A methodology for evaluating the energy, peak load and comfort effects of demand response control strategies for electric heating

Panel: 6. Innovations in buildings and appliances

This is a peer-reviewed paper.

Authors:
David Da Silva, Mines-Paristech, France
Bruno Duplessis, CEP Mines-Paristech, France
Jérôme Adnot, CEP Mines-Paristech, France

Abstract

Today due to new developments in the field of smart appliances and communications, new strategies in the residential sector can be employed to maintain the balance between electricity supply and demand. However the physical effects and the methods to evaluate the impact of these actions in the electric market system are not totally understood.

This article describes a methodology that makes possible the evaluation of Demand Response (DR) actions in thermal household appliances, from which the possible benefits can be determined for the consumer and for the electric grid.

The demand response actions that serve as example are associated with electric home heating load shifting when responding to a day-ahead real-time-pricing.

The randomized operation of a large number of electric heaters is simulated through a simple building thermal model. This enables calculation of the change in peak power during, preceding and after the demand response event, as well as the change in energy used. An important feature is that due to the introduction of a thermal model in our analysis, the energy recovery (“energy pay-back” – increase in consumption of energy after the control period application of a load shift) can be also determined by comparison with a reference case where there is no load control.

The model also simulates changes in the magnitude and timing of peak load and during, before and after the load shift. This aspect will provide important information on how much load can be reduced with the introduction of a DR action, but also how much it can increase after the DR action (or before the DR action, if a pre-heating strategy is used).

The simulations are then coupled with real-time-pricing tariffs which represent the actual electric power supply price. Subsequently economic benefits can be calculated at different times of the day and for different weather conditions, taking into account all the physical features described previously.