Forecasted chronological Power Flow for enabling timely dynamic tariff activation

Abstract

The introduction of sensors and smart meters in power networks has leveraged the use of several other technologies with the aim of improving efficiency of grid operation and maximizing the benefit for all the stakeholders. One way to improve efficiency is to foster a better utilization of the existing assets which delays the investment on new ones. Dynamic tariffs are tariffs whose prices change as a function of certain constraints (time-related or network operation-related). The goal of their utilization is to try to induce a load shift from peak periods thus decreasing the peak value.In order to anticipate the need of shifting load, besides a favourable regulatory environment and the adherence of consumers to this kind of strategies, the Distribution System Operator (DSO) needs to forecast when, in the future, will a peak period occur. To do so, it is needed to develop load and generation forecasting models, scale them on Big Data Infrastructures and integrate the results into power networks analysis tools capable of processing the amount of data within the time constraints for the analysis (if it takes too long to process the information may be useless).