A contribution to the load forecast of price elastic consumption behaviour

Abstract

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

Energy forecasting tools and services

“…However, it is not the main focus of the present contribution and thus it is not discussed further. Nonetheless, information regarding energy price forecasting and the influence that prices have on load time series forecasts in demand response scenarios can be found in the articles by Aggarwal et al, Klaiber et al, Waczowicz et al, and Weron . Another interesting case, which is not discussed further, is the forecast of time series formed by a combination of both generation—via renewable energy systems—and load (e.g., time series measured at a low voltage substation).…”

Investigation and mathematical modelling of the impact of incentive signals to consumers on their consumption, load forecast and network operation

“…1). The workflow is shared amongst them and distributed as follows: ‘Planning process’ denotes the blocks for calculating the OPF and cost‐optimisations. ‘Forecast system’ are the blocks responsible for the consumer behaviour modelling (in [19]) and the aggregation of household busses. ‘Influenced consumer process’ – for predictive modelling (in [14]). …”

“…‘Forecast system’ are the blocks responsible for the consumer behaviour modelling (in [19]) and the aggregation of household busses.…”

“…Modelling framework for consumer model and planning (modified from [19]): index [t] – discrete time t, H – forecast horizon, ΔT – iteration time step, n – household/bus index, p[t] – influencing price signal, u[t] ‐ input variables, u EES [t] – exogenous variables in the electric power system, P[t] – price‐influenced consumer load, P R [t] – price‐influenced component of the consumer load, P U [t] – uninfluenced consumer load, z[t] – disturbance…”

“…The ‘Forecast system’ block is intended to generate forecasts of the uninfluenced load component [19]. Its forecast method uses historical consumption data P [ t − 1]… P [ t − j ] and other available input variables to recalculate the uninfluenced load component.…”