Estimating the impacts of demand response by simulating household behaviours under price and CO2 signals

To facilitate the implementation of demand response (DR), it is necessary to establish proper methods to estimate and verify the load impacts of it. This paper develops a simulation model to investigate the joint influence of price and CO2 signals in a DR program in the ex ante evaluation. It consists of a Markov-chain load model for forecasting the power demands of residential consumers and a scheduling program for providing optimal schedules for smart appliances. A case study of the Stockholm Royal Seaport project is analysed to demonstrate how to apply the simulation model to assess a DR program by simulating consumers' behaviour change in response to the DR signals. The results show that consumers' attitude to the signals and willingness to change (expressed by weight λ and time preference) largely affect the load shift, bill saving and emission reduction. Moreover, by observing the load shifts over different lengths of the testing period, the model could also provide suggestions on the required testing period to get sufficient load data to distinguish the load patterns between consumers in different testing groups.