A Smart Energy system: Distributed resource management, control and optimization

This paper presents a novel concept of distributed energy resource and consumption management, which proposes to design a networked and embedded platform for realizing a dynamic energy mix and optimizing the energy consumption dynamically. Based on heterogeneous wireless sensor networks and a local Web of Things platform, the environmental parameters and energy data can be acquired and processed in a distributed manner in real time. In order to improve understanding on how different environmental factors and user behaviors influence the end use of energy, we propose a User Profiling module to investigate the characterization of user's goals and behaviors in terms of energy consumption. Besides the wireless sensor networks, the User Profiling module acquires data also from a questionnaire which mainly concerns four categories, i.e. characteristics of the residents, electrical appliances, attitudes towards energy and building structural information. Furthermore, based on the real-time information from the sensor network platform and the user profiling module, an embedded Resource and Consumption Controller will then adapt automatically for instance the regulation processes of energy consumption in a household locally for the users, so that the costs of all energy resources will not exceed the predetermined budget and be regulated in a user-preferred way.

[1]  Agent-Based Control of Distributed Infrastructure Resources , 2006 .

[2]  J. K. Kok,et al.  PowerMatcher: multiagent control in the electricity infrastructure , 2005, AAMAS '05.

[3]  Vlad Trifa,et al.  Towards the Web of Things: Web Mashups for Embedded Devices , 2009 .

[4]  R. Yokoyama,et al.  An autonomous agent for reliable operation of power market and systems including microgrids , 2008, 2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.

[5]  S.D.J. McArthur,et al.  Multi-Agent Systems for Power Engineering Applications—Part I: Concepts, Approaches, and Technical Challenges , 2007, IEEE Transactions on Power Systems.

[6]  Geert Deconinck,et al.  A multi-agent system architecture for electrical energy matching in a microgrid , 2008 .

[7]  Matthias Budde,et al.  SmartTecO: context-based ambient sensing and monitoring for optimizing energy consumption , 2011, ICAC '11.

[8]  H. Fujita,et al.  A multi-agent approach to distribution system restoration , 2004, The 2004 47th Midwest Symposium on Circuits and Systems, 2004. MWSCAS '04..

[9]  Khosrow Moslehi,et al.  Power System Control Centers: Past, Present, and Future , 2005, Proceedings of the IEEE.

[10]  Rebecca E. Grinter,et al.  Getting to green: understanding resource consumption in the home , 2008, UbiComp.

[11]  A.L. Dimeas,et al.  A MAS architecture for microgrids control , 2005, Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems.

[12]  Adam Dunkels,et al.  Efficient application integration in IP-based sensor networks , 2009, BuildSys '09.

[13]  Jagdish B. Helonde,et al.  A novel approach for Optimal Power Dispatch using Artificial Intelligence (AI) methods , 2009, 2009 International Conference on Control, Automation, Communication and Energy Conservation.

[14]  N. Hatziargyriou,et al.  A multiagent system for microgrids , 2004, IEEE Power Engineering Society General Meeting, 2004..

[15]  M. Newborough,et al.  Energy-use information transfer for intelligent homes : Enabling energy conservation with central and local displays , 2007 .

[16]  Michael Beigl,et al.  Beyond context-awareness: context prediction in an industrial application , 2010, UbiComp '10 Adjunct.

[17]  Robert H. Lasseter Microgrids and Distributed Generation , 2007 .

[18]  Richard M. Murray,et al.  Feedback Systems: An Introduction for Scientists and Engineers , 2008 .

[19]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[20]  Michael Beigl,et al.  Dinam: A wireless sensor network concept and platform for rapid development , 2010, 2010 Seventh International Conference on Networked Sensing Systems (INSS).

[21]  Elvira Kägi-Kolisnychenko Distribution management system including dispersed generation and storage in a liberalized market environment , 2009 .

[22]  Abder Koukam,et al.  Multi-agent systems for grid energy management: A short review , 2010, IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society.

[23]  T. Riedel,et al.  The uPart experience: building a wireless sensor network , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[24]  Mohammed H. Albadi,et al.  Demand Response in Electricity Markets: An Overview , 2007, 2007 IEEE Power Engineering Society General Meeting.

[25]  T. Logenthiran,et al.  Multi-agent coordination for DER in MicroGrid , 2008, 2008 IEEE International Conference on Sustainable Energy Technologies.

[26]  A.L. Dimeas,et al.  Operation of a multiagent system for microgrid control , 2005, IEEE Transactions on Power Systems.

[27]  T. Sauter,et al.  A Flexible Multi-Agent System Architecture for Plant Automation , 2007, 2007 5th IEEE International Conference on Industrial Informatics.

[28]  Jacques Ferber,et al.  Multi-agent systems - an introduction to distributed artificial intelligence , 1999 .

[29]  Stephanie Ropenus,et al.  Distributed Energy Resources and Control: A power system point of view , 2007 .

[30]  Hiroshi Sasaki,et al.  A multiagent approach to distribution system restoration , 2005 .