Hedging strategies for renewable resource integration and uncertainty management in the smart grid

Increased environmental and economic concerns have set the stage for an increase in the fraction of electricity supplied using renewable sources. Recent advances in wind prediction offer hope that reduction in the uncertainty of wind availability will lead to an increase in its value. Model based methods that predict future wind availability and then optimize local generation have been seen to be successful for both economic dispatch and demand management. In this paper we evaluate model free hedging strategies for renewable resource integration and uncertainty management in the smart grid. We compare the performance of these two classes of algorithms for intelligent generator scheduling using simple wind speed forecasters in both simulations and on real wind traces. We also suggest that algorithms based on online convex optimization can be applied to demand management problems and evaluate hedging algorithms for smart demand response, highlighting the reduction in costs possible when renewable energy is combined with demand response.