计算机科学
动态规划
可扩展性
水准点(测量)
网格
储能
算法
数学优化
计算机数据存储
随机规划
标杆管理
文件夹
电
分布式计算
功率(物理)
工程类
计算机硬件
数学
几何学
物理
电气工程
金融经济学
业务
经济
营销
数据库
量子力学
地理
大地测量学
作者
Daniel Salas,Warren B. Powell
出处
期刊:Informs Journal on Computing
日期:2017-11-27
卷期号:30 (1): 106-123
被引量:46
标识
DOI:10.1287/ijoc.2017.0768
摘要
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 .
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