Conference paper
Data-driven Demand Response Characterization and Quantification
Analysis of load behavior in demand response (DR) schemes is important to evaluate the performance of participants. Very few real-world experiments have been carried out and quantification and characterization of the response is a difficult task. Nevertheless it will be a necessary tool for portfolio management of consumers in a DR framework.
In this paper we develop methods to quantify and characterize the amount of DR in a load. The contribution to the aggregated load from each household is quantified on a daily basis, showing the potential variability of the response in time. Clustering on the average values and standard deviation of the contribution regroups households with the same average response.
Independent Component Analysis (ICA) is used to characterize different DR delivery profiles.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2017 |
| Pages: | 1-6 |
| Proceedings: | 12th IEEE Power and Energy Society PowerTech Conference |
| ISBN: | 1509042377 , 1509042385 , 9781509042371 and 9781509042388 |
| Types: | Conference paper |
| DOI: | 10.1109/PTC.2017.7981009 |
| ORCIDs: | Le Ray, Guillaume and Pinson, Pierre |
DR characterization DR quantification Demand Response (DR) Energy analytics SDG 7 - Affordable and Clean Energy Smart grid
Heat pumps Heating systems ICA Load modeling Real-time systems Standards Wind forecasting aggregated load consumer behaviour consumer portfolio management contribution regroups households data-driven demand response characterization data-driven demand response quantification demand response schemes demand side management energy analytics independent component analysis load behavior analysis real-world experiments smart grid standard deviation
