Large-Scale Demonstration of Precise Demand Response Provided by Residential Heating Systems

TL;DR

This study demonstrates large-scale, autonomous demand response in residential heating systems, showing significant load reduction capabilities and effective rebound damping, aiding grid stability with minimal data requirements.

Contribution

It introduces a method to identify and quantify demand response potential of residential heat pumps using only energy meter and temperature data, enabling scalable demand management.

Findings

Load reductions of 40-65% of aggregate demand

Precise prediction of load reduction and rebound effects

Effective damping of demand rebound

Abstract

Being able to adjust the demand of electricity can be an effective means for power system operators to compensate fluctuating renewable generation, to avoid grid congestion, and to cope with other contingencies. Electric heating and cooling systems of buildings can provide different demand response services because their electricity consumption is inherently flexible because of their thermal inertia. This paper reports on the results of a large-scale demand response demonstration involving a population of more than 300 residential buildings with heat pump installations. We show how the energetic behavior and flexibility of individual systems can be identified autonomously based only on energy meter data and outdoor air temperature measurements, and how the aggregate demand response potential of the population can be quantified. Various load reduction and rebound damping experiments illustrate the effectiveness of the approach: the load reductions can be predicted precisely and amount to 40-65% of the aggregate load, and the rebound can be damped efficiently.