Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging

Xia, Mingchao; Lai, Qingying; Zhong, Yajiao; Li, Canbing; Chiang, Hsiao‐Dong

doi:10.3390/en9030159

Cited by 34 publications

(35 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the charging activities of these users are somewhat adjustable and can be regarded as adjustable charging action (ACA), these users could be considered as the main objects to guide. This paper introduces a SOU price that is suitable for charging EVs on expressways after considering CSs' running states, as shown in Table 1 (this paper sets α and β to 0.15 and 0.11 based on [20]). It can be seen from the characteristics of users' automatic response to the price incentive [21] that the new charging price succeeded in motivating users to turn to idle CSs to get charged to some extent.…”

Section: Strategy Of Orderly Charging With a New Electricity Pricing mentioning

confidence: 99%

A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways

et al. 2016

View full text Add to dashboard Cite

Abstract:With the large-scale adoption of electric vehicles (EVs) on expressways, the exploration of a guiding-based charging method to effectively adjust interactions between EVs and the fast charging stations (CSs) is urgently needed. This paper proposes a status-of-use (SOU) price-based charging strategy that can motivate users to charge in advance. A queuing model for a CS cluster was established to verify the effectiveness of the strategy, and then a simulation of traveling and charging conditions of 12,000 pure EVs on the road network from 0:00 to 24:00 was performed according to the related data and using the Monte Carlo method, the Floyd-Warshall algorithm, and the queuing algorithm proposed in this paper. Compared to unordered charging (UC), SOU price-based charging can not only reduce the charging cost and waiting time for users, but also increase the utilization ratio of charging facilities in a CS cluster and thus lower their influence on the power grid and expressway traffic. SOU price-based charging can effectively adjust interactions between EVs and CSs.

show abstract

Section: Strategy Of Orderly Charging With a New Electricity Pricing mentioning

confidence: 99%

A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Constraint (11) indicates the integer variables whose element v si is 0 or 1 in the decision variable V X . Constraint (12) indicates that if and only if there is one type of charging piles configured for each EV. Constraint (13) indicates that the charging power of each EV does not exceed the allowable value during different periods, and that the charging process is uninterrupted.…”

Section: Stage Ii: Coordinated Optimization Of Ev Charging and Chargimentioning

confidence: 99%

“…In this paper, the load data on typical workdays was selected for plotting the routine power load [12], as shown in Figure 3. Among the horizontal coordinates, Segment 8 denotes 8:00 to 9:00, Segment 9 denotes 9:00 to 10:00, …, and Segment 17 denotes 17:00 to 18:00, the duration of all of which is one hour.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…Wei et al [11] proposed an EV optimal charging method that could improve the operating income of charging facilities. Xia et al [12] introduced the concept of the distribution network's power supply capability and proposed an EV optimal charging method that could reduce the charging costs for the users and the impacts on the power grid. Zhao et al [13] comprehensively investigated the photovoltaic output and EV stoppage features, and an optimal charging method was proposed to coordinate the EVs and photovoltaic output under the time-of-use price.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Study on Coordinated Optimization of Electric Vehicle Charging and Charging Pile Selection

et al. 2018

View full text Add to dashboard Cite

This paper was intended to explore the mutual influences between electric vehicle (EV) charging and charging facility planning, to establish a two-stage model for optimizing the EVs' charging and charging piles' selection. In the first stage, the distribution pattern of the demands for EV charging, and various EVs were effectively grouped, in order to reduce the amount of computation for solving the second stage model. The goal of the second stage was to minimize the annual investment and electricity purchasing costs on the charging piles, and the coordinated optimization was carried out for EV charging and charging pile selection. The CPLEX and IP_SOLVE packages were used in MATLAB (R2014a/64 bits) to solve the established optimization model. The simulation results showed that, compared with the scheme for selecting the charging pile under the typical charging pattern (TCP), the total cost of the charging pile could be reduced by 6.32% with a scheme under the optimized charging pattern (OCP), thereby promoting the coordinated development of both the EVs and charging facilities.

show abstract

“…This profit includes the revenue obtained from selling energy to the EV owners and from reducing load based on negative imbalance prices. The upper level problem is presented below: (9) where,  and ) (  are the confidence level and auxiliary variable to calculate risk, respectively. Equation (2) explains the objective function from the aggregator's perspective in which  is the risk coefficient that keeps the tradeoff between the expected profit and CVaR.…”

Section: Upper Level: Aggregator′s Viewpointmentioning

confidence: 99%

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

et al. 2017

View full text Add to dashboard Cite

This paper proposes a stochastic bi-level decision-making model for an electric vehicle (EV) aggregator in a competitive environment. In this approach, the EV aggregator decides to participate in day-ahead (DA) and balancing markets, and provides energy price offers to the EV owners in order to maximize its expected profit. Moreover, from the EV owners' viewpoint, energy procurement cost of their EVs should be minimized in an uncertain environment. In this study, the sources of uncertainty-including the EVs demand, DA and balancing prices and selling prices offered by rival aggregators-are modeled via stochastic programming. Therefore, a two-level problem is formulated here, in which the aggregator makes decisions in the upper level and the EV clients purchase energy to charge their EVs in the lower level. Then the obtained nonlinear bi-level framework is transformed into a single-level model using Karush-Kuhn-Tucker (KKT) optimality conditions. Strong duality is also applied to the problem to linearize the bilinear products. To deal with the unwilling effects of uncertain resources, a risk measurement is also applied in the proposed formulation. The performance of the proposed framework is assessed in a realistic case study and the results show that the proposed model would be effective for an EV aggregator decision-making problem in a competitive environment.

show abstract

Aggregator-Based Interactive Charging Management System for Electric Vehicle Charging

Cited by 34 publications

References 29 publications

A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways

A Study on Price-Based Charging Strategy for Electric Vehicles on Expressways

A Study on Coordinated Optimization of Electric Vehicle Charging and Charging Pile Selection

A Stochastic Bi-Level Scheduling Approach for the Participation of EV Aggregators in Competitive Electricity Markets

Contact Info

Product

Resources

About