Benchmarking a Scalable Approximate Dynamic Programming Algorithm for Stochastic Control of Grid-Level Energy Storage
提出并测试了一种近似动态规划算法，能为异构储能设备组合设计近最优控制策略，在随机模型上误差仅0.86%，并分析了双储能系统的高低频使用策略。
We present and benchmark an approximate dynamic programming algorithm that is capable of designing near-optimal control policies for a portfolio of heterogenous storage devices in a time-dependent environment, 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 on deterministic models, and within 0.86% on stochastic models. 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 distributed over the grid and more complex storage networks. The online supplement is available at https://doi.org/10.1287/ijoc.2017.0768 .