Placement of EV Charging Stations--Balancing Benefits Among Multiple Entities (original) (raw)
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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.
Optimal Electric Vehicle Charging Station Placement
2015
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...
A game-theory analysis of charging stations selection by EV drivers
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We give a comprehensive, market-oriented look at the networks of electric vehicles (EVs) and their components: vehicles, charging stations, and coalitions of stations. For such a setting, we propose a model in which individual stations, coalitions of stations and vehicles interact in a market revolving around the energy for battery recharge. We start by separately studying (i) how autonomously-operated charging stations form coalitions; (ii) the price policy enacted by such coalitions; (iii) how vehicles select the charging station to use, pursuing a time/price tradeoff. Our main goal is to investigate how equilibrium in such a market can be reached. We also address the issue of computational complexity, showing that, through our model, equilibria can be found in polynomial time.
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.
Optimal locations of electric public charging stations using real world vehicle travel patterns
We propose an optimization model based on vehicle travel patterns to capture public charging demand and select the locations of public charging stations to maximize the amount of vehicle-miles-traveled (VMT) being electrified. The formulated model is applied to Beijing, China as a case study using vehicle trajectory data of 11,880 taxis over a period of three weeks. The mathematical problem is formulated in GAMS modeling environment and Cplex optimizer is used to find the optimal solutions. Formulating mathematical model properly, input data transformation, and Cplex option adjustment are considered for accommodating large-scale data. We show that, compared to the 40 existing public charging stations, the 40 optimal ones selected by the model can increase electrified fleet VMT by 59% and 88% for slow and fast charging, respectively. Charging demand for the taxi fleet concentrates in the inner city. When the total number of charging stations increase, the locations of the optimal stations expand outward from the inner city. While more charging stations increase the electrified fleet VMT, the marginal gain diminishes quickly regardless of charging speed.
Sustainability
The uptake of Electric Vehicles (EVs) is rapidly changing the landscape of urban mobility services. Transportation Network Companies (TNCs) have been following this trend by increasing the number of EVs in their fleets. Recently, major TNCs have explored the prospect of establishing privately owned charging facilities that will enable faster and more economic charging. Given the scale and complexity of TNC operations, such decisions need to consider both the requirements of TNCs and local planning regulations. Therefore, an optimisation approach is presented to model the placement of CSs with the objective of minimising the empty time travelled to the nearest CS for recharging as well as the installation cost. An agent based simulation model has been set in the area of Chicago to derive the recharging spots of the TNC vehicles, and in turn derive the charging demand. A mathematical formulation for the resulting optimisation problem is provided alongside a genetic algorithm that can ...
A Hierarchical Optimization Model for a Network of Electric Vehicle Charging Stations
Energies
Charging station location decisions are a critical element in mainstream adoption of electric vehicles (EVs). The consumer confidence in EVs can be boosted with the deployment of carefully-planned charging infrastructure that can fuel a fair number of trips. The charging station (CS) location problem is complex and differs considerably from the classical facility location literature, as the decision parameters are additionally linked to a relatively longer charging period, battery parameters, and available grid resources. In this study, we propose a three-layered system model of fast charging stations (FCSs). In the first layer, we solve the flow capturing location problem to identify the locations of the charging stations. In the second layer, we use a queuing model and introduce a resource allocation framework to optimally provision the limited grid resources. In the third layer, we consider the battery charging dynamics and develop a station policy to maximize the profit by setting maximum charging levels. The model is evaluated on the Arizona state highway system and North Dakota state network with a gravity data model, and on the City of Raleigh, North Carolina, using real traffic data. The results show that the proposed hierarchical model improves the system performance, as well as the quality of service (QoS), provided to the customers. The proposed model can efficiently assist city planners for CS location selection and system design.
Integrated Decision Making in Electric Vehicle and Charging Station Location Network Design
Volume 2A: 40th Design Automation Conference, 2014
A major barrier in consumer adoption of electric vehicles (EVs) is ‘range anxiety,’ the concern that the vehicle will run out of power at an inopportune time. Range anxiety is caused by the current relatively low electric-only operational range and sparse public charging station infrastructure. Range anxiety may be significantly mitigated if EV manufacturers and charging station operators work in partnership using a cooperative business model to balance EV performance and charging station coverage. This model is in contrast to a sequential decision making model where manufacturers bring new EVs to the market first and charging station operators decide on charging station deployment given EV specifications and market demand. This paper proposes an integrated decision making framework to assess profitability of a cooperative business models based on a multi-disciplinary optimization model that combines marketing, engineering, and operations. This model is demonstrated in a case study ...
Learning EV Placement Factors with Social Welfare and Economic Variation Modeling
2019 North American Power Symposium (NAPS), 2019
The past few years have witnessed significant growth on the possession rate of electric vehicles (EV). Such growth urgently requires well-designed plans on charging station placement for sustainable EV growth. Existing solutions ignore many practical factors and lack a systematic method prioritizing them. Through constructive learning, we propose an urban EV charging station planning method with the deployment of levelized cost. This method incorporates four practical costs, considering the convexification of the constraints, economic parameter variation, and the interconnected electric and transportation networks. To better quantify the charging demand, the nested logit model is deployed. Meanwhile, we relate the public information of house prices with EV growth when assigning the weights. Furthermore, we also design the software that enables EV charging station placement. Numerical results reveal the trade-off in EV charger planning, as well as a promising system-level optimization performance.