Operation Of Charging Station Research Articles

Design and development of grid independent integrated energy system for electric vehicle charging stations at different locations in Malaysia

Electric vehicles (EVs) have revolutionized the transportation sector as an alternative to conventional fossil fuel-based transportation. It becomes more effective when the charging energy comes from green, renewable, cost-effective, eco-friendly resources. The present work discusses modelling a hybrid renewable energy system for EV charging stations in Malaysia. This work presents techno-economic investigation for different hybrid energy system arrangements of solar photovoltaic (PV), wind turbine (WT), natural gas generator (GS) and battery energy storage (BES) for EV charging station. The EV load demand is calculated based on the transportation fleet of an institution considered for five different climatic locations in Malaysia using HOMER-Pro software. Considering the technical sizing of the hybrid system, the minimization of the energy system and net present cost are discussed for each locations. The optimization study of hybrid energy systems is further examined for social benefits and environmental analysis by including the human development and employment creation index. The comparative analysis of hybrid energy storage systems is performed separately. This study uses sensitivity analysis to elaborate on the cost-effectiveness and technical feasibility of the hybrid system, considering the cost of system components, macroeconomics parameters, and load variation. The finalized results indicate that the solar PV-BES-GS12 hybrid energy system is the most suitable combination for the integrated grid-independent EV charging station operation at each locations.

Charging station service scope division and real-time pricing strategy based on the strongest occupation method

With the rapid construction of charging stations (CSs), charging station operators need to enhance their core competitiveness by precisely planning their service areas and formulating reasonable and effective pricing strategies. However, the regional competition among multiple charging station operators is generally ignored. In the traditional model, the service scope of CSs appears as regular circles, which is inconsistent with the market distribution law. In response to the irregular relationship between the dynamic service scope of CSs and the real-time charging price, a charging station service scope (CSSS) model is proposed by introducing the variable service field strength (SFS). First, the competitiveness of CSs is evaluated quantitatively, and the SFS of CSs is defined to describe the service scope of CSs by the strongest occupation method. Second, the impact of the charging price on the charging demand is analyzed based on the CSSS division model. In addition, the revenue of charging station operators and the stability of the power grid are considered to establish a real-time pricing optimization model. Finally, the numerical simulation is operated in Furong District of Changsha. It is shown that the proposed method effectively achieves more profits for charging stations and decreases the average power deviation of the whole region.

Accelerating a consensus-based EV smart charging algorithm by user priority clustering

The rapid increase in Electric Vehicle (EV) adoption leads to significant challenges for charging station operators due to the unbalance between the growing demand for charging services and their limited power capacity connected to the grid. To tackle the challenge, this paper applies a two-step architecture in which an energy management system (EMS) in the first step determines the available capacity to charge the present fleet of EVs, after which in the second step this capacity is distributed over the individual EVs. To optimally allocate available power and satisfy user requirements, a user priority-based charging management strategy is proposed. Such priority can optimally order the set of EVs to be charged based on user input. However, relying solely on user-estimated input can be prone to incorrect priority due to inaccurate estimations. Therefore, we introduced a clustering of charging sessions based on the user-provided estimated information in the proposed priority function. These clusters relax the effect of inaccurately estimated user input by grouping charging sessions with similar behavior. Our proposed priority can increase user satisfaction by up to 30%, as measured by the percentage of requested energy that is delivered to users, compared to scenarios without priority, 10% more than priority based solely on estimation information, and 8% more than first-come-first-serve. To avoid exposing the personal behavior of the user, we introduce a decentralized approach, applying a consensus-based method that allows local control per EV charging station and only requires minimal communication of a consensus variable with neighboring charging stations. By introducing the priority in the communication weight matrix of the consensus-based algorithm, we accelerate the convergence rate more than 10 times. Simulation results based on real-world data sets in Belgium demonstrate the proposed method scales well with growing fleet sizes.

Dynamic pricing strategy for electric vehicle charging stations to distribute the congestion and maximize the revenue

Electric vehicle (EV) charging station (CS) congestion is highly dependent on the EV owner’s behavior and their selected CS as charging choice. A fixed pricing strategy causes some CSs to be congested with EVs waiting in line to charge, while there are some other CSs with available electric plugs, which adversely affects both charging station operator (CSO) revenue and EV users’ welfare. To solve this problem, this paper presents a dynamic pricing strategy aimed at conducting EVs from congested CSs to the uncongested ones through controlling the charging prices of CSs at different times. The problem is formulated as a scenario-based stochastic optimization with the objective of maximizing overall revenue of the CSO. Moreover, an attraction function model is developed to quantify the charging choice of the EV owners by considering the effective parameters of CSs in EV charging choice decisions. QGIS software is used in this work to formulate a realistic modeling for CS locations and EV routes to calculate the distances from EVs to CSs. Three scenarios are designed to evaluate the performance of the proposed framework and to compare the results with the fixed pricing approach. The results indicate that the proposed dynamic pricing strategy mitigates the congestion of CSs while facilitating an increased number of charged EVs up to 48% as well as increasing the overall revenue of the CSO.

Suitable law-based location selection of high-power electric vehicles charging stations on the TEN-T core network for sustainability: a case of Poland

With the upcoming implementation of the amendment to Regulation (EU) 2019/631 of the European Parliament and of the Council, from 2035 there will be a ban on the registration of new vehicles with internal combustion engines (ICE) in the Member States of the European Union (EU). Consequently, changes in the transportation sector, resulting from the increasing use of electric vehicles, appear to be inevitable. According to the adopted legal acts, the European Union Member States will be obliged to develop, among others, a charging infrastructure and access to public charging stations for electric vehicles. As a result, there will be a need to ensure a significant increase in the power and the number of charging stations and to determine their appropriate location. The article presents the challenges faced by charging station operators and difficulties related to the further development of electric vehicle charging infrastructure in Poland. The still poorly developed public charging infrastructure for electric vehicles, especially in service areas located along the main communication routes, remains the main obstacle to the development of electromobility. In the context of legal, financial, technological, and organizational challenges, the problem of the proper distribution of electric vehicle charging stations along the main communication routes is therefore of particular importance. The aim of the article is to present a new, proprietary method for determining the location of electric vehicle charging stations in Poland within the Trans-European Transport Network (TEN-T), which considers objective location factors: adherence to AFIR requirements, the specificity of the Polish power system and existing parking infrastructure. As a result of using the developed method, a list of 188 recommended locations for the construction of electric vehicle charging stations in Poland along the Trans-European Transport Network (TEN-T) was created. It has been shown in this way that the use of the presented method enables the suitable determination of the location of electric vehicle charging stations along transport routes, considering legal, financial, and technological requirements, which will significantly facilitate the operation of zero-emission transport.

Electric Vehicle Aggregation in a PV-Battery Charging Station: A Contract-Based Approach

As the proportion of high-power fast charging piles increases, how to more stably aggregate electric vehicles to avoid market risks brought about by user charging behavior is an urgent problem for charging station operators. This is especially so for fast charging station operators. To address this issue, a retail package design method for PV-battery charging stations based on contract theory and Lyapunov optimization is proposed in this paper. The operator can formulate optimal charging and discharging packages under asymmetric information in the day-ahead and real-time stages. Packages with fixed power and prices will be offered to users to reduce the impact of user behavior uncertainty. In addition, the queuing problems of pile allocation and PV-battery energy management are resolved by the Lyapunov drift optimization method. The performance indicators, such as the average power and demand processing capability of in-station equipment, will be optimized while ensuring the stability of the queuing network. The simulation verifies that the proposed method can improve the operator's net income and make the social welfare approach the theoretical value under complete information. Moreover, it shows stronger stability in terms of user aggregation.

Blockchain-enabled electric vehicle orderly charging optimization

The increasing prevalence of electric vehicles (EVs) urges charging station operators (CSOs) to expand their networks. However, currently CSOs operate independent EV charging service platforms separately, which poses inconvenience for EV users and hinders the efficient utilization of charging infrastructure. This paper introduces a consortium blockchain-enabled EV charging service platform (CB-EVCSP), which leverages novel orderly charging guidance models (OCGMs) encoded as smart contracts to integrate and coordinate the efforts of CSOs. The CB-EVCSP not only mitigates trust and security concerns but also enhances the utilization of charging facilities. Both multinomial logit (MNL) and mixed MNL models are developed to characterize the interaction between charging station recommendations and EV users’ choice behaviors, where a coefficient of variation based fairness criterion is used to ensure distribution fairness. We provide equivalent reformulations and design efficient constraint generation algorithms. Numerical experiments are conducted to validate the effectiveness of the proposed models and algorithms.

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.

Site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Research on intelligent energy management method of multifunctional fusion electric vehicle charging station based on machine learning

The machine-learning based approach to energy management of multifunctional charging stations that meets the needs in the context of "carbon neutrality". The method takes multifunctional charging station as the research object, considers the uncertainty elements such as market price signal, charging load, and the change of photovoltaic power generation, and aims at maximizing the comprehensive operation efficiency of charging station in the decision cycle, realizes the prediction of power generation and load power by using deep learning method, and obtains the optimal energy management strategy by reinforcement learning, and realizes intelligent management and operation of multifunctional charging station. A typical case study is presented and simulation analysis is carried out for typical cases to verify the correctness and effectiveness of the method. It is shown that the method can effectively cope with the uncertain elements in the operation of multifunctional charging stations, reduce the hardware configuration and manual operation and maintenance costs of charging stations while ensuring safety constraints, and effectively improve the energy utilization efficiency and economy of the whole charging station.

Online Multi-Objective Optimization for Electric Vehicle Charging Station Operation

Online Multi-Objective Optimization for Electric Vehicle Charging Station Operation

Design and Analysis of a Peak Time Estimation Framework for Vehicle Occurrences at Solar Photovoltaic and Grid-Based Battery-Swappable Charging Stations

Due to global environmental impacts, the electric vehicle (EV) adoption rate is increasing. However, unlike conventional petrol vehicles, EVs take a considerable time to charge. EVs on the road with different battery charging statuses and driving demographics may cause uncertain peak time arrivals at charging stations. Battery-swappable charging stations are a quick and easier way to replace uncharged batteries with charged ones. However, charging due to uncertain EV arrival causes higher charging profiles posing load to the grid, management of charged and discharged batteries, and peak time charging tariffs. These challenges hinder the wide operation of battery-swappable charging stations. Nevertheless, a pre-assessment of peak hours using EV demographics can reduce congestion. In recent literature surveys for battery-swappable charging stations, spot congestion has not been given much attention, which has a direct influence on the sizing and operation of battery-swappable charging stations. This research study is focused on estimating peak time events using a novel integrated techno-economic assessment framework. A fuzzy-based parametric assessment tool is developed that identifies the factors that influence higher congestion events. Based on the peak event assessment, grid, and solar PV-based generation is optimized using mixed integer linear programming. In the final step, an environment analysis of a swappable charging station is performed. Furthermore, the results achieved using the proposed framework for battery-swappable charging stations (BSCSs) were compared with fast-charging (FC) stations. FC can economically perform well if integrated with solar PV systems; however, the capital cost is 80% greater than the BSCSs designed under the proposed framework. The operational cost of BSCSs is 39% higher than FC stations as they use 29% higher grid units than FC stations due to night operations under congestion.

EV charging load forecasting model mining algorithm based on hybrid intelligence

Precise EV charging load forecasting plays a critical role in optimizing resource allocation and facilitating economic operation and energy management of EV charging stations, as well as supporting the economic dispatch of the power grid for efficient and effective utilization of resources. Existing EV charging load forecasting models have highly restrictive load data requirements and thus have practical limitations in two fold: first, these models are a black box and cannot provide a quantitative reference for later analysis affecting EV charging load forecasting; and second, they fail in considering the impact of noisy data caused by uncontrollable factors (data collection failures, human errors, and network attacks, etc.) on the accuracy of EV charging load forecasting. To address the above issues, we propose a novel EV charging load forecasting model mining based on gene expression programming (CFMM-GEP) by fusing noisy load processing. This will tackle three-fold ideas: (1) the charging load dataset with abnormal data is reconstructed based on the Auto-Encoder, and the support vector machine-based abnormal detection algorithm is proposed; (2) a quantitative model for EV charging load forecasting based on gene expression programming is constructed. Extensive experiments are carried out on four open-source charging load datasets. Experimental results indicate that the superiority of our proposed CFMM-GEP model over 6 state-of-the-art models in terms of MAPE, RMSE, MAE, and R2.

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.

Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices.

Multi-period planning of locations and capacities of public charging stations

Appropriate charging resources allocation is critical to ensure charging convenience and charging station operation efficiency. However, the temporality of electric vehicle penetration, the development of charging-related technologies, and the randomness of charging behaviors bring highly spatiotemporal dynamics to the charging demands distribution in cities. In this paper, the multi-period and multi-scenario spatiotemporal distribution of charging demands is evaluated based on real-world operation data of electric vehicles in Beijing. A three-period charging stations locations and capacities planning model is proposed to deploy charging stations reasonably based on high-resolution spatiotemporal charging demands distribution at a spatial resolution of 0.46 km side length hexagon units and time resolution of 15 min to satisfy dynamic multi-period charging demands. The model takes minimizing the total costs of charging stations and electric vehicles during all the planning periods as the optimization objective. The capacity-constrained M/M/c/N charging queuing theory combined with the sensitivity analysis and optimization of the charging arrival rate is introduced into the capacity designing process to determine the corresponding charging pile quantity reasonably. Suggestions are given on the charging stations construction locations and the corresponding configurations of types and quantities of charging piles during different planning periods, an actual case study in the Haidian district, Beijing is conducted to validate the proposed planning models.

Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning

This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome this limitation, this paper proposes an evaluation method based on natural language models for assessing the operation of charging stations. By utilizing the proposed SimCSEBERT model, this study analyzes the operational data, user charging data, and basic information of charging stations to predict the operational status and identify influential factors. Additionally, this study compared the evaluation accuracy and impact factor analysis accuracy of the baseline and the proposed model. The experimental results demonstrate that our model achieves a higher evaluation accuracy (operation evaluation accuracy = 0.9464; impact factor analysis accuracy = 0.9492) and effectively assesses the operation of EVCSs. Compared with traditional evaluation methods, this approach exhibits improved universality and a higher level of intelligence. It provides insights into the operation of EVCSs and user demands, allowing for the resolution of supply–demand contradictions that are caused by power supply constraints and the uneven distribution of charging demands. Furthermore, it offers guidance for more efficient and targeted strategies for the operation of charging stations.

Nodal pricing of renewable-oriented distribution network in the presence of EV charging stations: A hybrid robust/stochastic optimization

The ever more increased usage of renewable resources and electric vehicles (EVs) with subsequent usage of charging stations (CSs) create new problems such as pricing and market clearing in their presence. Also, the possible uncertainties lead to huge problems in optimal operation of the distribution grid. The introduced framework in this paper addresses the possible uncertainties and risk conditions in the local market-clearing problem with the high penetration of CSs. The proposed model utilizes power tracing (PT) and loss allocation (LA) in its dynamic pricing scheme. Because of the CSs' role in the market and their different behavior with prosumers, the proposed bi-level model is a single leader and multi follower type. The distribution system operator (DSO) is the upper-level (leader) and the prosumer and CSs are each different lower-level (follower). The hybrid stochastic-robust local market clearing method is used in the proposed model. In the stochastic model, different scenarios for probabilistic parameters such as renewable energy sources (RES), loads, and electric vehicles (EVs) are generated to be used in the simulation. In the robust model, a wide range of errors from expected values are considered, which are used in the robust optimization model to solve the problem. The operation of CSs under stochastic-robust conditions and consideration of the EV owner's utility individually are investigated. The robust model's risk-averse conditions increase the network's prices and reduce electricity buying from the upstream grid. In order to hedge against the uncertainties, the distribution system operation costs only increases by 10.96 % for more 20 % robustness in optimal under-uncertainty operation.

Optimal Scheduling of Electric Vehicles Charging and Discharging Strategy Based on Real‐Time Price Considering Multi‐party Interest

In order to mitigate the negative impact brought by the large‐scale grid connection of EVs, it is highly significant to study the optimal charging scheduling strategy of EVs. In this paper, a multiobjective optimization model of ordered charging and discharging with the participation of distribution network operator (DNO), charging station operator (CSO) and EV users is established, which maximize the interest of three parties. At the same time, the real‐time price (RTP) based on the change in load rate is designed and compared with the time‐of‐use (TOU) price model to analyze the impact of both on the interests of all parties. At last, the sparrow search algorithm (SSA) is introduced and three improvements are made. The results demonstrate that different charging modes of EVs and charging price models will affect the balance of interests of the three parties, while the improved SSA performs better under different users' response rates. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Decentralized Distributionally Robust Coordination Between Distribution System and Charging Station Operators in Unbalanced Distribution Systems

Coordination between the distribution system operator (DSO) and charging station operators (CSOs) is crucial for ensuring stable and economical operation of distribution grids and electric vehicle charging stations (EVCSs). We propose a computationally efficient and privacy-preserving decentralized DSO-CSO coordination framework for robust operation of distribution grids and EVCSs under uncertainties in active unbalanced distribution systems. A centralized model predictive control (MPC) is developed based on distributionally robust optimization (DRO), which characterizes the uncertainties in photovoltaic generation outputs, loads, and electricity prices using the Wasserstein metric. Two scalable optimization approaches are presented to address the high computation time requirement of the proposed centralized DRO-based MPC method through the reduction of constraints: i) a robust optimization method including a distributionally robust bound on the uncertainties, and ii) a bi-level optimization method that determines critical buses associated with voltage violations. Furthermore, to preserve data privacy of electric vehicles, the centralized DRO-based MPC problem is transformed through an alternating direction multiplier method into a decentralized DRO-based MPC problem comprising the DSO and CSO problems. The efficiency and scalability of the proposed DSO-CSO coordination framework are analyzed using the IEEE 37-bus and IEEE 123-bus systems.