PROJECT TITLE :

Models and Techniques for Electric Load Forecasting in the Presence of Demand Response

ABSTRACT:

Demand-aspect management has been recently recognized as a strategic concept in smart electricity grids. During this context, active demand (AD) represents a demand response state of affairs in that households and tiny commercial consumers participate in grid management through applicable modifications of their consumption patterns throughout certain time periods in come back of a financial reward. The participation is mediated by a new player, known as aggregator, who styles the consumption pattern modifications to make up standardized products to be sold on the energy market. The presence of this new input to consumers generated by aggregators modifies the load behavior, posing for load forecasting algorithms that explicitly take into account the AD result. In this paper, we propose an approach to load forecasting within the presence of AD, based on gray-box models where the seasonal part of the load is extracted by a appropriate preprocessing and AD is considered as an exogenous input to a linear transfer function model. The approach is believed for a distribution system operator that performs technical validation of AD product, and therefore possesses full data about the AD schedule in the network. A comparison of the performance of the proposed approach with techniques not using the information on AD and with approaches based mostly on nonlinear black-box models is performed on a true load time series recorded in an area of the Italian low voltage network.


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