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Rolling forecasts have been almost overlooked in the renewable energy storageliterature. In this paper, we provide a new approach for handling uncertaintynot just in the accuracy of a forecast, but in the evolution of forecasts overtime. Our approach shifts the focus from modeling the uncertainty in alookahead model to accurate simulations in a stochastic base model. We developa robust policy for making energy storage decisions by creating aparametrically modified lookahead model, where the parameters are tuned in thestochastic base model. Since computing unbiased stochastic gradients withrespect to the parameters require restrictive assumptions, we propose asimulation-based stochastic approximation algorithm based on numericalderivatives to optimize these parameters. While numerical derivatives,calculated based on the noisy function evaluations, provide biased gradientestimates, an online variance reduction technique built in the framework of ourproposed algorithm, will enable us to control the accumulated bias errors andestablish the finite-time rate of convergence of the algorithm. Our numericalexperiments show the performance of this algorithm in finding policiesoutperforming the deterministic benchmark policy.

Stochastic Search for a Parametric Cost Function Approximation: Energy storage with rolling forecasts