Temporal and Spatial Pricing for Optimal Electric Vehicle Charging and Discharging Management

Dai, Xianzhong; Chai, Yufeng; Han, Xiaoyan; Zhan, Xiangpeng; Yang, Jun

doi:10.1109/isgt-asia.2019.8881740

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Loss aversion [27] means that when people face the same number of gains and losses, they consider the losses more intolerable. The negative utility of equal loss is far greater than the positive utility of equal return.…”

Section: Preference and Contract Type Of Evsmentioning

confidence: 99%

A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes

Lin,

Zhou,

et al. 2023

IET Generation Trans & Dist

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With the development of intelligent distribution networks and the proposal of the carbon peaking and carbon neutrality goals, the importance of demand‐side management for the improvement of flexible power operation systems is becoming increasingly prominent. To solve the problems of the excessive peak–valley load differences, the insufficient utilization of demand‐side resources, and the unreasonable pricing of aggregators, this paper proposes a three‐stage economy optimization method for the aggregator based on EV(electric vehicle) user response volumes. To gain the advantages of different types of EVs while increasing the dispatchable capacity, the contract mode between the aggregator and EVs is divided into three categories: complete dispatching, rolling reward and punishment mechanism dispatching, and free dispatching. Based on the cloud model, considering loss aversion, the user's own time flexibility, and the impact of the tariff set by the aggregator on the user's decision‐making, we introduced indicator weights to obtain the improved cloud model. Based on the improved cloud model, a user response volume model is obtained. Then, the aggregator performs a three‐stage pricing optimization operation for EVs based on the bid‐winning peak shaving capacity. In the first stage, the attraction between EVs and charging stations is determined based on the law of gravity, and the charging and discharging reward electricity price is set. In the second stage, the dynamic electricity price of three types of EVs and the charging punishment electricity prices of the rolling reward and punishment mechanism dispatching type are determined. In the third stage, the electricity price of the rolling reward and punishment dispatching EVs is further optimized based on their lack of peak shaving capacity. The method proposed in the paper has been verified to be effective in increasing aggregator profits, increasing EV dispatch capacity, and reducing EV charging costs through the example analysis.

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Section: Preference and Contract Type Of Evsmentioning

confidence: 99%

A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes

Lin,

Zhou,

et al. 2023

IET Generation Trans & Dist

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Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Rahila,

Soundra Devi,

Radhika

et al. 2024

Optim Control Appl Methods

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Integrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for electric mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach for EV smart charging with network expansion planning. The hybrid technique is the joint execution of the coati optimization algorithm (COA) and cascade‐correlation‐growing deep learning neural network, commonly known as the COA‐CCG‐DLNN technique. The objective of the proposed method is to minimize the cost of charging EVs, and it forecasts the best course of action. EV charging with network expansion is based on vehicle‐to‐building (V2B), vehicle‐to‐grid (V2G), and grid‐to‐vehicle (G2V). The COA approach is used to minimize the cost of EV charging and the CCG‐DLNN approach is used to predict the optimal solution for the system. The proposed method is executed on the MATLAB platform and is compared with existing techniques like particle swarm optimization (PSO), heap‐based optimization (HBO), and wild horse optimization (WHO). The proposed method achieves a low cost of $1.33 and a high accuracy of 99.5% compared with other existing techniques. The performance metrics for the proposed method include 2996.348 as the best result, 3000.100 as the mean, 3001.261 as the worst, and a standard deviation of 1.160348, along with a median of 2998.816, all of which outperform other existing methods.

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Optimal Charging Scheduling of Electric Vehicles Based on CPSS

Cao

Lin³

et al. 2022

2022 41st Chinese Control Conference (CCC)

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Temporal and Spatial Pricing for Optimal Electric Vehicle Charging and Discharging Management

Cited by 3 publications

References 11 publications

A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes

A three‐stage economy optimization method for the aggregator based on electric vehicle user response volumes

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Optimal Charging Scheduling of Electric Vehicles Based on CPSS

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