Open AccessReal‐time stochastic operation strategy of a microgrid using approximate dynamic programming‐based spatiotemporal decomposition approach
Author(s) -
Zhu Jianquan,
Mo Xiemin,
Zhu Tao,
Guo Ye,
Luo Tianyun,
Liu Mingbo
Publication year - 2019
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.0536
Subject(s) - microgrid , dynamic programming , mathematical optimization , computer science , decomposition , stochastic programming , operator (biology) , renewable energy , control (management) , algorithm , mathematics , engineering , ecology , biochemistry , chemistry , repressor , artificial intelligence , transcription factor , gene , electrical engineering , biology
This study focuses on the real‐time operation of a microgrid (MG). A novel approximate dynamic programming based spatiotemporal decomposition approach is developed to incorporate efficient management of distributed energy storage systems into MG real‐time operation while considering uncertainties in renewable generation. The original dynamic energy management problem is decomposed into single‐period and single‐unit sub‐problems, and the value functions are used to describe the interaction among the sub‐problems. A two‐stage procedure is further designed for the real‐time decisions of those sub‐problems. In the first stage, empirical data is utilised offline to approximate the value functions. Then in the second stage, each sub‐problem can make immediate and independent decision in both temporal and spatial dimensions to mitigate adverse effects of intermittent renewable generation in a MG. No central operator intervention is required, and the near optimal decisions can be obtained at a very fast speed. Case studies based on a six‐bus MG and an actual island MG are conducted to demonstrate the effectiveness of the proposed algorithm.