Evaluation of Demand Response Effectiveness Based on Edge Computing

Dai, Chengcheng; Liao, Minle; Fan, Gaosong; Mei, Mingshun; Li, Hualin; Huang, Chongyang

doi:10.1109/cyber53097.2021.9588265

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Paper [23] proposes a similar day method of constructing characteristic quantity matrices based on non-demand-response factors for all alternative similar days and the demand response day and then choosing the days of which the weighted correlation degrees calculated are above 0.7. Paper [24] mainly focuses on the weight identification of several historical days rather than similar day selection. They choose the past 3 to 5 days ahead of the demand response day and the same days of previous 3 to 5 years and identify the lateral parameters and longitudinal parameters by harmony search algorithm.…”

Section: Introductionmentioning

confidence: 99%

Residential Demand Response Potential Detection: Model Mechanism and Application Analysis

Gao

Cheng

et al. 2022

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

Demand response potential detection is essential to promote the effect of demand response activities and further enhance the operational stability of the power grid. In this article, we establish a demand response potential detection model including estimating the loads of main network and residential customers based on similar date matching, where a mathematical program is formulated to determine the parameters in the formula of date distance, and measuring the similarity between the main network and customers during the peak and valley period, where a measure of matching degree is put forward innovatively. Customer profiles are then established based on load profile pattern, electricity consumption quantity and demand response willingness. In addition, case studies based on smart meter data of residential customers in Yuhang District, Hangzhou, Zhejiang, China, validate the effectiveness of our model.

show abstract

Section: Introductionmentioning

confidence: 99%