Open Access

ARTICLE

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

Zhishuo Zhang, Xinhui Du^*, Yaoke Shang, Jingshu Zhang, Wei Zhao, Jia Su

School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, 030000, China

* Corresponding Author: Xinhui Du. Email:

Energy Engineering 2024, 121(6), 1577-1605. https://doi.org/10.32604/ee.2024.047706

Received 14 November 2023; Accepted 05 January 2024; Issue published 21 May 2024

Abstract

To address the issues of limited demand response data, low generalization of demand response potential evaluation, and poor demand response effect, the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis. Firstly, based on the demand response process and demand response behavior, obtain demand response characteristics that characterize the process and behavior. Secondly, establish a feature extraction and prediction model based on data mining, including similar day clustering, time series decomposition, redundancy processing, and data prediction. The predicted values of each demand response feature on the response day are obtained. Thirdly, the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads, and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios. Finally, the effectiveness of the method proposed in the article is verified through examples, providing a reference for load aggregators to formulate demand response schemes.

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Keywords

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