scholarly journals Optimal Customer Targeting for Sustainable Demand Response in Smart Grids 1

2016 ◽  
Vol 80 ◽  
pp. 324-334 ◽  
Author(s):  
Sanmukh R. Kuppannagari ◽  
Rajgopal Kannan ◽  
Charalampos Chelmis ◽  
Arash S. Tehrani ◽  
Viktor K. Prasanna
Keyword(s):  
Demand Response ◽  
Smart Grids ◽  
Customer Targeting

scholarly journals Efficient customer selection for sustainable demand response in smart grids

2014 ◽  
Author(s):  
Vasileios Zois ◽  
Marc Frincu ◽  
Charalampos Chelmis ◽  
Muhammad Rizwan Saeed ◽  
Viktor Prasanna
Keyword(s):  
Demand Response ◽  
Smart Grids ◽  
Selection For

Customer targeting optimization system for price-based demand response programs

2018 ◽  
Vol 29 (2) ◽  
pp. e2709 ◽  
Author(s):  
Luciano Cavalcante Siebert ◽  
Alexandre Rasi Aoki ◽  
Thelma S.P. Fernandes ◽  
Germano Lambert-Torres
Keyword(s):  
Demand Response ◽  
Optimization System ◽  
Customer Targeting ◽  
Demand Response Programs

scholarly journals Strategies for Datacenters Participating in Demand Response by Two-Stage Decisions

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yuling Li ◽  
Xiaoying Wang ◽  
Peicong Luo
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Smart Grids ◽  
Peak Load ◽  
Two Stage ◽  
The Sustainable Development ◽  
Second Stage ◽  
Potential Demand ◽  
Electricity Costs ◽  
Demand Response Programs

Modern smart grids have proposed a series of demand response (DR) programs and encourage users to participate in them with the purpose of maintaining reliability and efficiency so as to respond to the sustainable development of demand-side management. As a large load of the smart grid, a datacenter could be regarded as a potential demand response participant. Encouraging datacenters to participate in demand response programs can help the grid to achieve better load balancing effect, while the datacenter can also reduce its own power consumption so as to save electricity costs. In this paper, we designed a demand response participation strategy based on two-stage decisions to reduce the total cost of the datacenter while considering the DR requirements of the grid. The first stage determines whether to participate in demand response by predicting real-time electricity prices of the power grid and incentive information will be sent to encourage users to participate in the program to help shave the peak load. In the second stage, the datacenter interacts with its users by allowing users to submit bid information by reverse auction. Then, the datacenter selects the tasks of the winning users to postpone processing them with awards. Experimental results show that the proposed strategy could help the datacenter to reduce its cost and effectively meet the demand response requirements of the smart grid at the same time.

Demand Response in Smart Grids

2019 ◽  
Author(s):  
Pengwei Du ◽  
Ning Lu ◽  
Haiwang Zhong
Keyword(s):  
Demand Response ◽  
Smart Grids

Demand-Response Management Using a Fleet of Electric Vehicles: An Opportunistic-SDN-Based Edge-Cloud Framework for Smart Grids

IEEE Network ◽  
2019 ◽  
Vol 33 (5) ◽  
pp. 46-53 ◽  
Author(s):  
Kuljeet Kaur ◽  
Sahil Garg ◽  
Georges Kaddoum ◽  
Syed Hassan Ahmed ◽  
Francois Gagnon ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Demand Response ◽  
Smart Grids ◽  
Cloud Framework
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