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

TL;DR

This paper introduces a novel stochastic search method for optimizing parametric cost functions in energy storage, accounting for forecast evolution uncertainty, and demonstrates its effectiveness over deterministic benchmarks.

Contribution

It develops a simulation-based stochastic approximation algorithm with variance reduction for tuning parameters in a robust energy storage policy under forecast uncertainty.

Findings

Algorithm outperforms deterministic benchmark policies

Effective control of bias errors in stochastic gradient estimates

Finite-time convergence of the proposed optimization method

Abstract

Rolling forecasts have been almost overlooked in the renewable energy storage literature. In this paper, we provide a new approach for handling uncertainty not just in the accuracy of a forecast, but in the evolution of forecasts over time. Our approach shifts the focus from modeling the uncertainty in a lookahead model to accurate simulations in a stochastic base model. We develop a robust policy for making energy storage decisions by creating a parametrically modified lookahead model, where the parameters are tuned in the stochastic base model. Since computing unbiased stochastic gradients with respect to the parameters require restrictive assumptions, we propose a simulation-based stochastic approximation algorithm based on numerical derivatives to optimize these parameters. While numerical derivatives, calculated based on the noisy function evaluations, provide biased gradient estimates, an online variance reduction technique built in the framework of our proposed algorithm, will enable us to control the accumulated bias errors and establish the finite-time rate of convergence of the algorithm. Our numerical experiments show the performance of this algorithm in finding policies outperforming the deterministic benchmark policy.