scholarly journals Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 808
Author(s):  
Andrew Blohm ◽  
Jaden Crawford ◽  
Steven A. Gabriel
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Wholesale Price ◽  
Electricity Consumption ◽  
Behavioral Changes ◽  
Electricity Prices ◽  
Reduce Electricity Consumption ◽  
Grid Operators ◽  
Residential Demand

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.

Real-Time Residential Demand Response Management

Smart Grid ◽  
2017 ◽  
pp. 193-222
Author(s):  
Zhi Chen ◽  
Lei Wu
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Residential Demand

scholarly journals Expert Assessment of Prioritized Determinants For a Smarter Grid Through The Lens of Residential Demand Response: The Case Study of Ontario, Canada

2021 ◽  
Vol 8 ◽  
pp. 33-46
Author(s):  
Iliopoulos Nikolaos ◽  
◽  
Onuki Motoharu ◽  
Nistor Ioan ◽  
Esteban Miguel
Keyword(s):  
Smart Grid ◽  
Demand Response ◽  
Smart Grids ◽  
Policy Implications ◽  
Expert Assessment ◽  
Private Data ◽  
Residential Demand ◽  
Technological Limitations ◽  
Demand Response Programs

In recent years, smart grids have attracted considerable attention. However, despite the promising potential of the technologies encompassed within such systems, their adoption has been slow, geographically varied, and in the context of residential demand response, often subject to public scrutiny. The heterogeneous evolution of the smart grid is not only the product of technological limitations but is additionally sensitive to socio-political considerations prevalent at the national or provincial level. Through expert interviews that were conducted in Ontario, Canada, this study provides insights into which smart grid factors are considered as most important for its development, and also what are the drivers, inhibitors, benefits, and drawbacks that a smart grid provides and / or entails, placing particular emphasis on residential demand response programs. The constructs scrutinized were adapted from previous studies, and the information collected was analyzed following the procedure of the Grounded Delphi Method. The findings indicate that a consensus was reached, in that smart grids pave the way for increased demand flexibility and loss reductions, though these are contingent on measures being implemented regarding the creation of investment opportunities, engagement of consumers, and ensuring the security of private data. Relevant policy implications and research recommendations are also explored.

Unleashing analytics to reduce electricity consumption using incremental clustering algorithm

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Archana Yashodip Chaudhari ◽  
Preeti Mulay
Keyword(s):  
Real Time ◽  
Clustering Algorithm ◽  
Electricity Consumption ◽  
Incremental Clustering ◽  
Science Data ◽  
Load Curve ◽  
Data Set ◽  
Content Type ◽  
Validity Indices ◽  
Reduce Electricity Consumption

Purpose To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch. Design/methodology/approach This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering. Findings Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors. Originality/value ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.

Real-Time Opportunistic Scheduling for Residential Demand Response

2013 ◽  
Vol 4 (1) ◽  
pp. 227-234 ◽  
Author(s):  
Peizhong Yi ◽  
Xihua Dong ◽  
Abiodun Iwayemi ◽  
Chi Zhou ◽  
Shufang Li
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Opportunistic Scheduling ◽  
Residential Demand

scholarly journals Ramping of Demand Response Event with Deploying Distinct Programs by an Aggregator

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1389 ◽  
Author(s):  
Omid Abrishambaf ◽  
Pedro Faria ◽  
Zita Vale
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Small Time ◽  
Time Step ◽  
Strategic Plans ◽  
Time Simulation ◽  
Demand Reduction ◽  
Resistive Loads ◽  
Demand Response Programs

System operators have moved towards the integration of renewable resources. However, these resources make network management unstable as they have variations in produced energy. Thus, some strategic plans, like demand response programs, are required to overcome these concerns. This paper develops an aggregator model with a precise vision of the demand response timeline. The model at first discusses the role of an aggregator, and thereafter is presented an innovative approach to how the aggregator deals with short and real-time demand response programs. A case study is developed for the model using real-time simulator and laboratory resources to survey the performance of the model under practical challenges. The real-time simulation uses an OP5600 machine that controls six laboratory resistive loads. Furthermore, the actual consumption profiles are adapted from the loads with a small-time step to precisely survey the behavior of each load. Also, remuneration costs of the event during the case study have been calculated and compared using both actual and simulated demand reduction profiles in the periods prior to event, such as the ramp period.

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