Energy management with the support of dynamic pricing strategies in real micro-grid scenarios

Abstract

Although smart grids are regarded as the technology to overcome the limits of nowadays power distribution grids, the transition will require much time. Dynamic pricing, a straightforward implementation of demand response, may provide the means to manipulate the grid load thus extending the life expectancy of current technology. However, to integrate a dynamic pricing scheme in the crowded pool of technologies, available at demand side, a proper energy manager with the support of a pricing profile forecaster is mandatory. Although energy management and price forecasting are recurrent topics, in literature they have been addressed separately. On the other hand, in this work, the aim is to investigate how well an energy manager is able to perform in presence of data uncertainty originating from the forecasting process. On purpose, an energy and resource manager has been revised and extended in the current manuscript. Finally, it has been complemented with a price forecasting technique, based on the Extreme Learning Machine paradigm. The proposed forecaster has proven to be better performing and more robust, with respect to the most common forecasting approaches. The energy manager, as well, has proven that the energy efficiency of the residential environment can be improved significantly. Nonetheless, to achieve the theoretical optimum, forecasting techniques tailored for that purpose may be required.