Hierarchical Charge Control of Large Populations of EVs

Shao, Chengcheng; Wang, Xifan; Wang, Xiuli; Du, Chao; Wang, Biyang

doi:10.1109/tsg.2015.2396952

Cited by 80 publications

(55 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Definition 3: Stable Matching A matching Φ is stable if and only if there are no two EVs j and j , ∀j, j ∈ M that Φ(j) j ≺ Φ(j ) and Φ(j ) j ≺ Φ(j) [8], where Φ(j) j ≺ Φ(j ) means EV j prefers CS assigned to EV j to its assignment Φ(j), and so does EV j .…”

Section: φ(Cs I ) ∈ M {∅} and |φ(Csmentioning

confidence: 99%

“…Reference [7] changes the charging time of each EV to minimize the fluctuation of the total load curve. In [8], a novel hierarchical charge control framework based on the Benders decomposition is proposed for large populations of EVs to minimize the grid operation cost and improve the unit operating efficiency. Reference [9] proposes a distributed EV coordination mechanism which utilizes the flexibility of EV demand the electricity feedback capability to minimize the load variance of the power grid.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

Zhang

Leng

et al. 2016

2016 IEEE/CIC International Conference on Communications in China (ICCC)

View full text Add to dashboard Cite

Abstract-The frequency, time and places of charging have large impact on the Quality of Experience (QoE) of EV drivers. It is critical to design effective EV charging scheduling system to improve the QoE of EV drivers. In order to improve EV charging QoE and utilization of CSs, we develop an innovative travel plan aware charging scheduling scheme for moving EVs to be charged at Charging Stations (CS). In the design of the proposed charging scheduling scheme for moving EVs, the travel routes of EVs and the utility of CSs are taken into consideration. The assignment of EVs to CSs is modeled as a two-sided manyto-one matching game with the objective of maximizing the system utility which reflects the satisfactory degrees of EVs and the profits of CSs. A Stable Matching Algorithm (SMA) is proposed to seek stable matching between charging EVs and CSs. Furthermore, an improved Learning based On-LiNe scheduling Algorithm (LONA) is proposed to be executed by each CS in a distributed manner. The performance gain of the average system utility by the SMA is up to 38.2% comparing to the Random Charging Scheduling (RCS) algorithm, and 4.67% comparing to Only utility of Electric Vehicle Concerned (OEVC) scheme. The effectiveness of the proposed SMA and LONA is also demonstrated by simulations in terms of the satisfactory ratio of charging EVs and the the convergence speed of iteration.

show abstract

Section: φ(Cs I ) ∈ M {∅} and |φ(Csmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

Zhang

Leng

et al. 2016

2016 IEEE/CIC International Conference on Communications in China (ICCC)

View full text Add to dashboard Cite

show abstract

“…Reference [8] presents vehicle-to-parking and parking-to-vehicle schemes for EVs in each home and parking lot through a stochastic model and analyzes their effect on the power system. Reference [9] suggests a way to satisfy both the minimization of power generation cost and the system constraint by hierarchical charging control considering the uncertainty of EVs. Reference [10] presents charging control schemes for EVs considering transmission congestion.…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging Strategy Based on Model Predictive Control in Electric Vehicle Parking Lots Considering Voltage Stability

2018

View full text Add to dashboard Cite

Abstract:Recently, electric vehicles (EVs) using energy storage have gained attention over conventional vehicles using fossil fuels owing to their advantages such as being eco-friendly and reducing the operation cost. In a power system, an EV, which operates through the energy stored in the battery, can be used as a type of load or energy source; hence, an optimal operation of EV clusters in power systems is being extensively studied. This paper proposes an optimal strategy for charging EVs in parking lots. This strategy is based on the model predictive control (MPC) framework due to the uncertainty of loads, renewable energy sources, and EVs, and considers the voltage stability of the distribution systems. EV chargers in the parking lot charge EVs to minimize the charging cost, which results in a sudden increase in charge load at a certain time. As a result, an excessive voltage drop may occur in the power system at that time. Therefore, we need to minimize the charging cost of EVs while preventing an excessive voltage drop in the power system. The parking lot is stochastically modeled to consider EV uncertainty under the MPC framework. In the MPC framework, the charging schedule of an EV charger in the parking lot is optimized by considering both voltage stability and charging cost minimization in real time. The charging constraints on voltage stability are updated through parameters that change in real time, and thus, errors caused by uncertainty can be reduced. Subsequently, this charging strategy is applied to multiple chargers through Monte Carlo simulation. The proposed charging strategy is verified based on MATLAB/Simulink.

show abstract

“…However, for photovoltaics (PV) and other intermittent renewable energy sources with output volatility, large-scale renewable energy connected to the power grid still has many barriers to overcome. Moreover, it is difficult to maintain stable operation of the power grid and optimize scheduling for large populations of EVs with uncertain charging characteristics [1][2][3]. In addition, if the charging power is generated by coal-fired power plants, the emission advantage for EV is not obvious [4,5].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Charging Strategy for PV-Based Battery Swapping Stations in a DC Distribution System

Li³

et al. 2017

International Journal of Photoenergy

View full text Add to dashboard Cite

Photovoltaic-(PV-) based battery swapping stations (BSSs) utilize a typical integration of consumable renewable resources to supply power for electric vehicles (EVs). The charging strategy of PV-based BSSs directly influences the availability, cost, and carbon emissions of the swapping service. This paper proposes an optimal charging strategy to improve the self-consumption of PV-generated power and service availability while considering forecast errors. First, we introduce the typical structure and operation model of PV-based BSSs. Second, three indexes are presented to evaluate operational performance. Then, a particle swarm optimization (PSO) algorithm is developed to calculate the optimal charging power and to minimize the charging cost for each time slot. The proposed charging strategy helps decrease the impact of forecast uncertainties on the availability of the battery swapping service. Finally, a day-ahead operation schedule, a real-time decision-making strategy, and the proposed PSO charging strategy for PV-based BSSs are simulated in a case study. The simulation results show that the proposed strategy can effectively improve the self-consumption of PV-generated power and reduce charging cost.

show abstract

Hierarchical Charge Control of Large Populations of EVs

Cited by 80 publications

References 26 publications

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

Matching theory based travel plan aware charging algorithms in V2G smart grid networks

Optimal Charging Strategy Based on Model Predictive Control in Electric Vehicle Parking Lots Considering Voltage Stability

An Optimal Charging Strategy for PV-Based Battery Swapping Stations in a DC Distribution System

Contact Info

Product

Resources

About