Dynamic Energy Management System for a Smart Microgrid

被引：240

作者：

Venayagamoorthy, Ganesh Kumar ^{[1
,2
]}

Sharma, Ratnesh K. ^{[3
]}

Gautam, Prajwal K. ^{[1
,4
]}

Ahmadi, Afshin ^{[1
,5
]}

机构：

[1] Clemson Univ, Real Time Power & Intelligent Syst Lab, Clemson, SC 29634 USA

[2] Univ KwaZulu Natal, Eskom Ctr Excellence HVDC Engn, ZA-4041 Durban, South Africa

[3] NEC Labs Amer Inc, Energy Management Dept, Cupertino, CA 95014 USA

[4] Spirae Inc, Ft Collins, CO 80524 USA

[5] Clemson Univ, Dept Elect & Comp Engn, Clemson, SC 29634 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2016年 / 27卷 / 08期

基金：

美国国家科学基金会;

关键词：

Adaptive dynamic programming; dynamic energy management system (DEMS); evolutionary computing; microgrid; neural networks; reinforcement learning; renewable energy; OPERATION; STORAGE;

D O I：

10.1109/TNNLS.2016.2514358

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents the development of an intelligent dynamic energy management system (I-DEMS) for a smart microgrid. An evolutionary adaptive dynamic programming and reinforcement learning framework is introduced for evolving the I-DEMS online. The I-DEMS is an optimal or near-optimal DEMS capable of performing grid-connected and islanded microgrid operations. The primary sources of energy are sustainable, green, and environmentally friendly renewable energy systems (RESs), e.g., wind and solar; however, these forms of energy are uncertain and nondispatchable. Backup battery energy storage and thermal generation were used to overcome these challenges. Using the I-DEMS to schedule dispatches allowed the RESs and energy storage devices to be utilized to their maximum in order to supply the critical load at all times. Based on the microgrid's system states, the I-DEMS generates energy dispatch control signals, while a forward-looking network evaluates the dispatched control signals over time. Typical results are presented for varying generation and load profiles, and the performance of I-DEMS is compared with that of a decision tree approach-based DEMS (D-DEMS). The robust performance of the I-DEMS was illustrated by examining microgrid operations under different battery energy storage conditions.

引用

页码：1643 / 1656

页数：14