Further exploring the potential of residential demand response programs in electricity distribution

2014 ◽  
Vol 125 ◽  
pp. 39-59 ◽  
Author(s):  
Cajsa Bartusch ◽  
Karin Alvehag
Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2795
Author(s):  
Nikolaos Iliopoulos ◽  
Motoharu Onuki ◽  
Miguel Esteban

Residential demand response empowers the role of electricity consumers by allowing them to change their patterns of consumption, which can help balance the energy grid. Although such type of management is envisaged to play an increasingly important role in the integration of renewables into the grid, the factors that influence household engagement in these initiatives have not been fully explored in Japan. This study examines the influence of interpersonal, intrapersonal, and socio-demographic characteristics of households in Yokohama on their willingness to participate in demand response programs. Time of use, real time pricing, critical peak pricing, and direct load control were considered as potential candidates for adoption. In addition, the authors explored the willingness of households to receive non-electricity related information in their in-home displays and participate in a philanthropy-based peer-to-peer energy platform. Primary data were collected though a questionnaire survey and supplemented by key informant interviews. The findings indicate that household income, ownership of electric vehicles, socio-environmental awareness, perceived sense of comfort, control, and complexity, as well as philanthropic inclinations, all constitute drivers that influence demand flexibility. Finally, policy recommendations that could potentially help introduce residential demand response programs to a wider section of the public are also proposed.


Author(s):  
Iliopoulos Nikolaos ◽  
◽  
Onuki Motoharu ◽  
Nistor Ioan ◽  
Esteban Miguel

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.


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.


2015 ◽  
Vol 6 (3) ◽  
pp. 1453-1462 ◽  
Author(s):  
Mohammad Rastegar ◽  
Mahmud Fotuhi-Firuzabad

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 693
Author(s):  
Iker Esnaola-Gonzalez ◽  
Marko Jelić ◽  
Dea Pujić ◽  
Francisco Javier Diez ◽  
Nikola Tomašević

Recent studies show that energy consumption of buildings has dramatically increased over the last decade, accounting for more than 35% of global energy use. However, with proper operation, significant energy savings can be achieved. Demand response is envisioned as a key enabler of this operation enhancement, as it may contribute to the reduction of demand peaks and maximization of renewable energy exploitation while mitigating potential problems with grid stability. In this article, a system based on artificial intelligence that solves the complex multi-objective problem to bring demand response programs to the residential sector is proposed. Through the application of novel machine learning-based algorithms, a unique control loop is developed to help dwellers determine how and when to use their appliances. The feasibility and validity of the proposed system has been demonstrated in a real-world neighbourhood where a notable reduction and shift of electricity demand peaks has been achieved. Concretely, in accordance with extreme changes in the energy prices, the users have demonstrated the ability to shift their demand to periods with lower prices as well as reducing power consumption during periods with higher prices, thus fully translating the demand peak in time.


Sign in / Sign up

Export Citation Format

Share Document