Cross-grid demand response (DR) coordinating framework in energy Internet – A case of power market participation of gas DR resources

2022 ◽  
Vol 135 ◽  
pp. 107352
Author(s):  
Fangyuan Xu ◽  
Zhengxin Fu ◽  
Yiqiang Duan ◽  
Sibin Xu ◽  
Yifei Wang ◽  
...  
Keyword(s):  
Demand Response ◽  
Power Market ◽  
Market Participation ◽  
Energy Internet

scholarly journals Research on Demand Response Aggregators Participating in Power Market Based on Bi-level Optimization

2021 ◽  
Vol 781 (4) ◽  
pp. 042009
Author(s):  
Weijie Shen ◽  
Cheng Fang ◽  
Jiaxin Ma ◽  
Jialin Lin ◽  
Ming Zeng
Keyword(s):  
Demand Response ◽  
Power Market ◽  
On Demand

Using Cluster Analysis and Dynamic Programming for Demand Response Applied to Electricity Load in Residential Homes

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Pattanun Chanpiwat ◽  
Steven A. Gabriel ◽  
Rachel L. Moglen ◽  
Michael J. Siemann
Keyword(s):  
Dynamic Programming ◽  
Demand Response ◽  
Power Market ◽  
Practical Method ◽  
Important Application ◽  
Homogeneous Groups ◽  
Market Participants ◽  
Electricity Load ◽  
Scheduling Tool ◽  
Business As Usual

Abstract This paper develops means to analyze and cluster residential households into homogeneous groups based on the electricity load. Classifying customers by electricity load profiles is a top priority for retail electric providers (REPs), so they can plan and conduct demand response (DR) effectively. We present a practical method to identify the most DR-profitable customer groups as opposed to tailoring DR programs for each separate household, which may be computationally prohibitive. Electricity load data of 10,000 residential households from 2017 located in Texas was used. The study proposed the clustered load-profile method (CLPM) to classify residential customers based on their electricity load profiles in combination with a dynamic program for DR scheduling to optimize DR profits. The main conclusions are that the proposed approach has an average 2.3% profitability improvement over a business-as-usual heuristic. In addition, the proposed method on average is approximately 70 times faster than running the DR dynamic programming separately for each household. Thus, our method not only is an important application to provide computational business insights for REPs and other power market participants but also enhances resilience for power grid with an advanced DR scheduling tool.

scholarly journals Research on joint optimal scheduling of air conditioning load and electric vehicle under demand response

2021 ◽  
Vol 257 ◽  
pp. 01058
Author(s):  
Haiyu Huang ◽  
Chunming Wang ◽  
Shaolian Xia ◽  
Huaqiang Xiong ◽  
Baofeng Jiang ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Demand Response ◽  
Air Conditioning ◽  
Power Grid ◽  
Peak Load ◽  
Optimal Scheduling ◽  
Peak Shaving ◽  
Energy Internet ◽  
Direct Load Control ◽  
Scheduling Model

As an important part of energy Internet carrier, demand side resources can participate in many interactions with power grid. In order to reduce the peak to valley load difference of power grid, from the perspective of tapping the combined peak shaving potential of air conditioning load and electric vehicles, guided by TOU price and direct load control, this paper proposes an optimal scheduling model with the minimum load difference and the maximum total revenue of users as the objective function. The results show that the joint optimal scheduling strategy can reduce the peak load and eliminate the “secondary peak load” caused by disorderly charging of electric vehicles.

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