Demand Response in Smart Residential Buildings

2020 ◽  
pp. 361-390
Author(s):  
S. L. Arun ◽  
M. P. Selvan
Keyword(s):  
Demand Response ◽  
Residential Buildings

Dynamic Demand Response Controller Based on Real-Time Retail Price for Residential Buildings

2014 ◽  
Vol 5 (1) ◽  
pp. 121-129 ◽  
Author(s):  
Ji Hoon Yoon ◽  
Ross Baldick ◽  
Atila Novoselac
Keyword(s):  
Real Time ◽  
Demand Response ◽  
Residential Buildings ◽  
Retail Price ◽  
Dynamic Demand

scholarly journals Data-Driven Baseline Estimation of Residential Buildings for Demand Response

Energies ◽  
2015 ◽  
Vol 8 (9) ◽  
pp. 10239-10259 ◽  
Author(s):  
Saehong Park ◽  
Seunghyoung Ryu ◽  
Yohwan Choi ◽  
Jihyo Kim ◽  
Hongseok Kim
Keyword(s):  
Demand Response ◽  
Residential Buildings ◽  
Data Driven ◽  
Baseline Estimation

scholarly journals A set of comprehensive indicators to assess energy flexibility: a case study for residential buildings

2019 ◽  
Vol 111 ◽  
pp. 04044
Author(s):  
Francesco D’Ettorre ◽  
Marcus Brennenstuhl ◽  
Anjukan Kathirgamanathan ◽  
Mattia De Rosa ◽  
Malcolm Yadack ◽  
...  
Keyword(s):  
Demand Response ◽  
Residential Buildings ◽  
Renewable Energy Sources ◽  
Residential Building ◽  
Heating System ◽  
Mixed Integer ◽  
Programming Approach ◽  
Building Model ◽  
Capacity Change ◽  
External Signals

The increasing share of renewable energy sources in the power industry poses challenges for grid management due to the stochastic nature of their production. Besides the traditional supplyside regulation, grid flexibility can also be provided by the demand side. Demand-Response is an attractive approach based on adapting user demand profiles to match grid supply constraints. Nevertheless, defining the flexibility potential related to buildings is not straightforward and continues to pose challenges. Commonly accepted and standardized indicators for quantifying flexibility are still missing. The present paper proposes a new quantification methodology to assess the energy flexibility of a residential building. A set of comprehensive indicators capturing three key elements of building energy flexibility for demand response, notably, capacity, change in power consumption and cost of the demand response action have been identified. The proposed methodology is applied to a residential building, whose heating system is controlled by means of a model predictive control algorithm. The building model is developed on the basis of the experimental data collected in the framework of a European Commission supported H2020 research project Sim4Blocks, which deals with the implementation of demand response in building clusters. The optimal control problem has been investigated by means of mixed-integer linear programming approach. Real time prices are considered as external signals from the grid driving the DR actions. Results show that the proposed indicators, presented in the form of daily performance maps, allow to effectively assess the energy flexibility potential through its main dimensions and can be easily used either by an end-user or a grid-operator perspective to identify day by day the best DR action to be implemented.

scholarly journals Demand response algorithms for smart-grid ready residential buildings using machine learning models

2019 ◽  
Vol 239 ◽  
pp. 1265-1282 ◽  
Author(s):  
Fabiano Pallonetto ◽  
Mattia De Rosa ◽  
Federico Milano ◽  
Donal P. Finn
Keyword(s):  
Machine Learning ◽  
Smart Grid ◽  
Demand Response ◽  
Residential Buildings ◽  
Learning Models ◽  
Machine Learning Models
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