Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Multidimensional Energy Storage Problems

We present and benchmark an approximate dynamic programming algorithm that is capable of designing nearoptimal control policies for time-dependent, finite-horizon energy storage problems, where wind supply, demand and electricity prices may evolve stochastically. We found that the algorithm was able to design storage policies that are within 0.08% of optimal in deterministic comparisons and within 1.34% in stochastic ones, much lower than those obtained using model predictive control. We use the algorithm to analyze a dual-storage system with different capacities and losses, and show that the policy properly uses the low-loss device (which is typically much more expensive) for high-frequency variations. We close by demonstrating the algorithm on a five-device system. The algorithm easily scales to handle heterogeneous portfolios of storage devices distriibuted over the grid and more complex storage networks.