A Study of the Analytical Method for the Location Planning of Charging Stations for Electric Vehicles (original) (raw)
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Optimal planning of charging stations and electric vehicles traffic assignment: a bi-level approach
IFAC-PapersOnLine, 2020
A new bi-level approach is proposed for the location and sizing of charging stations, considering both the transportation and energy demands. The lower level considers the User Equilibrium traffic assignment conditions for Electric Vehicles (EVs) which are derived and inserted as constraints in the overall optimization problem. The higher level presents the formalization of an optimization problem for the optimal planning of locations, sizes and unit prices of a set of new charging stations in a territory characterized by the presence of an already existing set of charging stations. A case study in the Genoa Municipality is considered for the application of the proposed model.
Optimal Electric Vehicle Charging Station Placement
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Many countries like Singapore are planning to introduce Electric Vehicles (EVs) to replace traditional vehicles to reduce air pollution and improve energy efficiency. The rapid development of EVs calls for efficient deployment of charging stations both for the convenience of EVs and maintaining the efficiency of the road network. Unfortunately, existing work makes unrealistic assumption on EV drivers' charging behaviors and focus on the limited mobility of EVs. This paper studies the Charging Station PLacement (CSPL) problem, and takes into consideration 1) EV drivers' strategic behaviors to minimize their charging cost, and 2) the mutual impact of EV drivers' strategies on the traffic conditions of the road network and service quality of charging stations. We first formulate the CSPL problem as a bilevel optimization problem, which is subsequently converted to a single-level optimization problem by exploiting structures of the EV charging game. Properties of CSPL proble...
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Optimal allocation for electric vehicle charging stations using Trip Success Ratio
International Journal of Electrical Power & Energy Systems, 2017
This paper proposes a new model for optimally allocating Plug-in Electric Vehicle (PEV) Charging Stations (CSs) in the network. The model considers Trip Success Ratio (TSR) in order to enhance CS accessibility for PEV drivers. Diversity of usage and different driving habits are considered in the presented model, as well as different trip types (In-city, Highway). The allocation model has two stages: modeling TSR to estimate Charging Station Service Range (CSSR), and the CS allocation stage. In the first stage, the service range of charging stations has been estimated using TSR with consideration of the uncertainty of trip distances (In-city, Highway) and the uncertainty in the Remaining Electric Range (RER) of PEVs. The estimated CSSR is utilized in the CS allocation stage in order to optimize the CS location set that covers the network with a certain guaranteed TSR level. The allocation problem has been formulated as the Maximum Covering Location Problem (MCLP) in order to make the optimal decision for allocating CSs in the network.
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This paper explores how to optimally locate public charging stations for electric vehicles on a road network, considering drivers' spontaneous adjustments and interactions of travel and recharging decisions. The proposed approach captures the interdependency of different trips conducted by the same driver by examining the complete tour of the driver. Given the limited driving range and recharging needs of battery electric vehicles, drivers of electric vehicles are assumed to simultaneously determine tour paths and recharging plans to minimize their travel and recharging time while guaranteeing not running out of charge before completing their tours. Moreover, different initial states of charge of batteries and risk-taking attitudes of drivers toward the uncertainty of energy consumption are considered. The resulting multi-class network equilibrium flow pattern is described by a mathematical program, which is solved by an iterative procedure. Based on the proposed equilibrium framework, the charging station location problem is then formulated as a bi-level mathematical program and solved by a genetic-algorithm-based procedure. Numerical examples are presented to demonstrate the models and provide insights on public charging infrastructure deployment and behaviors of electric vehicles.
Energy Informatics, 2021
In the coming years, several transformations in the transport sector are expected, associated with the increase in electric vehicles (EVs). These changes directly impact electrical distribution systems (EDSs), introducing new challenges in their planning and operation. One way to assist in the desired integration of this technology is to allocate EV charging stations (EVCSs). Efforts have been made towards the development of EVCSs, with the ability to recharge the vehicle at a similar time than conventional vehicle filling stations. Besides, EVs can bring environmental benefits by reducing greenhouse gas emissions. However, depending on the energy matrix of the country in which the EVs fleet circulates, there may be indirect emissions of polluting gases. Therefore, the development of this technology must be combined with the growth of renewable generation. Thus, this proposal aims to develop a mathematical model that includes EVs integration in the distribution system. To this end, ...
Optimal Deployment of Electric Vehicles’ Fast-Charging Stations
Journal of Advanced Transportation, 2023
As climate change has become a pressing concern, promoting electric vehicles' (EVs) usage has emerged as a popular response to the pollution caused by fossil-fuel automobiles. Locating charging stations in areas with an expanding charging infrastructure is crucial to the accessibility and future success of EVs. Nonetheless, suitable planning and deployment for EV fast-charging stations is one of the most critical determinants for large-scale EV adoption. Installing charging stations in existing fuel/gas stations in the city may be an efective way to persuade people to adopt EVs. In this paper, we aim to optimally locate a fast-charging station in an existing gas station in the real-world scenario of Aichi Prefecture, Japan. Te purpose is to locate and size fast-charging stations in such ways that drivers can get access to these charging facilities within a rational driving range while considering real-world constraints. Furthermore, we include the investment cost and the EVs users' convenience cost. Tis problem is formulated by fve integer linear programming using a weighted set covering models. Te developed model determines where to locate charging stations as well as how many chargers should be installed in each charging station. Te experimental results demonstrate that an appropriate location scheme can be obtained using the model M 5. A computational experiment identifes the best infrastructure solutions for policymakers to consider in the context of growing environmental policies.
The battery of an electric vehicle (EV) needs to be recharged when it is exhausted. So charging stations must be extensively installed to sufficiently serve a number of electric vehicles, especially in residential areas. Since electric charging stations will be used simultaneously by many EVs, they should be optimally installed in areas of dense traffic for minimum total cost of the fast charging station. Another impact of fast charging station on the electric distribution system is transmission line loss which should be minimized. In this paper, the calculation for number of fast charging stations in a residential area and an optimization model of fast charging station planning is proposed. Ant colony optimization (ACO) is implemented to minimize total cost of fast charging station or transmission line loss in the optimization model subject to traffic and power system security constraints. An IEEE 69-bus system in a residential area is used to verify the proposed technique. The results show that ACO method found the best location of fast charging station on residential power distribution with minimum total cost or loss while satisfying many technical and geographical constraints.
Optimization models for placement of an energy-aware electric vehicle charging infrastructure
Transportation Research Part E: Logistics and Transportation Review, 2016
This paper addresses the problem of optimally placing charging stations in urban areas. Two optimization criteria are used: maximizing the number of reachable households and minimizing overall e-transportation energy cost. The decision making models used for both cases are mixed integer programming with linear and nonlinear energy-aware constraints. A multi-objective optimization model that handles both criteria (number of reachable households and transportation energy) simultaneously is also presented. A number of simulation results are provided for two different cities in order to illustrate the proposed methods. Among other insights, these results show that the multi-objective optimization provides improved placement results.
Applied Energy, 2016
h i g h l i g h t s A demand-side approach to the location of charging infrastructure problem is discussed in the paper. The analysis is based on a large data-set of private vehicle travels within the urban area of Rome. Cluster analysis is applied to the data to find the optimal location zones for charging infrastructures. The daily energy demand and the average number of users per day are calculated for each and every charging infrastructure.