Date: July, 16th, 10h00
Place: Inria Lille-Nord Europe
Abstract: Demand response is a valuable resource in the smart grid. It has been proved that demand response can improve the reliability of the grid while decreasing load consumption at peak time. Aggregators play a major role in the smart grid: as intermediaries between the utility and the customers, they have to satisfy the requests of the utility while meeting the customers’
constraints. Demand response requests have to be distributed optimally by the aggregators to improve the reliability of the grid at peak time. The uncertainty of the availability of the resources has to be included in the optimization model in order to satisfy the request of the utility. We propose to include this uncertainty in the optimization model using the chance-constrained method. In order to have an accurate optimal solution, aggregators predict the load consumption over a short-term horizon, and commit sufficient demand response requests at minimum cost while ensuring sufficient backup capacity to mitigate the uncertainty. We observe that support vector regression offers a good short-term forecast for the loads in the residential and commercial sector.
This is joint work with Miguel F. Anjos, Laurent Lenoir, and Dalal Asber.