Prediction based storage management in the smart grid

Economic and environmental concerns have fostered interest in incorporating greater amounts of electricity from renewable energy sources into the grid. Unfortunately, the intermittent availability of renewable power has raised a barrier to the inclusion of these sources. Distributed storage is perceived as a means to extract value from the different resources. However, the large cost of storage requires the design of algorithms that can manage intermittent resources with minimum storage size. At the same time, advances in metering, communication, and weather prediction allow real time management of energy generation, distribution and consumption based on predictions of the future. In this paper, we focus on online algorithms for local storage management that use short term predictions of intermittent renewable resource availability. In contrast to prior work, we develop algorithms that come with theoretical bounds on performance even when demand, prices and availability are arbitrary (possibly non-stochastic), and the utility functions non-concave. Our fundamental contribution is to prove how appropriate discounting of future welfare leads to storage management algorithms that exhibit excellent practical performance even in the worst-case scenario. We substantiate these theoretical guarantees with experiments that demonstrate the effectiveness of our algorithms and the value of storage in the smart grid.