Capacity Of Charging Stations Research Articles

Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced, considering the cost-effectiveness and robustness of the bus system from 20 (real-world case) to 12. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge in different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30%, and 10-minute charging is more recommended when the state-of-charge is between 30% and 70%.

Optimization of Charging Station Capacity Based on Energy Storage Scheduling and Bi-Level Planning Model

With the government’s strong promotion of the transformation of new and old driving forces, the electrification of buses has developed rapidly. In order to improve resource utilization, many cities have decided to open bus charging stations (CSs) to private vehicles, thus leading to the problems of high electricity costs, long waiting times, and increased grid load during peak hours. To address these issues, a dual-layer optimization model was constructed and solved using the Golden Sine Algorithm, balancing the construction cost of CSs and user costs. In addition, the problem was alleviated by combining energy storage scheduling and the M/M/c queue model to reduce grid pressure and shorten waiting times. The study shows that energy storage scheduling effectively reduces grid load, and the electricity cost is reduced by 6.0007%. The average waiting time is reduced to 2.1 min through the queue model, reducing the electric vehicles user’s time cost. The bi-level programming model and energy storage scheduling strategy have positive implications for the operation and development of bus CSs.

Smart City Charging Station allocation for electric vehicles using analytic hierarchy process and multiobjective goal-programming

In view of recent developments in urban transport systems, there has been a significant increase in the presence of electric vehicles on the market. One of the major challenges faced is the effective allocation of these electric vehicles to appropriate charging stations. The aim of this study is to tackle this challenge by leveraging various input parameter models within a smart city context. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. In addition, we propose a novel methodology that combines the analytic hierarchy process with a multi-objective goal programming model to aid decision-making regarding the allocation of electric vehicles–charging stations. The aim of this methodology is to assist electric vehicle owners in selecting effective charging stations within a complex decision-making environment. In addition, practical examples of how these methodologies can be used to solve a real-life problem have been highlighted. We validated the effectiveness of the proposed method in terms of enhancing the satisfaction factor of electric vehicles by updating their energy levels and reducing range anxiety through simulation results using SUMO and GAMS with the BARON solver. These results confirm the efficacy of the proposed method.

Novel energy management options for charging stations of electric vehicles in buildings without increasing peak demand for sustainable cities

Electric vehicles are recognised as a critical step in making the transportation sector more environmentally friendly, especially when powered by renewable energy sources. A major hindrance to their widespread adoption is the scarcity of charging stations and the absence of grid access. This study explores and examines four distinct ways to enhance the energy grid of buildings. The primary goal of these solutions is to generate more capacity without raising the peak electricity load of the building through the incorporation of energy storage technologies. The target location is Southern Ontario, Canada, which is one of most populated regions in Canada. Strategy 3 stands out as the most promising alternative, with an impressive 35 % increase in charging station capacity compared to other strategies. Using this method, it is feasible to store 353.4 GWh of energy during the summer and 480.1 GWh during the winter. In summer, the station can release 243.1 GWh, and in the winter, 345.6 GWh of energy can be efficiently used to charge electric vehicles. Strategy 4, which includes hydrogen generation and fuel cell systems, offers a competitive alternative with a 28 % increase in capacity. This method allows for charging station capabilities of 52 GW during the summer and 13 GW during the winter. The strategies emphasize the importance of adaptable capacity solutions to address variations in demand on a seasonal and daily basis while considering both economic and environmental sustainability. As the adoption of electric vehicles continues to rise, the nuanced operational aspects of these strategies are pivotal for the development of sustainable and efficient transportation ecosystems, ensuring that electric vehicle charging infrastructure keeps pace with the growing demand.

Multiagent Q-Learning Approach for the Recharging Scheduling of Electric Automated Guided Vehicles in Container Terminals

In recent years, advancements in battery technology have led to increased adoption of electric automated guided vehicles in container terminals. Given how critical these vehicles are to terminal operations, this trend requires efficient recharging scheduling for automated guided vehicles, and the main challenges arise from limited charging station capacity and tight vehicle schedules. Motivated by the dynamic nature of the problem, the recharging scheduling problem for an entire vehicle fleet given capacitated stations is formulated as a Markov decision process model. Then, it is solved using a multiagent Q-learning (MAQL) approach to produce a recharging schedule that minimizes the delay of jobs. Numerical experiments show that under a stochastic environment in terms of vehicle travel time, MAQL enables the exploration of better scheduling by coordinating across the entire vehicle fleet and charging facilities and outperforms various benchmark approaches, with an additional improvement of 18.8% on average over the best rule-based heuristic and 5.4% over the predetermined approach. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72101203], the Shaanxi Provincial Key R&D Program, China [Grant 2022KW-02], and the Singapore Maritime Institute [Grant SMI-2017-SP-002]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0113 .

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.

Charging station layout planning for electric vehicles based on power system flexibility requirements

Under the ambitious commitment of reaching carbon neutrality by 2060, China promotes both the deployment of renewable energy and the development of electric vehicles. Renewable fluctuations in the supply side and EV charging burden in the demand side propose higher requirements for the system flexibility. Optimized station layout and charging schedule could coordinate the load curve, provide system flexibility and accommodate the variable renewables. However, previous work only focuses on maximizing the profit of station holders and vehicle owners, lacking the consideration of the broader impact on power system. Here, we propose an EV charging station layout optimization methodology considering not only the EV charging behavior, sequential charging demand, but also its further impact on power system. The station layout and charging schedule are co-optimized with an integrated power system model. Applying the proposed methodology to Jiangxi in 2025, a cumulative charging station capacity of 1412, 1092, and 1415 MW is recommended in workplaces, residences, and shopping centers, respectively. Increased renewable energy integration and decreased thermal power generation are realized, achieving reduced carbon emissions of 800 kilotons. This work provides an effective methodology for slow charging station layout from the perspective of the integrated power system.

Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints

Recent advances in vehicle technology offer new opportunities for an electric, automated, modular bus (MB) unit with an adjustable capacity to be applied to transit systems, promising to tackle the resource allocation challenges of traditional buses in coping with uneven travel demand. Drawing on the concept of modular vehicles, this paper introduces a novel scheduling system in which MB units can be combined/separated from fulfilling imbalanced trip demands through capacity adjustments. We develop an optimization model for determining the optimal formation and trip sequence of MB units. In particular, given that the vehicles are electrically powered, battery range limits and charging plans are considered in the system scheduling process. A column-generation-based heuristic algorithm is designed to efficiently solve this model, with constraints related to travel demand and charging station capacity incorporated into the master problem and the trip sequence for modular units with limited energy solved by the subproblem. Taking real data from transit operations for numerical examples, the proposed model performs well in terms of both algorithmic performance and practical applications. The generated optimal MB dispatching scheme can significantly reduce the operating cost from $1534.31 to $1144.26, a decrease of approximately 25% compared to conventional electric buses. The sensitivity analysis on the MB dispatch cost and battery capacity provides some insights for both the scenario configuration and the battery selection for MB system implementation.

Research on the Planning of Electric Vehicle Fast Charging Stations Considering User Selection Preferences

The global energy and environmental crisis promotes the development of electric vehicles (EVs), and the rational planning of EV fast charging stations is an important influencing factor for their development. In this paper, for the EV fast charging station capacity planning problem, a joint-optimization model for optimal planning of EV fast charging stations and the economic operation of a distribution network is constructed, considering the impact of user preference selection and EV access on the regional distribution network. To address the problems of low efficiency and local convergence found in traditional heuristic optimization algorithms, an improved krill swarm optimization algorithm (CKHA) that introduces chaotic optimization parameters to make the initial population as uniformly distributed as possible is proposed to find the optimal planning scheme for EV fast charging stations. The case results show that the optimal planning model and its solution method are effective.

Cost-oriented optimization of the location and capacity of charging stations for the electric Robotaxi fleet

Large-scale adoption of Robotaxi requires a comprehensive charging infrastructure as a guarantee, but the problems of insufficient capacity, low utilization, and unreasonable deployment of electric vehicle charging stations (EVCSs) are still common. In this context, this study proposed a multi-stage optimization strategy consisting of fleet sizing, charging demand simulation, model construction and solution to achieve the location and capacity optimization of EVCSs for the electric Robotaxi fleet. In stage one, a vehicle shareable network model based on graph theory was developed to determine the minimum Robotaxi fleet size required to adequately meet user travel demand, which was solved using the Hopcroft-Karp algorithm. In stage two, the specific spatio-temporal distribution of fleet charging demand was obtained by Monte Carlo simulation, considering the decision-making characteristics of Robotaxi operation and charging process. In stage three, a charging station location and capacity optimization model was established with the objective of minimizing the comprehensive costs, and an improved particle swarm optimization algorithm applying genetic operators to improve the population diversity was proposed. Finally, the effectiveness of the proposed model and algorithm was analyzed and discussed based on a case study using the real passenger order and geographic data from the city of Chengdu, China.

Decentralized cooperative approach for electric vehicle charging

The proliferation in the deployment of electric vehicles has brought up several challenges in the planning and operation of power systems. The health of power system components and the satisfaction of electric vehicle users are becoming equally important with the increasing presence of electric vehicles on the power grid. The charging strategy is crucial as it plays a significant role in shaping users’ behavior to deal with the adverse effects of electric vehicles on grid components. There is a lack of a proper unified framework for the charging management, which considers both the system perspectives such as transformer overloading, shortage of power, and users perspectives such as satisfaction, charging urgency, and privacy. This paper presents a fully decentralized cooperative approach/strategy for charging electric vehicles. The solution is achieved by developing a decentralized iterative algorithm based on a consensus concept to protect the privacy of EV owners. The charging priority factor is incorporated to address the charging urgency of electric vehicles. Multiple electric vehicles can be charged simultaneously without a central decision-maker or coordinator using the proposed strategy. As a result, electric vehicles fulfill their charging needs by the deadline to maximize user satisfaction while respecting their constraints and limitations imposed by the available charging power and charging station capacity. The convergence of the proposed approach is demonstrated by observing the charging of multiple electric vehicles. The results are benchmarked against the centralized approach to verify the effectiveness of the proposed approach.

Simultaneous placement of electric vehicle charging station and DG units in urban area using novel enhanced antlion optimizer

The electric vehicle market has surged the consideration of charging station requirements in the commercial and residential areas of the urban regions. The addition of charging stations at the existing power network introduces a greater challenge on voltage stability and losses. The effect of the charging station can be addressed through the optimal integration of Distributed Generation (DG) units into the network. The improper placement of DG units can jeopardize the network stability. These issues are addressed by optimal placement of DG units and charging stations in the network to improve voltage, reduce transmission loss and maximize the charging station capacity. Here the objectives are considered as a multi-objective problem and solved using an enhanced Ant-lion optimization algorithm. The proposed method is implemented and tested over IEEE – 33, 69 and 94 radial bus system in MATLAB R2020a version. In IEEE – 33 bus system, the total loss reduction of 67.63% and the minimum voltage of 0.981 is attained with 2909.2 kW of DG and 1770.7 kW of charging station. The voltage stability index is improved to 0.92. The efficacy of the proposed method is evaluated through comparison with existing methods such as Genetic Algorithm (GA) with VRP method, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO). It is evident that the proposed method gives improved performance than other methods.

Data-driven intelligent EV charging operating with limited chargers considering the charging demand forecasting

Coordinated charging scheduling can improve the operating economics of charging stations and reduce the required amount of charging facilities. However, existing optimal scheduling schemes either simplify the charging station capacity modeling when taking into account the traffic uncertainty, or ignore the future charging demands when considering charging capacity limitation. To tackle this issue, a data-driven intelligent EV charging scheduling algorithm is proposed in this paper, by scheduling in response to the time-of-use (TOU) electricity price, the limitation of charging facilities, and detailed charger operating process is also considered. First, based on the neural network algorithm, a charging demand forecasting method is introduced to establish the charging task of the charging station. Then, according to the established task, an optimization model that considers the charging costs, battery degradation, and users’ dissatisfaction comprehensively is proposed. The proposed model is formulated as a mixed-integer nonlinear programming problem, and a corresponding approach for solving the model is also proposed. Finally, the real-time operation process of the proposed scheduling method in the actual charging station is presented. By comparing with the existing methods, better effectiveness and performance of the proposed scheduling method are verified by simulation results.

Energy and Environmental Benefit of Solar Charging System for Charging Auto-Rickshaw in Developing Countries

The energy crisis and environmental impact are the major concern mf the present world. Three-wheeler auto-rickshaw becoming an important passenger transport vehicle in a developing country which are indirectly powered by the grid electricity through the batteries. Such vehicle consumes significant grid energy during charging which increases the load in the national grid and put extra stress on the electrification in line with the environmental impact. This paper investigated the existing facilities for charging auto-rickshaw in Bangladesh and designed a proposed solar charging model as a replacement based on the existing model. Then the energy and environmental benefit were estimated to reflect the significance of the proposed model and contribution in the context of the global energy crisis and environmental impact. The investigation found that the daily energy consumption is 290 kWh for a charging station capacity of 30 auto-rickshaws which is significant. The proposed solar model is designed to meet this demand offset. The environmental analysis showed that the proposed model can offset CO2, CH4, and NOx emissions by 54 tCO2eq, 40 kgCO2eq, and 60 kgCO2eq per year respectively of which the contribution of CO2­ is significant. Further research could be focused on the economic and financial analysis in-depth to promote the proposed model.

Capacity Planning for an Electric Vehicle Charging Station Considering Fuzzy Quality of Service and Multiple Charging Options

Electric vehicles (EVs) have received considerable attention in dealing with severe environmental and energy crises. The capacity planning of public charging stations has been a major factor in facilitating the wide market penetration of EVs. In this paper, we present an optimization model for charging station capacity planning to maximize the fuzzy quality of service (FQoS) considering queuing behavior, blocking reliability, and multiple charging options classified by battery technical specifications. The uncertainty of the EV arrival and service time are taken into account and described as fuzzy numbers characterized by triangular membership functions. Meanwhile, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula> -cuts-based algorithm is proposed to defuzzify the FQoS. Finally, the numerical results illustrate that a more robust plan can be obtained by accounting for FQoS. The contribution of the proposed model allows decision-makers and operators to plan the capacity of charging stations with fuzzy EV arrival rate and service rate and provide a better service for customers with different charging options.

Optimal design of electric vehicle charging stations for commercial premises

Influx of plug-in electric vehicles (PEVs) creates a pressing need for careful charging infrastructure planning. In this paper, the primary goal is to devise a closed-form expression for the PEV charging station capacity problem. Two types of commercial charging stations are considered. The first problem is related to the calculation of the optimal service capacity for charging lots located at workplaces where PEV parking statistics are given as a priori. The second problem, on the other hand, is related to the optimisation of arrival rates for a given station capacity. In the second part, the mathematical models are expanded for the case where multiple charger technologies serve customer demand. This time the goal is to calculate the optimal customer load for each charger type according to its rate. Calculations are carried out for both social and individual optimality cases. Markovian queues are used to model the charging station system to capture the complex interactions between customer load, service waiting times, and electricity cost. The related optimisation problems are solved using convex optimisation methods. Closed-form expressions of station capacity and optimal arrival rates are explicitly derived. Both analytical calculations and discrete-event simulations are carried out and the results show that 60% of the waiting times and 42% of the queue length can be reduced by optimal capacity planning.

Electric vehicle dispatching in smart charging station: a pricing strategy

With the rapid development of electric vehicles (EVs), intelligent charging stations will surely become the focus of development. However, the disordered charging of electric vehicles will cause unbalanced resource utilization. Therefore, it is necessary to study the scheduling problem of electric vehicles in charging stations. In this paper, we propose a new electric vehicle dispatching method to realize balanced utilization of resources and high economic benefits. We model the multi-charging station pricing strategy problem as a non-cooperative continuous strategy game with an integrated objective combining charging cost and charging station capacity. Then the existence of Nash equilibrium is proved, and we combining particle swarm optimization with iterative search method to solve the equilibrium point. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.

Online EV Charging Scheduling With On-Arrival Commitment

The rapid proliferation of electric vehicles has resulted in a drastic increase in the total energy demand of EVs. Given the limited charging rate capacity of charging stations and uncertainty of EV arrivals, the aggregate demand might go beyond the charging station capacity, even with proper scheduling. This paper formulates a social welfare maximization problem for EV charging scheduling with charging capacity constraint. Even though the underlying problem is linear, it is difficult to tackle since the input to the problem, i.e., the charging profile of EVs, reveals in online fashion. We devise charging scheduling algorithms that not only work in the online scenario, but also provide the following two key features: 1) on-arrival commitment; respecting the capacity constraint may hinder fulfilling charging requirement of the deadline-constrained EVs entirely. Therefore, committing a guaranteed charging amount upon arrival of each EV is highly essential; 2) (group)-strategy-proofness as a salient feature to promote EVs to reveal their true type and do not collude with other EVs. Extensive simulations using real traces demonstrate the effectiveness of our online scheduling algorithms as compared to the optimal non-committed offline solution.

Planning of electric vehicle charging station based on queuing theory

EV charging station is a very important link in urban planning, and the design of the position and capacity of charging stations are not only to meet the public demand for traveling, but also to maintain the stability of operation and management in power grid. Considering the non-simultaneity and randomness of the EVs arriving at the charging station, the capacity of EV charging stations is calculated by using queuing theory in this study. This study proposes a model which has considered the EVs' charging requirement, economy, and power grid safety to solve the problem of planning EV charging station. Firstly, determine the location of charging station by traffic satisfaction. Secondly, the EV charging stations' charging capacity is optimized with the minimum cost as the optimization target, with the EV user' waiting time patience limit and the distribution network running safety as the constraint condition. Finally, the service area of charging station is divided by the Voronoi diagram. In this study, a 25_nodes traffic network with 24_nodes distribution network underground is modelled in the MATLAB environment to demonstrate the effectiveness of the proposed method. In addition the authors solve the model by the genetic algorithm in MATLAB.