Planning Of Charging Stations Research Articles

Bi-level planning of electric vehicle charging station in coupled distribution-transportation networks

To ensure the security of the coupled distribution-transportation networks (CDTN) after a fault occurs, an improved road model and a bi-level planning method are proposed for electric vehicles charging stations (EVCSs). This approach considers the feature of fault propagation in the CDTN. Specifically, the upper-level model identifies the optimal locations and corresponding capacity strategies. In the lower-level model, EV charging routes are designed to reduce the overall journey costs for electric vehicles (EVs). The outputs from the lower-level model, including traffic flow and EV queuing results, are incorporated into the upper-level model for refinement. The proposed bi-level model is iteratively solved, with the optimal sensitivity coefficient serving as the evaluation criterion. Finally, this paper utilizes a CDTN, consisting of a 90-node regional road network and three IEEE 33-node systems, to verify the accuracy and feasibility of the proposed model. The optimal planning results for charging stations within the example area indicate the allocation of 10 stations, with a final total investment of 21.2721 million CNY, which is significantly better than other planning schemes. Moreover, all schemes are verified through N-1 fault simulation to determine the capacity allocation for each charging station.

Low-carbon planning of urban charging stations considering carbon emission evolution characteristics and dynamic demand

As a new generation of transportation, electric vehicles play an important role in carbon-peak targets. The development of electric vehicles needs the support of a charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposed a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with the stochastic impacts by regression on population, affluence, and technology (STIRPAT) model to predict the carbon emission trend and quantified the correlation between the construction speed of a charging station and the evolution characteristics of carbon emission by Pearson’s correlation coefficient. A multi-stage charging station planning model was established, which captures the dynamic characteristics of the charging demand of the transportation network and determines the station deployment scheme with economic and low-carbon benefits on the spatiotemporal scale. The Pareto frontier was solved by using the elitist non-dominated sorting genetic algorithm. The model and solution algorithm were verified by the actual road network in a certain area of Shanghai. The results showed that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities, and reduce the carbon emission of transportation networks.

Joint planning of residential electric vehicle charging station integrated with photovoltaic and energy storage considering demand response and uncertainties

Residential electric vehicle charging station integrated with photovoltaic and energy storage represents a burgeoning paradigm for the advancement of future charging infrastructures. This paper investigates its planning problem considering multiple load demand response and their uncertainties. First, a hybrid time series and Kalman Filter model is proposed for photovoltaic output prediction. Second, an orderly charging model and an incentive scheduling model are developed for electric vehicles to facilitate both price-based and incentive-based demand responses. Third, to address uncertainties in user response behavior, consumer psychology theory is applied to construct fuzzy response models for both charging and residential loads. Finally, a multi-objective capacity allocation model is constructed and optimized from the perspectives of economy, environment and safety. The simulation case studies the impact of different demand response strategies and their uncertainties on the planning results. The findings indicate that implementing multiple demand response strategies significantly increases annual revenue by 295.82 %, while reducing carbon emissions and power fluctuations by 16.48 % and 44.27 %, respectively.

Peak shaving potential and its economic feasibility analysis of V2B mode

Electric vehicles (EVs) as mobile energy-storage devices improve the grid's ability to absorb renewable energy while reducing peak-to-valley load differences. With a focus on smoothing the load curve, this study investigates the peak shaving potential and its economic feasibility analysis of V2B mode. First, based on the virtual microgrid system established in this paper, a Monte Carlo method was used to simulate the driving, charging, and discharging processes of EVs. Second, the effects of the number of charging stations, battery capacity of EVs, and photovoltaic (PV) penetration rate on the peak shaving results were evaluated separately by using the standard deviation of daily loads. Third, the V2B peak shaving strategy and cost composition were explored considering the battery aging costs. Moreover, the economic feasibility and factors influencing the proposed strategy were analyzed. The results indicate that 1) V2B effectively transfers peak power loads between 08:00 h And 20:00 h To low-demand periods at night; 2) as the deployment ratio of charging station increases, the level of load fluctuation decreases significantly. When EV battery capacity is at or above 60 kW h, charging station deployment ratios of 0.43, 0.42, and 0.47 are required for saturated load smoothing in winter, transition period, and summer, respectively; 3) the PV penetration rate for maximizing the collaborative peak shaving capability of V2B and PV power is 30%–40 % and 4) reducing battery costs or extending battery life has the most significant effect on V2B power costs, followed by charging station costs. This study provides a theoretical basis for determining the economic feasibility of charging station planning and provides technical guidance for the rational scheduling of EVs and their friendly interaction with the power grid.

Towards a multimodal charging network: Joint planning of charging stations and battery swapping stations for electrified ride-hailing fleets

This paper considers a multimodal charging network in which charging stations and battery swapping stations are jointly built to support an electric ride-hailing fleet synergistically. Our argument is based on the observation that charging an EV is a time-consuming burden, and battery swapping faces scaling issues due to its deployment costs. However, charging stations are cost-effective, making them ideal for scaling up EV fleets, while battery swapping stations offer quick turnaround and can be deployed in tandem with charging stations to improve fleet utilization and reduce operational costs. To fulfill this vision, we consider a ride-hailing platform that jointly builds charging and battery swapping stations to support an EV fleet. An optimization model is proposed to capture the platform’s planning and operational decisions. In particular, the model incorporates essential components such as elastic passenger demand, spatial charging equilibrium, charging and swapping congestion, etc. The overall problem is formulated as a nonconcave program. Instead of pursuing the globally optimal solution, we establish a tight upper bound through relaxation and decomposition, allowing us to evaluate the solution optimality even in the absence of concavity. Through case studies for Manhattan, New York City, we find that joint planning of charging and battery swapping stations outperforms deploying only one of them, yielding a total profit that is 11.7% higher than swapping-only deployment under a limited budget, and 17.5% higher than charging-only deployment under a sufficient budget. These results underscore the complementary benefit between charging and battery swapping facilities.

Merchant EV charging station expansion planning

The significant expansion of Electric Vehicles (EV) creates an uncertain load demand that may pose many challenges to conventional planning methods, especially at the distribution network level. The development of EV technology requires charging infrastructure, which must be considered in the Distribution System Expansion Planning (DSEP). Due to the presence of EV and the load they impose on the distribution system, the planning of EV charging stations is done simultaneously with the DSEP so that the system operator can properly consider the technical constraints of the network and operational constraints. This paper presents a bi-level model for EV charging station expansion planning. The main purpose of the first level problem is expansion planning of charging stations and distribution system. But the charging stations are mostly built by a merchant investor, and they do so when it is profitable and has a reasonable risk. Therefore, the merchant investor should be encouraged to build a charging station in the planned areas. For this reason, in the second level problem, the profitability of the charging station and its risk are examined. To solve this problem, genetic algorithm (GA) has been used. Finally, to test the proposed model, the simulation was performed on a modified 69-bus IEEE network.

Bilevel planning of electric vehicle charging station and battery swapping station considering real-time uncertainty

The placement of electric vehicle charging stations (EVCS) and battery swapping stations (BSS) is crucial to meet demand while minimizing their impact on the power system network. This article addresses the placement and scheduling of EVCS and BSS within a radial distribution network (RDN) overlaid with a road network. Electric Vehicle Routing Problem (EVRP) and Traffic Assignment Problem (TAP) is introduced to reduce EV energy consumption during trips to EVCS and BSS. A Bi-layer optimization approach is used, with the outer layer minimizing energy loss by allocating EVCS and BSS optimally, and the inner layer optimizing scheduling considering associated costs. Uncertainties related to EV is considered on a real-time trip basis and modelled using 2m-Point estimation method. The problem is solved using Differential Evolution (DE) and Prairie Dog Optimization (PDO) techniques. PDO results in a 9.6% reduction in total energy losses and a 22.5% decrease in total associated costs by optimally placing EVCS at 69, 25,114 and BSS at 19, 84,113 of the RDN compared to DE. Inclusion of 2-m PEM uncertainty increases total energy loss by 8.76% but decreases total associated costs by 6.97% compared to real-time uncertainty cases, enhancing the realism of the results.

Integrating Environmental and Economic Considerations in Charging Station Planning: An Improved Quantum Genetic Algorithm

Integrating Environmental and Economic Considerations in Charging Station Planning: An Improved Quantum Genetic Algorithm

A multi-objective site selection of electric vehicle charging station based on NSGA-II

The planning of charging infrastructure is crucial to developing electric vehicles. Planning for charging stations requires considering several variables, including building costs, charging demand, and coverage levels. It might be advantageous to use a multi-objective optimization method based on the NSGA-II. We need to address the current problems in choosing the location of electric vehicle charging stations. Firstly, urban land use is divided into five functional areas, and the TF-IDF algorithm is applied to the division of functional areas. A combined clustering algorithm is proposed to cluster POIs in functional areas into several clusters and determine the cluster centers as charging demand points. We Analyze charging practices and travel patterns of electric car users, fit the charging likelihood of various functional regions, and calculate the charging demand of each charging demand point in the study area. Introduce the NSGA-II algorithm and consider the charging station's progressive coverage to fit the actual area covered by the charging station.Taking the maximization of system benefits and the maximization of the minimum coverage level as the optimization objectives to carry out multi-objective optimization. Finally, we take the charging station planning in the urban area of Hohhot as an example and provide different site selection planning schemes. The planning schemes for different numbers of charging stations are analyzed to obtain a charging station planning scheme that takes into account both objectives.

Robust planning of electric vehicle charging stations considering demand uncertainty and facility failures

Robust planning of electric vehicle charging stations considering demand uncertainty and facility failures

UAV Charging Station Planning and Route Optimization Considering Stochastic Delivery Demand

UAV Charging Station Planning and Route Optimization Considering Stochastic Delivery Demand

Stochastic MILP Model for Merging EV Charging Stations with Active Distribution System Expansion Planning by considering Uncertainties

Radial Power Distribution Networks (PDNs) often suffer from limited reliability, flexibility, and efficiency, leading to service interruptions. Planning for radial PDNs is essential to enhance redundancy resilience, reduce disruptions, and improve overall efficiency. However, traditional PDN planning methods have become obsolete due to the proliferation of Distributed Generation (DG) resources and energy storage systems. Additionally, the rise of Electric Vehicles (EVs) demands sophisticated charging infrastructure planning. This article presents a Mixed-Integer Linear Programming (MILP) model for joint expansion planning of PDN and Electric Vehicle Charging Stations (EVCSs). The model takes into account the construction or reinforcement of substations and circuits, along with the integration of EVs, the installation of DGs, and the placement of capacitor banks, all regarded as traditional conventional expansion options alternatives. To address uncertainties associated with DG generation, conventional loads, and EV demand, our model identifies optimal installation and asset locations. We formulate this as a stochastic scenario-based program with chance constraints for Power Distribution Network Expansion Planning (PDNEP), minimizing investment, operational, and energy loss cost costs over a planning horizon. Through two deterministic and stochastic approaches, encompassing six case studies on an 18-node test system, we evaluate the effectiveness of our model. Results are further validated on a 54-node system, confirming the model’s robustness. Notably, the numerical findings underscore the substantial cost reduction achieved by including EVCSs in the stochastic expansion planning approach, demonstrating its cost-effectiveness. In case study I, where all EVs charge at home during peak hours, it’s the worst case for the PDN. The 54-node system, more complex, demands longer computational time. In the 18-node system, costs improve from 9.97% (case study II) to 3.96% (case study VI) versus the worst-case (case I). In the 54-node system, improvements range from 10.47% (case study II) to 1.40% (case study VI). As a result, In comparative analyses against deterministic and stochastic approaches, our model consistently outperforms in diverse test case studies. The proposed model’s adaptability to address uncertainties underscores its suitability for solving the PDNEP problem in PND.

Bilevel optimization approach to fast charging station planning in electrified transportation networks

Unplanned en-route charging of electric vehicles (EVs) can unexpectedly cripple traffic conditions. As less-effective location of fast charging station (FCS) may exacerbate this issue, the effects of FCS on EVs' charging behaviors should be incorporated at the system planning stage. This paper proposes a strategic-charging-behavior awared model in the context of electrified transportation network environment. This model is formulated into a bilevel mixed-integer programming problem. A newly designed network equilibrium model targets on the low-level problem, which is to model the drivers' charging reaction to a certain FCS layout. In considering the EV self-serving routing and charging behaviors as well as power network constraints, the upper-level problem is formulated for location and sizing decision-makings, of which the objective is to minimize the overall traffic time and investment cost. To handle the proposed bilevel problem, a descent algorithm is further developed. To examine the effectiveness of the proposed approach, extensive numerical experiments are conducted, through which the obtained results demonstrate that the EV charging demand can perfectly be met while traffic congestions can be alleviated to a great extent.

Multi-objective approach for optimized planning of electric vehicle charging stations and distributed energy resources

Multi-objective approach for optimized planning of electric vehicle charging stations and distributed energy resources

Study on optimal configuration of EV charging stations based on second-order cone

The Electric Vehicle(EV) charging station frequently participates in the daily regulation and operation of the power distribution system, whether the installation position is reasonable and the configuration capacity is appropriate, it will directly affect the operation strategy and operating conditions of the distribution system, and then affect the economic and environmental benefits of the distributed power supply and EV load connected to the power grid, based on the analysis of fuel vehicles, EV travel characteristics and electrical geography coupled state, by fitting the charging data of EV with travel chain, more accurate EV charging load can be obtained. Secondly, the optimal allocation model of EV charging stations is established to optimize the grid loss, and the nonconvex problem is solved by combining the second-order cone relaxation technique with the distflow power flow model, the configuration scheme of EV charging station is obtained. Finally, taking an EV charging scene in a certain area as an example, the optimal configuration of the charging station is simulated by setting up two types of charging station: conventional charging station and conventional charging station and fast charging station, the effectiveness of the proposed configuration scheme is verified. The optimal configuration scheme of EV charging station described in this paper provides a theoretical basis for charging station planning and capacity expansion.

Multi objective queue theory based optimal planning of rapid charging stations and distributed generators in coupled transportation and distribution network

AbstractThe environment is adversely affected by greenhouse gas (GHG) emissions from conventional combustion engines. In this regard, electric vehicles (EVs) are a viable transportation option that benefit the environment in reducing GHG emissions. Although the installation of rapid charging stations (RCSs) helps to promote EVs, installing these at improper locations in the distribution network worsens the voltage profile, increases power loss, and energy loss while travelling from EV's current location to RCS. Furthermore, RCS installation cost and waiting time at RCS need to be considered. Therefore, a two‐stage optimal planning is proposed in this article to address the issues stated above. In the first stage, simultaneous optimal planning of RCS and distributed generators is done to minimize active power loss, voltage deviation, EV user cost and to maximize voltage stability index. In the second stage, optimal number of connectors are decided to minimize the installation cost and waiting time in queue at RCS. Here, queuing model is considered to determine the waiting time. A test network of coupled IEEE 33 bus distribution system and transport network is proposed to validate the proposed methodology. Multi objective Rao algorithm (MORA) is used to solve the formulated optimization problems, and results are compared with non dominated sorting genetic algorithm (NSGA‐II) algorithm.

Optimal expansion planning of electric vehicle fast charging stations

With the penetration rate of electric vehicles (EVs) increasing exponentially, the high charging load may cause new issues in future power system, e.g., voltage drop. Besides, EV Charging stations (EVCSs) located in inadequate places exacerbate these issues, causing charging demand to concentrate on a few EVCSs. Therefore, the charging demand estimation for EVCSs and their strategic placement is essential for the system operators and charging station owner. The paper proposes a method for optimal EVCS placement to achieve charging demand dispersion considering not only installation cost but also drivers’ preferences and existing charging stations. To optimally place EVCSs, this work first estimates the charging demand of existing EVCSs based on kernel density estimation. The charging demand for new and existing EVCSs is modeled using the nearest neighbor search to consider drivers’ preference for the nearer station. Next, the EVCS placement problem is formulated to minimize the peak charging demand using integer nonlinear programming, a non-convex problem. To tackle this non-convex problem, a minimax genetic algorithm is proposed, which is genetic algorithm combined with game theory. The validity and effectiveness of the proposed method are demonstrated through simulations based on real data from Jeju Island. After applying the proposed method, new EVCS placement is determined by analyzing the tradeoff between the degree of charging demand dispersion and installation cost. As a result, the charging demand concentrated on a particular EVCS is suitably dispersed.

Optimization of charging stations integrated with dynamic transportation systems in metropolises

The development of electric vehicles (EVs) is expected to play an important role in achieving the emission reduction targets in the transportation sector by many countries and regions, including China’s dual carbon goals. A bottleneck to the mass roll-out of EVs is the limited charging facilities. This paper considers the planning of charging facilities for a new developing area in a metropolis, and an optimization model for charging station planning based on the dynamic transportation system is proposed. The proposed model is developed using an objective framework that considers the spatio-temporal characteristics of EV charging demand in order to minimize the overall cost while the constraints from suppliers and drivers are met. The Voronoi diagram is used to determine the final service boundary of each charging station, and the effect is verified in the planning of charge stations for the Yizhuang new town in Beijing. The case study confirms that the proposed method can optimize the charging facilities that fit well with the traffic network conditions. Furthermore, it is shown that the charging demand varies in accordance with the population density and regional functionality in different areas.

Including research on optimization planning of multi-energy complementary electric vehicle charging station

With the enhancement of environmental protection and the incentive policies issued by governments, the choice of electric vehicles as a means of transportation has gradually become a new mode, so the establishment of a perfect charging station service network is a key link and an critical guarantee for accelerating the adoption of electric vehicles (EVs). In the early stage of EV charging station construction, reasonable and scientific planning is not only related to the convenient travel of users, but also related to the effective utilization of resources. To promote the development of low-carbon economy, this paper first propose a Voronoi diagram based charging station service division approach, followed by a M/G/K queuing model based approach for evaluating the charging capacity of the EV charging station. Finally, a location and volume model is constructed and solved to determine the optimal construction plan of the EV charging station with minimum total cost for the whole society. The experimental results show that the charging station planning method proposed in this paper is economical and effectively improves the charging convenience of electric vehicle users. The reasonable location setting and capacity configuration of charging stations promote the sustainable development of the power grid in the planning area, which has certain practical significance.

Centralized charging station planning for battery electric trucks considering the impacts on electricity distribution systems

To address environmental challenges resulting from transportation sector’s carbon emissions, replacing conventional internal combustion engine vehicles with electric vehicles is a key solution. Comprehensive electrification of heavy-duty trucks can promote the decarbonization of the freight transportation. The mismatch of supporting infrastructures is one of the major barriers to the deployment of battery-electric trucks (BET). BET charging behaviors and vehicle daily operations could influence the charging station planning and electricity distribution systems, which will cause the costs of upgrading distribution grid. This paper studies centralized BET charging station planning, considering the impacts of plug-in fast charging mode on the distribution systems. First, the BET model is proposed to capture BETs charging load profiles based on real-world operating data. Then, a mixed-integer programming model is formulated for charging station planning considering the upgrade requirements for distribution systems based on the BET model. After the co-optimization of centralized charging station planning and charging strategies of BET, the size of charging stations and charging schedules are determined. The results of numerical experiments indicate that charging demand from 50 BET can be met at current charging infrastructure technologies (>60 kW per charger) with a 37% reduction in the upgrade of distribution systems if incorporating managed charging strategy.