A novel method for evaluating the impact of residential demand response in a real time distribution energy market

2016 ◽  
Vol 7 (4) ◽  
pp. 533-545 ◽  
Author(s):  
Pierluigi Siano ◽  
Debora Sarno ◽  
Lorena Straccia ◽  
Anna Teresa Marrazzo
Smart Grid ◽  
2017 ◽  
pp. 193-222
Author(s):  
Zhi Chen ◽  
Lei Wu

2013 ◽  
Vol 4 (1) ◽  
pp. 227-234 ◽  
Author(s):  
Peizhong Yi ◽  
Xihua Dong ◽  
Abiodun Iwayemi ◽  
Chi Zhou ◽  
Shufang Li

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.


Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 808
Author(s):  
Andrew Blohm ◽  
Jaden Crawford ◽  
Steven A. Gabriel

Residential demand response (DR) programs are generally administered through an electricity distribution utility, or an electric grid operator. These programs typically reduce electricity consumption by inducing behavioral changes in the occupants of participating households. We propose implementing a wholesale-price-sensitive residential DR program through the retail electricity provider (REP), who has more naturally aligned incentives to avoid high wholesale electricity prices and maintain customer satisfaction, as compared to distribution utilities, grid operators, and the average residential consumer. Retail electricity providers who serve residential consumers are exposed to substantial price risk as they generally have a portion of their portfolio exposed to variable real-time wholesale electricity prices, despite charging their residential customers a fixed retail electricity price. Using Monte Carlo simulations, we demonstrate that demand response, executed through internet-connected thermostats, to shift real-time residential HVAC load in response to real-time prices, can be used as an effective physical hedge, which is both less costly and more effective than relying solely on financial hedging mechanisms. We find that on average a REP can avoid USD 62.07 annually per household using a load-shifting program. Given that REPs operate in a low margin industry, an annual avoided cost of this magnitude is not trivial.


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