Sensing-Driven Energy Purchasing in Smart Grid Cyber-Physical System
Abstract
Distributed and renewable-energy resources are likely to play an important role in the future energy landscape as consumers and enterprise energy users reduce their reliance on the main electricity grid as their source of electricity. Environmental or ambient sensing of parameters such as temperature and humidity, and amount of sunlight and wind, can be used to predict electricity demand from users and supply from renewable sources, respectively. In this paper, we describe a Smart Grid Cyber- Physical System (SG-CPS) comprising sensors that transmit realtime streams of sensed information to predictors of demand and supply of electricity and an optimization-based decision maker that uses these predictions together with real-time grid electricity prices and historical information to determine the quantity and timing of grid electricity purchases throughout the day and night. We investigate two forms of the optimization-based decision maker, one that uses linear programming and another that uses multi-stage stochastic programming. Our results show that sensing-driven predictions combined with the optimization-based purchasing decision maker hosted on the SG-CPS platform can cope well with uncertainties in demand, supply, and electricity prices and make grid electricity purchasing decisions that successfully keep both the occurrence of electricity shortfalls and the cost of grid electricity purchases low. We then examine the computational and memory requirements of the aforementioned prediction and optimization algorithms and find that they are within the capabilities of modern embedded system microprocessors and, hence, are amenable for deployment in typical households and communities.
Recommended Citation
C. Tham and T. T. Luo, "Sensing-Driven Energy Purchasing in Smart Grid Cyber-Physical System," IEEE Transactions on Systems, Man, and Cybernetics:Systems and Humans, vol. 43, no. 4, pp. 773 - 784, Institute of Electrical and Electronics Engineers (IEEE), Jul 2013.
The definitive version is available at https://doi.org/10.1109/TSMCA.2012.2224337
Department(s)
Computer Science
Keywords and Phrases
Cyber-Physical System (CPS); Distributed embedded system; Energy management system; Optimization; Sensor networks; Smart Grid
International Standard Serial Number (ISSN)
1083-4427
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2013 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jul 2013
