tract level in King County, Washington, USA. A gravity model was used to measure the accessibility of stations in each tract while considering the competition among tracts for charging infrastructure. Unlike prior studies, we evaluate current distribution from various equity viewpoints, clearly defining these perspectives and investigating their implications for charging distribution. The findings revealed significantly higher accessibility of public stations in areas with a lower share of single-family homes, a higher share of commercial areas, and proximity to major roadways, irrespective of population and EV adoption rates. Moreover, we found that the current distribution is consistent with utilitarian and capabilities-based theories of equity. Spatial tools such as Moran's I and spatial regression were used to analyze the spatial disparity of the proposed accessibility measure, showing the existence of a highly clustered spatial distribution. This study offers valuable insights into integrating equity considerations in assessing accessibility to transportation facilities, especially EV charging stations. Such an approach can facilitate the promotion of EV adoption across diverse groups, a critical factor in achieving sustainable city development goals by mitigating carbon emissions and enhancing mobility.

Coin-Operated Public Electric Vehicle Charging Station with Solar Panels as the Energy Source

Electric transport is becoming increasingly popular as another option to use environmentally friendly energy in the form of transport. To support the development of electric transport, efficient and accessible charging infrastructure is required. Public Electric Vehicle Charging Stations (SPKLU) have become a major part of supporting long-distance travel and reducing the impact of greenhouse gas emissions on the environment. To increase the availability of SPKLUs and expand accessibility for electric vehicle users, this research aims to design an innovative coin-based SPKLU system that also utilizes solar energy through solar panels. The station can have a smaller environmental effect and lower operating costs by employing solar energy as its primary source. The examination included a review of the economy as well. As a result, those in charge, business owners, and communities who want to promote the usage of electric vehicles find it to be a desirable alternative. As a result of this research, it was found that a full charge of a 20Ah battery only requires a coin of Rp 7000

Analysis of PT PLN's Innovative Strategy in Supporting Electric Vehicles in Indonesia

Global transformation towards sustainable mobility, the electric vehicle (EV) industry plays an important role in creating a cleaner and environmentally friendly future with the journey towards sustainable mobility, PT PLN (Persero) plays an important role in supporting the electric vehicle ecosystem in Indonesia. With more than a thousand Public Electric Vehicle Charging Stations (SPKLU) operating by the end of 2023, PT PLN confirms its commitment to accelerate the transition to clean mobility. However, technical challenges, change management, and organizational culture are obstacles that must be overcome. This research method uses a qualitative approach to understand PT PLN's strategy in supporting the electric vehicle ecosystem in Indonesia. PT PLN's strategy in supporting clean mobility includes infrastructure development, strategic partnerships, home charging installation initiatives, and the application of digital technology. Through holistic analysis, it highlights the importance of effective change management and an inclusive organizational culture. The results of this research provide insight into best practices in supporting the electric vehicle ecosystem and provide input for designing more effective policies. By continuing to strengthen infrastructure, develop partnerships, empower communities, technological innovation and good change management, PT PLN can strengthen its role as a leader in clean mobility and contribute to global sustainable development goals.

A two-level charging scheduling method for public electric vehicle charging stations considering heterogeneous demand and nonlinear charging profile

This paper investigates the electric vehicle (EV) charging scheduling problem for public EV charging stations (EVCSs) that can accommodate heterogeneous charging demands, aiming to flatten the aggregate load on the power grid and reduce the peak demand. In contrast to existing works that mainly focus on a single scheduling strategy, a two-level hierarchical charging scheduling method is proposed, which includes an online booking system (OBS) and a pricing-based charging control system (PCCS). Specifically, the implementation of OBS can reduce the service capacity of bookable chargers, whereas PCCS can facilitate the redistribution of charging demand from peak to off-peak time periods. Moreover, the nonlinear charging profile of the battery is incorporated into the model to better reflect the real charging process. A deep reinforcement learning (DRL)-based approach with a discrete Markov decision process (MDP) formulation is designed to find the optimal scheduling solution, where deep Q-network (DQN) and deep deterministic policy gradient (DDPG) are combined to handle the hybrid action space with both discrete and continuous actions. A comprehensive set of experiments is carried out to examine the effectiveness of the developed two-level scheduling scheme. The results indicate that the proposed method improves the scheduling effectiveness compared to single-strategy methods by 6.7% to 49.44%.

Impact Analysis of Public Electric Vehicle Charging Stations on Transformers and Distribution Networks

Public Electric Vehicle Charging Stations (SPKLUs) are critical infrastructure in facilitating the increasingly popular use of electric vehicles. The increasing number of electric vehicles using SPKLU also has an impact on existing transformers and distribution networks. This research uses the Fluke 1748 tool to analyze the impact on transformers and distribution networks. The Fluke 1748 is a tool that can record and analyze electrical parameters such as voltage, current, power, and power factor with high precision. The method used in this analysis involves installing the Fluke 1748 device at strategic points around the SPKLU, transformer, and distribution network. The results of this study are At the public electric vehicle charging station, an analysis has been carried out on the voltage and current where the results of THDV of 2.212% still meet the IEEE 2014 standard and THDi of 4.929% meet the IEEE 2014 standard, the impact caused is that there are losses in kWh sales due to voltage drops or voltage drops of 2250 watts, losses in significant transformers and in the conductor also cause a voltage drop of 2.5% of the nominal voltage.

Analysis Development of Public Electric Vehicle Charging Stations Using On-Grid Solar Power Plants in Indonesia

Analysis Development of Public Electric Vehicle Charging Stations Using On-Grid Solar Power Plants in Indonesia

Planning of fast charging infrastructure for electric vehicles in a distribution system and prediction of dynamic price

The increasing number of electric vehicles (EVs) has led to the need for installing public electric vehicle charging stations (EVCS) to facilitate ease of use and to support users who do not have the option of residential charging. The public electric vehicle charging infrastructures (EVCIs) must be equipped with a good number of EVCSs, with fast charging capability, to accommodate the EV traffic demand, which would otherwise lead to congestion at the charging stations. The location of these fast-charging infrastructures significantly impacts the distribution system (DS). We propose the optimal placement of fast-charging EVCIs at different locations in the distribution system, using multi-objective particle swarm optimization (MOPSO), so that the power loss and voltage deviations are kept at a minimum. Time-series analysis of the DS and EV load variations are performed using MATLAB and OpenDSS. We further analyze the cost benefits of the EVCIs under real-time pricing conditions and employ an autoregressive integrated moving average (ARIMA) model to predict the dynamic price. The simulated test system without any EVCI has a power loss of 164.36 kW and squared voltage deviations of 0.0235 p.u. Using the proposed method, the results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and squared voltage deviations of 0.0182 p.u. in the system. Significant cost benefits for the EVCIs are also achieved, and an R-squared value of dynamic price predictions of 0.9999 is obtained. This would allow the charging station operator to make promotional offers for maximizing utilization and increasing profits.

Multi-Criteria assessment of the user experience at E-Vehicle charging stations in Germany

We propose an assessment framework for the user experience at public electric vehicle charging stations in Germany, which enables a structured analysis from a driver’s perspective. The assessment criteria were derived from 20 interviews with battery electric vehicle drivers and corroborated by existing literature. Seven criteria are considered as particularly relevant: ease of use, green power, comfort, functionality, price, price transparency, and availability. Using Saaty’s Analytic Hierarchy Process approach, the weights assigned to these criteria were determined in an online survey with 108 drivers of battery electric vehicles. The results indicate the particular importance of availability and functionality. The assessment framework includes value scores that indicate the match between specific charging stations and the assessment criteria. The conceptual framework can help to support operators of public charging points and electric mobility providers in the design of their services and products, as well as guide policy-makers.

Business Design Of Public Electric Vehicle Charging Stations For Trans Java Cikampek – Surabaya Toll With The Dynamic System Approach

Government support through Presidential Regulation No. 55 of 2019 concerning the Acceleration of the Battery-Based Electric Motor Vehicle Program for Road Transportation and the Ministry of Energy and Mineral Resources which has issued Ministerial Regulation No. 13 of 2020 concerning the Provision of Electric Charging Infrastructure for Battery-Based Electric Motorized Vehicles. The Ministerial Regulation explains that the government aims to accelerate the battery-based Electric Motorized Vehicle (KBL) program. This regulates the electric charging facility for KBL.The Public Electric Vehicle Charging Station (SPKLU) is a business opportunity that can provide benefits for producers and national business entrepreneurs. This study reviews the business opportunities for public electric vehicle charging stations, especially on the Trans Java Cikampek Surabaya Toll Road, using a dynamic system approach. Several reviews were also discussed which included emission conversions, business schemes, financial analysis, and investment values. In this paper, there isan option for placing the SPKLU on the Trans Java Cikampek – Surabaya 54 rest area as a business opportunity for business actors to invest in SPKLU.Financial analysis regarding the feasibility of SPKLU investment, can provide investment certainty regarding the business model of providing SPKLU with the Ultra Fast Charging Out Door type

Holistic approach for microgrid planning for e-mobility infrastructure under consideration of long-term uncertainty

Integrating renewable energy sources in sectors such as electricity, heat, and transportation has to be planned economically, technologically, and emission-efficient to address global environmental issues. Microgrids appear to be the solution for large-scale renewable energy integration in these sectors. The microgrid components must be optimally planned and operated to prevent high costs, technical issues, and emissions. Existing approaches for optimal microgrid planning and operation in the literature do not include a solution for e-mobility infrastructure development. Consequently, the authors provide a compact new methodology that considers the placement and the stochastic evolution of e-mobility infrastructure. In this new methodology, a retropolation approach to forecast the rise in the number of electric vehicles, a monte-carlo simulation for electric vehicle (EV) charging behaviors, a method for the definition of electric vehicle charging station (EVCS) numbers based on occupancy time, and public EVCS placement based on monte-carlo simulation have been developed. A deterministic optimization strategy for the planning and operation of microgrids is created using the abovementioned methodologies, which additionally consider technical power system issues. As the future development of e-mobility infrastructure has high associated uncertainties, a new stochastic method referred to as the information gap decision method (IGDM) is proposed. This method provides a risk-averse strategy for microgrid planning and operation by including long-term uncertainty related to e-mobility. Finally, the deterministic and stochastic methodologies are combined in a novel holistic approach for microgrid design and operation in terms of cost, emission, and robustness.The proposed method has been tested in a new settlement area in Magdeburg, Germany, under three different EV development scenarios (negative, trend, and positive). EVs are expected to reach 31 percent of the total number of cars in the investigated settlement area. Due to this, three public electric vehicle charging stations will be required in the 2031 trend scenario. Thus, the investigated settlement area requires a total cost of 127,029 €. In association with possible uncertainties, the cost of the microgrid must be raised by 80 percent to gain complete robustness against long-term risks in the development of EVCS.

Cooperative behaviour at public electric vehicle charging stations

Underutilised charging stations can be a bottleneck in the swift transition to electric mobility. This study is the first to research cooperative behaviour at public charging stations as a way to address improved usage of public charging stations. It does so by viewing public charging stations as a common-pool resource and explains cooperative behaviour from an evolutionary perspective. Current behaviour is analysed using a survey (313 useful responses) and an analysis of large dataset (2.1 million charging sessions) on the use of public charging infrastructure in Amsterdam, The Netherlands. In such a way it identifies the potential, drivers and possible obstacles that electric vehicle drivers experience when cooperating with other drivers to optimally make use of existing infrastructure. Results show that the intention to show direct reciprocal charging behaviour is high among the respondents, although this could be limited if the battery did not reach full or sufficient state-of-charge at the moment of the request. Intention to show direct reciprocal behaviour is mediated by kin and network effects.

Layout and optimization of charging piles for new energy electric vehicles – A study on Xi'an urban area

This paper analyzes the current layout of public charging stations within the third ring road of Xi'an central urban area and the daily charging needs of residents, the main problems in the layout of electric vehicle charging stations in the central urban area of Xi'an were found, the differentiated demand analysis of living space charging was carried out, and the location model of electric vehicle public charging station facilities in the central urban area of the city was constructed, the location selection and optimization of electric vehicle public charging stations in the central urban area of Xi'an were studied. At the same time, a reasonable pile configuration was carried out, finally, the layout scheme of electric vehicle public charging stations in the central urban area was formed, the main shortcomings of the current charging pile layout and the factors (demand side) that should be considered in the current and future charging pile layout are concluded, and the layout and optimization of charging piles for clean energy in the future are prospected.

Bi-level programming model approach for electric vehicle charging stations considering user charging costs

• A new bi-level planning model is proposed that integrates the interests of both charging station investors and EV users, where the upper model has the global economic return as the optimization objective, and the lower model has the EV user's satisfaction with the charging service (charging cost) as the optimization objective. The global economic cost is optimized while the service satisfaction of the user is improved. • The time cost, economic cost, and mileage anxiety are considered together to characterize the service satisfaction of EV users. Specifically, the charging queuing time, driving distance, desired charging volume, and actual charging volume are used to reflect the charging cost of EV users and to reflect the autonomy of EV users more accurately, and a combination of fast and slow charging posts are used for planning to meet the needs of different users and grid constraints, to improve the operational efficiency of charging stations and reduce the charging queuing time of EV users. • The conventional method and the bi-level planning method are solved separately for a region of Beijing, and the effectiveness of the proposed method is verified by comparing factors such as investors' returns, users' charging costs, and queuing times. The rapid development of electric vehicles (EV) has placed greater demands on the planning and construction of public electric vehicle charging stations (EVCS). As EV users are highly autonomous in their charging behavior, the interests of investors and EV users are mutually affected and challenging to balance. Therefore, this paper proposes a bi-level planning model to balance the interests of investors and EV users and optimize global economic costs while improving the service satisfaction of users. The upper-level model aims to optimize the economic cost. In contrast, the lower-level model aims to optimize the service satisfaction of EV users and characterizes the charging satisfaction of EV users through the costs of charging queuing time, distance traveled, desired to charge volume, and actual charging volume, to more accurately reflect the autonomy of EV users. A combination of fast and slow charging piles is also used for planning to meet the needs of different users and improve charging stations' operational efficiency. Finally, a case study is conducted in an area of Beijing to verify that the optimization model has the advantages of low global economic cost, short charging queuing time for users, and high service satisfaction.

Impact of Reactive Power from Public Electric Vehicle Stations on Transformer Aging and Active Energy Losses

Climate change at the global level has accelerated the energy transition around the world. With the aim of reducing CO2 emissions, the paradigm of using electric vehicles (EVs) has been globally accepted. The impact of EVs and their integration into the energy system is vital for accepting the increasing number of EVs. Considering the way the modern energy system functions, the role of EVs in the system may vary. A methodology for analyzing the impact of reactive power from public electric vehicle charging stations (EVCSs) on two main indicators of the distribution system is proposed as follows: globally, referring to active power losses, and locally, referring to transformer aging. This paper indicates that there is an optimal value of reactive power coming from EV chargers at EVCSs by which active energy losses and transformer aging are reduced. The proposed methodology is based on relevant models for calculating power flows and transformer aging and appropriately takes into consideration the stochastic nature of EV charging demand.

Simultaneity Factors of Public Electric Vehicle Charging Stations Based on Real-World Occupation Data

Charging of electric vehicles may cause stress on the electricity grid. Grid planners need clarity regarding likely grid loading when creating extensions. In this paper, we analyse the simultaneity factor (SF) or peak power of public electric vehicle charging stations with different recharging strategies. This contribution is the first of its kind in terms of data quantity and, therefore, representativeness. We found that the choice of charging strategy had a massive impact on the electricity grid. The current “naive” charging strategy of plugging in at full power and recharging until the battery is full cause limited stress. Price-optimised recharging strategies, in turn, create high power peaks. The SFs varied by strategy, particularly when using several connectors at once. Compared to the SF of a single connector in naive charging, the SF decreased by approximately 50% for groups of 10 connectors. For a set of 1000 connectors, the SF was between 10% and 20%. Price-optimised strategies showed a much slower decay where, in some cases, groups of 10 connectors still had an SF of 100%. For sets of 1000 connectors, the SF of price-optimised strategies was twice that of the naive strategy. Overall, we found that price optimisation did not reduce electricity purchase costs by much, especially compared to peak-related network expansion costs.