Real-Time Modeling and Control of a Cyber-Physical Energy System

This paper introduces an approach for applying real-time scheduling techniques to balance electric loads in cyber-physical energy systems. The proposed methodology aims to determine, guarantee and optimize an upper bound on the peak load of electric power, which represents a desirable feature for both the electricity supplier and the user of the electrical system. For this purpose, networked electric devices are modeled using parameters derived from the real-time scheduling discipline used for computing systems. Therefore, the upper bound can be enforced by predictably and timely switching on/off the electric devices composing the electrical system. The paper contribution include: the illustration of the relevance of electric load balancing in cyber-physical energy systems, motivating the use of real-time scheduling techniques to achieve predictability of electric loads scheduling; the presentation of a novel and powerful modeling methodology of the physical system based on a set of periodically activated loads, to enable the use of traditional real-time system models and scheduling algorithms, with adequate adaptations, to manage loads activation/deactivation. We finally derive interesting properties of real-time parameters and provide theoretical results concerning the computation of their values. Keywords-Cyber-Physical Energy Systems; Load Balancing; Real-Time; Modeling; Peak Load

[1]  Lothar Thiele,et al.  Real-time calculus for scheduling hard real-time systems , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[2]  Enrico Bini,et al.  Reducing the Peak Power through Real-Time Scheduling Techniques in Cyber-Physical Energy Systems , 2010 .

[3]  Richard Hampshire Realising the benefits of smart metering: Creating consumer engagement , 2009 .

[4]  Pandian Vasant,et al.  The Microchp Scheduling Problem , 2009 .

[5]  R.E. Brown,et al.  Impact of Smart Grid on distribution system design , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[6]  Insup Lee,et al.  Opportunities and Obligations for Physical Computing Systems , 2005, Computer.

[7]  Chung Laung Liu,et al.  Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment , 1989, JACM.

[8]  Bradley Reaves,et al.  Engineering future cyber-physical energy systems: Challenges, research needs, and roadmap , 2009, 41st North American Power Symposium.

[9]  Holger Hermanns,et al.  Future design challenges for electric energy supply , 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

[10]  Kang G. Shin,et al.  Scheduling of Battery Charge, Discharge, and Rest , 2009, 2009 30th IEEE Real-Time Systems Symposium.

[11]  Rami G. Melhem,et al.  Power-aware scheduling for periodic real-time tasks , 2004, IEEE Transactions on Computers.

[12]  Giorgio Buttazzo,et al.  Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications , 1997 .

[13]  L.L. Lai,et al.  Smart metering in micro-grid applications , 2009, 2009 IEEE Power & Energy Society General Meeting.

[14]  Jean-Yves Le Boudec,et al.  Network Calculus: A Theory of Deterministic Queuing Systems for the Internet , 2001 .

[15]  Giorgio C. Buttazzo,et al.  FTT-Ethernet: a flexible real-time communication protocol that supports dynamic QoS management on Ethernet-based systems , 2005, IEEE Transactions on Industrial Informatics.

[16]  Ralph Turvey,et al.  Peak-Load Pricing , 1968, Journal of Political Economy.