By influencing the demand side by means of price signals (Demand Response) additional flexibility potential in electric supply systems can be provided. However, by influencing the demand side typical consumption patterns of previously unaffected consumers are changed. This will lead to increasing uncertainty in load forecasting. This paper deals with the forecast of load time series in consideration of price-based consumption influence. Additional requirements for load forecasting methods resulting from the price elastic consumption behaviour are analysed in this paper. Furthermore, the model residuals of established model approaches will be analysed to explain the disturbance characteristic caused by the price elasticity. Finally, the impact of the model residuals on the load forecast was investigated
The article is dedicated to the analysis of the effects of different price signals on the consumption behavior of household electricity customers. It proposes a systematic analysis process consisting of data preprocessing, different aggregation steps, the analysis with clustering methods and the analysis of time samples. This analysis process was applied to the Olympic Peninsula Project database
Additional flexibilities on the demand side can be obtained by using price signals to change the consumption behavior of household electricity customers. The present contribution proposes a new theoretically motivated demand response model type called virtual storage. First, the basic model structure of several virtual storage models is introduced. All of these models are based on a system of difference equations that describe load reductions/increases in response to price signals. The virtual storage models differ thereafter in how past or prognosis‐based future price information is considered. After a description of a proposed model validation strategy, the model behavior of several virtual storage models is compared with some of the common demand response model types and with real customer responses to a price signal. Thus, a model comparison is performed on the basis of a real smart meter data set (Olympic Peninsula Project).
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