Optimal Decomposition of Flexible Load Resource Reserve Target Based on Power Demand Response

Liu, Junhui; Hongkun, Bai; Li, Hujun; Yang, Quan‐Hong; Deng, Fangzhao; Zhao, Wenjie

doi:10.1109/icpeca51329.2021.9362628

Cited by 6 publications

(2 citation statements)

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“…When the price of the wholesale electricity market rises or the system's reliability is threatened, aggregators and other companies temporarily change their inherent customary power consumption mode according to their price signal or incentive measures, reducing or shifting the power load in a certain period. In recent years, the national policy has put forward precise requirements for demand response capacity building to guide and encourage end users to use peak shaving resources, which should cover about 3% of the maximum annual load [4][5].…”

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%

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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“…The demand-side users can change their original power consumption mode according to the market price signals or incentive mechanism (Junhui et al, 2021;Lijesen, 2007). The price-based DR can utilize price signals to guide electrical load users to coordinate the electrical load of the system to realize the time transfer of electrical energy.…”

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Physical Energy Storage Planning of Integrated Energy Systems Considering Dynamic Characteristics of Heating Network and Demand Response

et al. 2021

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The configuration of energy storage in the integrated energy system (IES) can effectively improve the consumption rate of renewable energy and the flexibility of system operation. Due to the high cost and long cycle of the physical energy storage construction, the configuration of energy storage is limited. The dynamic characteristics of the heating network and the demand-side response (DR) can realize the space-time transfer of energy. Although there is no actual energy storage equipment construction, it plays a similar role to physical energy storage and can be considered as virtual energy storage in IES planning. In this paper, a multi-scenario physical energy storage planning model of IES considering the dynamic characteristics of the heating network and DR is proposed. To make full use of the energy storage potential of the proposed model, the virtual energy storage features of the dynamic heating characteristics of the heating network and DR are analyzed at first. Next, aiming at the uncertainty of wind turbine (WT) and photovoltaic (PV) output, the scenario analysis method is used to describe the wind and photovoltaic power output with different probabilities. Finally, an electrothermal IES with an IEEE 33-node network and a 26-node heating network serves as an example to verify the effectiveness of the proposed model. The case study shows that the proposed model effectively reduces the physical energy storage configuration and achieves the economic trade-off between the investment cost and the operation cost.

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Potential Evaluation Method for Aggregated Demand Response Resources Based on User Pattern Recognition

Chen

Zhang

Chen

et al. 2023

2023 6th International Conference on Energy, Electrical and Power Engineering (CEEPE)

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Optimal Decomposition of Flexible Load Resource Reserve Target Based on Power Demand Response

Cited by 6 publications

References 0 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

Multi-Scenario Physical Energy Storage Planning of Integrated Energy Systems Considering Dynamic Characteristics of Heating Network and Demand Response

Potential Evaluation Method for Aggregated Demand Response Resources Based on User Pattern Recognition

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