Optimal Control of Smart Home Energy Management Based on Stochastic Dynamic Programming

被引：0

作者：

Jiang, Lanhai ^{[1
]}

Tang, Hao ^{[1
]}

Jiang, Qi ^{[1
]}

Zhou, Lei ^{[2
]}

机构：

[1] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei, Anhui, Peoples R China

[2] Hefei Univ Technol, Sch Comp & Informat, Hefei, Anhui, Peoples R China

来源：

2014 11TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2014年

关键词：

Smart Home; Energy Management; Semi-Markov Decision Process; SARSA-algorithm; OPTIMIZATION;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The optimal energy management problem of smart home is concerned. The objective is to maximize reward of smart home with distributed generation, through choosing a suitable energy management control strategy by stochastic dynamic programming. Firstly, we modeled the smart home energy management system as a semi-Markov decision process according to the stochastic characteristics of solar photovoltaic, load demand and electricity price. We apply policy iteration algorithm to solve the optimal control of energy management. Then, SARSA algorithm based on simulated annealing is applied to optimize the energy management strategy for the system. Finally, a simulation example is used to illustrate the effectiveness of the algorithm, and the results show that the algorithm can effectively increased the user rewards.

引用

页码：570 / 575

页数：6

共 15 条

[1]

Borenstein S, 2002, Dynamic pricing, advanced metering and demand response in electricity markets

[2] Semi-Markov decision problems and performance sensitivity analysis [J].

Cao, XR .

IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2003, 48 (05) :758-769

[3] Control Methods of Inverter-Interfaced Distributed Generators in a Microgrid System [J].

Chung, Il-Yop ;

Liu, Wenxin ;

Cartes, David A. ;

Collins, Emmanuel G., Jr. ;

Moon, Seung-Il .

IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2010, 46 (03) :1078-1088

[4]

Danilo F., 2013, INT J ELEC POWER, V48, P148

[5]

Danilo F., 2012, OPTIMAL BATTERY MANA, P355

[6] Modified Particle Swarm Optimization Applied to Integrated Demand Response and DG Resources Scheduling [J].

Faria, Pedro ;

Soares, Joao ;

Vale, Zita ;

Morais, Hugo ;

Sousa, Tiago .

IEEE TRANSACTIONS ON SMART GRID, 2013, 4 (01) :606-616

[7]

Francesco D. A., 2012, OPTIMAL TASK ENERGY, V7367, P650

[8]

Huang T, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P119, DOI 10.1109/IJCNN.2011.6033209

[9] Particle Swarm Optimization Based Noncoherent Detector for Ultra-Wideband Radio in Intensive Multipath Environments [J].

Li, Bin ;

Zhou, Zheng ;

Zou, Weixia ;

Gao, Wanxin .

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING, 2011,

[10]

Matteo B., 2013, COGN COMPUT, V5, P264

← 1 2 →