Demand response potential of residential load equipments

Abstract

This thesis was conducted inside the Smart Grids and Energy Markets (SGEM) project and as a continuation of SGEM Task 6.11, which aims to investigate the utilization of AMRs for spatial load analysis for long term load forecasting of households providing calculating mechanism of hourly level appliance consumptions in the modelling of Demand Response. The goals of this study are to disaggregate electrical loads of a sample of Finnish households to load device groups. Subsequently to divide these loads into critical and non-critical loads, which enables timely changes in operation, and as the final outcome, to present the Demand Response Potential of these households. Data for this study was obtained from a database consisting of hourly metered power consumption (AMR) data from 1630 households from Kainuu (Finland) and background information of those costumers. The final dataset is compound by 337 households from the original data, those which have District Heating as their primary heating system.Conditional Demand Analysis (CDA) is performed using regression analysis as a tool. The analysis was computed with MATLAB and the used regression technique is the Stepwise regression. The regression analysis consists of 24 independent equations multilinear regression for different models and as outcome the daily load profiles of the appliances included in each different model were obtained.The obtained load profiles for different appliances and models are presented in the CDA results and appendices, in both graphic and numeric format, this last in tables containing the obtained estimated coefficients and the INMODEL matrix.Subsequently, the obtained loads are divided into critical and non-critical loads, the latter ones being ones to allow for timey changes in operation. Demand Response (DR) analysis is performed defining the maximum allowed postponement in time of each load use within different scenarios. Subsequently, the DR potential for each scenario are graphically presented, according the defined time spans and seasons models and using the appliances load profiles obtained from the CDA. Finally, in the conclusions chapter, the main finding are summarized and the author’s conclusions are presented, not only regarding the CDA and DR studies, but also about improvements in data collection and data analysis as well as further steps recommendations.