Flexible demand response programs modeling in competitive electricity markets

2011 ◽  
Vol 88 (9) ◽  
pp. 3257-3269 ◽  
Author(s):  
M. Parsa Moghaddam ◽  
A. Abdollahi ◽  
M. Rashidinejad
Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 570
Author(s):  
Peter Schwarz ◽  
Saeed Mohajeryami ◽  
Valentina Cecchi

Peak-time rebates offer an opportunity to introduce demand response in electricity markets. To implement peak-time rebates, utilities must accurately determine the consumption level if the program were not in effect. Reliable calculations of customer baseline load elude utilities and independent system operators, due to factors that include heterogeneous demands and random variations. Prevailing research is limited for residential markets, which are growing rapidly with the presence of load aggregators and the availability of smart grid systems. Our research pioneers a novel method that clusters customers according to the size and predictability of their demands, substantially improving existing customer baseline calculations and other clustering methods.


2020 ◽  
Vol 53 ◽  
pp. 101921 ◽  
Author(s):  
Dongmin Yu ◽  
Xinyi Xu ◽  
Mingyu Dong ◽  
Sayyad Nojavan ◽  
Kittisak Jermsittiparsert ◽  
...  

Author(s):  
Hassan Jalili ◽  
Pierluigi Siano

Abstract Demand response programs are useful options in reducing electricity price, congestion relief, load shifting, peak clipping, valley filling and resource adequacy from the system operator’s viewpoint. For this purpose, many models of these programs have been developed. However, the availability of these resources has not been properly modeled in demand response models making them not practical for long-term studies such as in the resource adequacy problem where considering the providers’ responding uncertainties is necessary for long-term studies. In this paper, a model considering providers’ unavailability for unforced demand response programs has been developed. Temperature changes, equipment failures, simultaneous implementation of demand side management resources, popular TV programs and family visits are the main reasons that may affect the availability of the demand response providers to fulfill their commitments. The effectiveness of the proposed model has been demonstrated by numerical simulation.


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