Low-Rank Value Function Approximation for Co-Optimization of Battery Storage

被引:23
作者
Cheng, Bolong [1 ]
Asamov, Tsvetan [2 ]
Powell, Warren B. [2 ]
机构
[1] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
[2] Princeton Univ, Dept Operat Res & Financial Engn, Princeton, NJ 08544 USA
关键词
Energy storage; frequency regulation; energy arbitrage; low-rank approximation; ENERGY-STORAGE; ALGORITHM;
D O I
10.1109/TSG.2017.2716382
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
We develop a near-optimal solution to the problem of co-optimizing frequency regulation and energy arbitrage with battery storage using backward approximate dynamic programming, which is shown to handle the different time scales of each revenue stream. Solution of the problem using classical backward exact dynamic programming is computationally intractable for this problem due to the large state space and long horizon. Instead, we use state sampling and low-rank approximations to estimate the entire value function, producing a high quality solution that can he computed in real time. The new algorithm is shown to reduce the computational time by one order of magnitude, and the storage requirements by two orders of magnitude, while producing near optimal policies that consistently outperform pure frequency regulation.
引用
收藏
页码:6590 / 6598
页数:9
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