scholarly journals Analyzing the Impacts of a Deep-Learning Based Day-Ahead Residential Demand Response Model on The Jordanian Power Sector in Winter Season

2021 ◽  
Vol 7 ◽  
pp. 247-254
Author(s):  
Ayas Shaqour ◽  
Hooman Farzaneh
Keyword(s):  
Deep Learning ◽  
Demand Response ◽  
Winter Season ◽  
Response Model ◽  
Power Sector ◽  
Residential Demand

Modified deep learning and reinforcement learning for an incentive-based demand response model

Energy ◽  
2020 ◽  
Vol 205 ◽  
pp. 118019
Author(s):  
Lulu Wen ◽  
Kaile Zhou ◽  
Jun Li ◽  
Shanyong Wang
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Demand Response ◽  
Response Model

scholarly journals Day-Ahead Residential Electricity Demand Response Model Based on Deep Neural Networks for Peak Demand Reduction in the Jordanian Power Sector

2021 ◽  
Vol 11 (14) ◽  
pp. 6626
Author(s):  
Ayas Shaqour ◽  
Hooman Farzaneh ◽  
Huthaifa Almogdady
Keyword(s):  
Neural Networks ◽  
Price Elasticity ◽  
Demand Response ◽  
Deep Neural Networks ◽  
Response Model ◽  
Power Sector ◽  
Short Term ◽  
Peak Demand ◽  
Detailed Model ◽  
Residential Sector

In this paper, a comprehensive demand response model for the residential sector in the Jordanian electricity market is introduced, considering the interaction between the power generators (PGs), grid operators (GOs), and service providers (SPs). An accurate day-ahead hourly short-term load forecasting is conducted, using deep neural networks (DNNs) trained on four-year data collected from the National Electric Power Company (NEPCO) in Jordan. The customer behavior is modeled by developing a precise price elasticity matrix of demand (PEMD) based on recent research on the short-term price elasticity of Jordan’s residential and the analysis of the different types of electrical appliances and their daily operational hours according to the latest surveys. First, the DNNs are fine-tuned with a detailed feature analysis to predict the day-ahead hourly electrical demand and achieved a mean absolute percentage error (MAPE) of 1.365% and 1.411% on the validation and test datasets receptively. Then the predictions are used as input to a detailed model of the Jordanian power grid market, where a day-ahead peak-time demand response policy for the residential sector is applied to the three distribution power companies in Jordan. Based on different PEMD analyses for the Jordanian residential sector, the results suggest a reduction potential of 5.4% in peak demand accompanied by a cost reduction of USD 154,505 per day for the Jordanian power sector.

Learning-Enabled Residential Demand Response: Automation and Security of Cyberphysical Demand Response Systems

2021 ◽  
Vol 9 (1) ◽  
pp. 36-44
Author(s):  
Robert Mieth ◽  
Samrat Acharya ◽  
Ali Hassan ◽  
Yury Dvorkin
Keyword(s):  
Demand Response ◽  
Response Systems ◽  
Residential Demand

scholarly journals Model-Based and Data-Driven HVAC Control Strategies for Residential Demand Response

2021 ◽  
pp. 1-1
Author(s):  
Xiao Kou ◽  
Yan Du ◽  
Fangxing Li ◽  
Hector Pulgar-Painemal ◽  
Helia Zandi ◽  
...  
Keyword(s):  
Demand Response ◽  
Control Strategies ◽  
Data Driven ◽  
Model Based ◽  
Hvac Control ◽  
Residential Demand

scholarly journals Shedding Light on the Factors That Influence Residential Demand Response in Japan

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2795
Author(s):  
Nikolaos Iliopoulos ◽  
Motoharu Onuki ◽  
Miguel Esteban
Keyword(s):  
Demand Response ◽  
Primary Data ◽  
Willingness To Participate ◽  
The Public ◽  
Related Information ◽  
Direct Load Control ◽  
Integration Of Renewables ◽  
Residential Demand ◽  
Demand Response Programs ◽  
Energy Grid

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.

A review on price-driven residential demand response

2018 ◽  
Vol 96 ◽  
pp. 411-419 ◽  
Author(s):  
Xing Yan ◽  
Yusuf Ozturk ◽  
Zechun Hu ◽  
Yonghua Song
Keyword(s):  
Demand Response ◽  
Residential Demand

Real-time price-based demand response model for combined heat and power systems

Energy ◽  
2019 ◽  
Vol 168 ◽  
pp. 1119-1127 ◽  
Author(s):  
Manijeh Alipour ◽  
Kazem Zare ◽  
Heresh Seyedi ◽  
Mehdi Jalali
Keyword(s):  
Power Systems ◽  
Real Time ◽  
Demand Response ◽  
Combined Heat And Power ◽  
Response Model ◽  
Time Price
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