Combined Provision of Economic Dispatch and Frequency Regulation by Aggregated EVs Considering Electricity Market Interaction

Gao, Shuang; Dai, Ruxin; Cao, Wenjing; Che, Yanbo

doi:10.1109/tte.2022.3195567

Cited by 16 publications

(6 citation statements)

References 30 publications

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“…This calls for the development of appropriate energy management and supply concepts for EVs [1]- [3]. In this context, aggregation entities are introduced to enable the market participation of EV users [4]- [6]. The aggregation entities manage the interaction between the EV users and the market to provide market visibility and participation opportunities [4], [7]- [11].…”

Section: Introductionmentioning

confidence: 99%

“…This enables aggregation entities to define and implement smart charging strategies for EVs. Prior research showed how to design smart charging strategies [12]- [20] to reduce the overall energy cost, avoid grid congestions, circumvent peak loads due to the charging processes, and provide frequency regulation services [6], [21].…”

Section: Introductionmentioning

confidence: 99%

“…The optimization approaches in [24], [25] consider risk aversion to obtain the aggregated charging schedule under uncertainty of energy prices, driving behavior of the users, and renewable energy generation. In [6] and [21], it was demonstrated that aggregation and optimization of the charging process of EVs can at the same time reduce the energy cost and form the basis for offering ancillary services of frequency regulation and peak load shaving.…”

Section: Introductionmentioning

confidence: 99%

“…Nonetheless, each individual charging process operates independently and utilizes the aggregated charging schedule as an indirect control signal to determine charging time and charging power. Individual optimization of each single EV, as in [6], [9], [12], is not necessary. Third, while EV users send information about their EVs' states of energy to the aggregator, this process is realized in an anonymized manner.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Gromann,

Raab,

Strunz

2024

IEEE Trans. Transp. Electrific.

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With the share of electric vehicles in the transportation sector rising, solutions for their power system and energy market integration are of increasing importance. In this context, aggregation entities are helpful for addressing the integration process of electric vehicle (EV) fleets. In this paper, a novel methodology for day-ahead charging control of EV fleets is proposed. This includes the prediction of required energy used for charging purposes based on synthetic data and functionalities for optimized energy procurement in the day-ahead energy market to determine the aggregated charging schedule of the fleet. The aggregated charging schedule provides the basis for the proposed charging control algorithm for individual EVs. This algorithm allows for charging flexibility according to the user's driving preferences while considering data privacy. To avoid data privacy conflicts, the scheduling of the individual charging is decoupled from the upcoming individual driving schedules of the users themselves. This means that the user is not asked to plan and submit expected driving profiles. Instead, realization is by a user-oriented disaggregation that utilizes the aggregated charging schedule as an input to coordinate the individual charging processes itself. The resulting integrative market solution allows to fulfill the contract positions in energy market participation. The simulation results verify the optimality of the proposed methodology for day-ahead operation and its applicability for aggregation entities that serve as an intermediate between the vehicle owners and electrical utilities or the energy market.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Gromann,

Raab,

Strunz

2024

IEEE Trans. Transp. Electrific.

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“…In [8], the bidding strategy and dispatching scheme of EVs and energy storage in the auxiliary service market were analyzed. In [9], a dynamic control strategy for EV charging in response to regulatory signals was developed. In [10], an EV aggregator bidding strategy for the day‐ahead market was developed.…”

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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Smart Vehicle-to-Grid Operation of Power System based on EV User Behavior

Shin,

Kim,

Mahseredjian

et al. 2024

J. Electr. Eng. Technol.

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Combined Provision of Economic Dispatch and Frequency Regulation by Aggregated EVs Considering Electricity Market Interaction

Cited by 16 publications

References 30 publications

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

Optimal Charging Control of Electric Vehicle Fleets Based on Demand Aggregation and User-Oriented Disaggregation Respecting Data Privacy

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

Smart Vehicle-to-Grid Operation of Power System based on EV User Behavior

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