scholarly journals Bi-Level Planning Model of Charging Stations Considering the Coupling Relationship between Charging Stations and Travel Route

2018 ◽  
Vol 8 (7) ◽  
pp. 1130 ◽  
Author(s):  
Haixiang Zang ◽  
Yuting Fu ◽  
Ming Chen ◽  
Haiping Shen ◽  
Liheng Miao ◽  
...  

The major factors affecting the popularization of electric vehicles (EV) are the limited travel range and the lack of charging infrastructure. Therefore, to further promote the penetration of EVs, it is of great importance to plan and construct more fast charging stations rationally. In this study, first we establish a travel pattern model based on the Monte Carlo simulation (MCS). Then, with the traveling data of EVs, we build a bi-level planning model of charging stations. For the upper model, with an aim to maximize the travel success ratio, we consider the influence of the placement of charging stations on the user’s travel route. We adopt a hybrid method based on queuing theory and the greedy algorithm to determine the capacity of charging stations, and we utilize the total social cost and satisfaction index as two indicators to evaluate the optimal solutions obtained from the upper model. Additionally, the impact of the increase of EV ownership and slow charger coverage in the public parking lot on the fast charging demands and travel pattern of EV users are also studied. The example verifies the feasibility of the proposed method.

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1577
Author(s):  
Shuang Gao ◽  
Jianzhong Wu ◽  
Bin Xu

A considerable market share of electric vehicles (EVs) is expected in the near future, which leads to a transformation from gas stations to EV charging infrastructure for automobiles. EV charging stations will be integrated with the power grid to replace the fuel consumption at the gas stations for the same mobile needs. In order to evaluate the impact on distribution networks and the controllability of the charging load, the temporal and spatial distribution of the charging power is calculated by establishing mapping the relation between gas stations and charging facilities. Firstly, the arrival and parking period is quantified by applying queuing theory and defining membership function between EVs to parking lots. Secondly, the operational model of charging stations connected to the power distribution network is formulated, and the control variables and their boundaries are identified. Thirdly, an optimal control algorithm is proposed, which combines the configuration of charging stations and charging power regulation during the parking period of each individual EV. A two-stage hybrid optimization algorithm is developed to solve the reliability constrained optimal dispatch problem for EVs, with an EV aggregator installed at each charging station. Simulation results validate the proposed method in evaluating the controllability of EV charging infrastructure and the synergy effects between EV and renewable integration.


Author(s):  
Carola Leone ◽  
Michela Longo

AbstractRoad transport electrification is essential for meeting the European Union's goals of decarbonization and climate change. In this context, an Ultra-Fast Charging (UFC) system is deemed necessary to facilitate the massive penetration of Electric Vehicles (EVs) on the market; particularly as medium-long distance travels are concerned. Anyway, an ultra-fast charging infrastructure represents the most critical point as regards hardware technology, grid-related issues, and financial sustainability. Thus far, this paper presents an impact analysis of a fast-charging station on the grid in terms of power consumption, obtained by the Monte Carlo simulation. Simulation results show that it is not economical convenient size the assumed ultra-fast charging station for the maximum possible power also considering its high impact on the grid. In view of the results obtained from the impact analysis, the last part of the paper focuses on finding a method to reduce the power installed for the DC/DC stage while keeping the possibility for the electric vehicle to charge at their maximum power. To achieve this goal a modular approach is proposed. Finally, two different modular architectures are presented and compared. In both the solutions, the probability of having EVs charging at limited power is less than 5%.


2021 ◽  
Vol 2 (2) ◽  
pp. 1-21
Author(s):  
Hossam ElHussini ◽  
Chadi Assi ◽  
Bassam Moussa ◽  
Ribal Atallah ◽  
Ali Ghrayeb

With the growing market of Electric Vehicles (EV), the procurement of their charging infrastructure plays a crucial role in their adoption. Within the revolution of Internet of Things, the EV charging infrastructure is getting on board with the introduction of smart Electric Vehicle Charging Stations (EVCS), a myriad set of communication protocols, and different entities. We provide in this article an overview of this infrastructure detailing the participating entities and the communication protocols. Further, we contextualize the current deployment of EVCSs through the use of available public data. In the light of such a survey, we identify two key concerns, the lack of standardization and multiple points of failures, which renders the current deployment of EV charging infrastructure vulnerable to an array of different attacks. Moreover, we propose a novel attack scenario that exploits the unique characteristics of the EVCSs and their protocol (such as high power wattage and support for reverse power flow) to cause disturbances to the power grid. We investigate three different attack variations; sudden surge in power demand, sudden surge in power supply, and a switching attack. To support our claims, we showcase using a real-world example how an adversary can compromise an EVCS and create a traffic bottleneck by tampering with the charging schedules of EVs. Further, we perform a simulation-based study of the impact of our proposed attack variations on the WSCC 9 bus system. Our simulations show that an adversary can cause devastating effects on the power grid, which might result in blackout and cascading failure by comprising a small number of EVCSs.


Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1937 ◽  
Author(s):  
Germana Trentadue ◽  
Alexandre Lucas ◽  
Marcos Otura ◽  
Konstantinos Pliakostathis ◽  
Marco Zanni ◽  
...  

Multi-type fast charging stations are being deployed over Europe as electric vehicle adoption becomes more popular. The growth of an electrical charging infrastructure in different countries poses different challenges related to its installation. One of these challenges is related to weather conditions that are extremely heterogeneous due to different latitudes, in which fast charging stations are located and whose impact on the charging performance is often neglected or unknown. The present study focused on the evaluation of the electric vehicle (EV) charging process with fast charging devices (up to 50 kW) at ambient (25 °C) and at extreme temperatures (−25 °C, −15 °C, +40 °C). A sample of seven fast chargers and two electric vehicles (CCS (combined charging system) and CHAdeMO (CHArge de Move)) available on the commercial market was considered in the study. Three phase voltages and currents at the wall socket, where the charger was connected, as well as voltage and current at the plug connection between the charger and vehicle have been recorded. According to SAE (Society of Automotive Engineers) J2894/1, the power conversion efficiency during the charging process has been calculated as the ratio between the instantaneous DC power delivered to the vehicle and the instantaneous AC power supplied from the grid in order to test the performance of the charger. The inverse of the efficiency of the charging process, i.e., a kind of energy return ratio (ERR), has been calculated as the ratio between the AC energy supplied by the grid to the electric vehicle supply equipment (EVSE) and the energy delivered to the vehicle’s battery. The evaluation has shown a varied scenario, confirming the efficiency values declared by the manufacturers at ambient temperature and reporting lower energy efficiencies at extreme temperatures, due to lower requested and, thus, delivered power levels. The lowest and highest power conversion efficiencies of 39% and 93% were observed at −25 °C and ambient temperature (+25 °C), respectively.


2018 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Julia Krause ◽  
Stefan Ladwig ◽  
Lotte Saupp ◽  
Denis Horn ◽  
Alexander Schmidt ◽  
...  

Fast-charging infrastructure with charging time of 20–30 min can help minimizing current perceived limitations of electric vehicles, especially considering the unbalanced and incomprehensive distribution of charging options combined with a long perceived charging time. Positioned on optimal location from user and business perspective, the technology is assumed to help increasing the usage of an electric vehicle (EV). Considering the user perspectives, current and potential EV users were interviewed in two different surveys about optimal fast-charging locations depending on travel purposes and relevant location criteria. The obtained results show that customers prefer to rather charge at origins and destinations than during the trip. For longer distances, charging locations on axes with attractive points of interest are also considered as optimal. From the business model point of view, fast-charging stations at destinations are controversial. The expensive infrastructure and the therefore needed large number of charging sessions are in conflict with the comparatively time consuming stay.


2019 ◽  
Vol 23 (2) ◽  
pp. 9-21
Author(s):  
Aivars Rubenis ◽  
Aigars Laizans ◽  
Andra Zvirbule

Abstract This article presents preliminary analysis of the Latvian national EV fast - charging network after the first year of operation. The first phase of Latvian national EV fast-charging network was launched in 2018 with 70 charging stations on the TEN-T roads and in the largest towns and cities. The article looks at the initial results, both looking at the total capacity utilization for individual charging stations, determining the hourly charging distribution; and to the utilization of the network as a whole. The results present that there is a very large dispersion of the data, most of the charging events happening in a few charging stations in and around the capital of Latvia. However, there have been charging events in all charging stations, even in the most remote ones. Even more skewed distribution was observed analyzing the charging habits of the EV users, with 10 % of users accounting for more than half of the charging events. This should be taken into account when considering applying the results for the future, expecting larger number of electric vehicles in Latvia.


Author(s):  
Gurappa Battapothula ◽  
Chandrasekhar Yammani ◽  
Sydulu Maheswarapu

Abstract Electric vehicles (EVs) load and its charging methodologies play a significant role in distribution system planning. The inaccurate modelling of EV load may overload the distribution system components, increase in Network Power Loss (NPL) and Maximum Voltage Deviation (MVD). The Constant Power (CP) load model is more popularly used to model both the conventional and EV loads in the distribution system. But the CP load modelling cannot provide accurate information of EV charging process. In this paper, the EV load is modelled as constant Impedance-constant Current-constant Power (ZIP), Exponential, Constant Current and Constant Power load models and the conventional loads are modelled as Residential–Industrial–Commercial (RIC) and Constant Power load models. With these EV and conventional load models, the optimal site and size of Fast Charging Stations (FCSs) in the distribution system have been determined. Further, to analyse the impact of load of FCSs in the distribution system, the distribution indices are calculated. The multi-objective hybrid SFL-TLBO algorithm has been used to determine the optimal location and size FCSs with the minimization of NPL, MVD and EV User Cost (EVUC) in the distribution system. To consider the uncertainty of the initial SOC of EVs, the Monte-Carlo simulation technique has been used. These studies have been carried out on 38-bus distribution system and substantiate results are presented.


Author(s):  
Zhaocai Liu ◽  
Ziqi Song ◽  
Yi He

Battery electric buses (BEBs) are increasingly being embraced by transit agencies as an energy-efficient and emission-free alternative to bus fleets. However, because of the limitations of battery technology, BEBs suffer from limited driving range, great battery cost, and time-consuming charging processes. On-route fast charging technology is gaining popularity as a remedy, reducing battery cost, extending driving range, and reducing charging time. With on-route fast charging, BEBs are as capable as their diesel counterparts in relation to range and operating time. However, transit agencies may have the following concerns about on-route fast charging: 1) on-route fast charging stations require massive capital costs; 2) on-route fast charging may lead to high electricity power demand charges; and 3) charging during peak hours may increase electricity energy charges. This study conducts a quantitative economic analysis of on-route fast charging for BEBs, thereby providing some guidelines for transit agencies. An integrated optimization model is first proposed to determine battery size, charger type, and recharging schedule for a general BEB route. Based on the model, an economic analysis of on-route fast charging is then performed on 10 real-world bus routes and a simplified general bus route with different parameters. The results demonstrate that given the current prices of on-route fast charging stations and batteries, it is always beneficial to install on-route fast charging stations for BEBs. A sensitivity analysis is also conducted to show the impact of potential price reductions of batteries.


Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4516 ◽  
Author(s):  
Akhtar Hussain ◽  
Van-Hai Bui ◽  
Ju-Won Baek ◽  
Hak-Man Kim

Optimal sizing of stationary energy storage systems (ESS) is required to reduce the peak load and increase the profit of fast charging stations. Sequential sizing of battery and converter or fixed-size converters are considered in most of the existing studies. However, sequential sizing or fixed-converter sizes may result in under or oversizing of ESS and thus fail to achieve the set targets, such as peak shaving and cost reduction. In order to address these issues, simultaneous sizing of battery and converter is proposed in this study. The proposed method has the ability to avoid the under or oversizing of ESS by considering the converter capacity and battery size as two independence decision variables. A mathematical problem is formulated by considering the stochastic return time of electrical vehicles (EVs), worst-case state of charge at return time, number of registered EVs, charging level of EVs, and other related parameters. The annualized cost of ESS is computed by considering the lifetime of ESS equipment and annual interest rates. The performance of the proposed method is compared with the existing sizing methods for ESS in fast-charging stations. In addition, sensitivity analysis is carried out to analyze the impact of different parameters on the size of the battery and the converter. Simulation results have proved that the proposed method is outperforming the existing sizing methods in terms of the total annual cost of the charging station and the amount of power buying during peak load intervals.


2021 ◽  
Author(s):  
Tran Van Hung

Electric vehicles have become a trend as a replacement to gasoline-powered vehicles and will be a sustainable substitution to conventional vehicles. As the number of electric vehicles in cities increases, the charging demand has surged. The optimal location of the charging station plays an important role in the electric vehicle transit system. This chapter discusses the planning of electric vehicle charging infrastructure for urban. The purpose of this work develops an electric vehicle fast-charging facility planning model by considering battery degradation and vehicle heterogeneity in driving range, and considering various influencing factors such as traffic conditions, user charging costs, daily travel, charging behavior, and distribution network constraints. This work identifies optimal fast-charging stations to minimize the total cost of the transit system for deploying fast-charging networks. Besides, this chapter also analyzes some optimization modeling approach for the fast charging location planning, and point out future research directions.


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