Loss Aversion Robust Optimization Model Under Distribution and Mean Return Ambiguity

Jia, Wang; Zhou, MengChu; Guo, Xiwang; Qi, Liang; Wang, Xu

doi:10.1109/tcss.2022.3195816

Cited by 3 publications

(1 citation statement)

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“…In [13], the stability conditions of noncooperative exchange systems with loss‐averse agents were studied. In [14], the effects of ambiguity aversion and loss aversion on robust optimal portfolio returns were studied. In [15], a payment mechanism was designed to encourage users to improve their data quality based on loss aversion.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%