Agent-based control of distributed electricity generation with micro combined heat and power - Cross-sectoral learning for process and infrastructure engineers

For the distributed control of an electricity infrastructure incorporating clusters of residential combined heat and power units (micro-CHP or ?CHP) a Multi-Agent System approach is considered. The network formed by households generating electricity with ?CHP units and the facilitating energy supplier can be regarded as an electricity production system, analogous to a (flexible) manufacturing system. Next, the system boundary is extended by allowing the trade of electricity between networks of households and their supplier. A methodology for designing an agent-based system for manufacturing control is applied to both cases, resulting in a conceptual design for a control system for the energy infrastructure. Because of the analogy between production systems and infrastructures Process Systems Engineering (PSE) approaches for optimisation and control can be applied to infrastructure system operations. At the same time we believe research on socio-technical infrastructure systems will be a valuable contribution to PSE management strategies.

[1]  Stephanie Hamilton,et al.  Distributed Generation: A Nontechnical Guide , 2001 .

[2]  Panagiotis D. Christofides,et al.  Coordinating feedback and switching for control of hybrid nonlinear processes , 2003 .

[3]  Pieter J. Beers,et al.  Facilitating Interdisciplinary Modelling of Complex Problems , 2007, 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07).

[4]  Nicholas R. Jennings,et al.  Modelling Distributed Industrial Processes in a Multi-Agent Framework , 1997 .

[5]  Michael C. Fu,et al.  Guest editorial , 2003, TOMC.

[6]  M Newborough,et al.  Assessing the benefits of implementing micro-CHP systems in the UK , 2004 .

[7]  Panagiotis D. Christofides,et al.  Fault‐tolerant control of process systems using communication networks , 2005 .

[8]  Z. Lukszo,et al.  Towards a Generic Approach for Analyzing the Efficiency of Complex Networks , 2006, 2006 IEEE International Conference on Networking, Sensing and Control.

[9]  Ivo Bouwmans,et al.  Socio-Technical Complexity in Energy Infrastructures Conceptual Framework to Study the Impact of Domestic Level Energy Generation, Storage and Exchange , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[10]  Ignacio E. Grossmann Challenges in the new millennium: Product discovery and design, enterprise and supply chain optimization, global life cycle assessment , 2003 .

[11]  Steinar Hauan,et al.  Toward agent-based process systems engineering: proposed framework and application to non-convex optimization , 2003, Comput. Chem. Eng..

[12]  Rajagopalan Srinivasan,et al.  Agent-based supply chain management—2: a refinery application , 2002 .

[13]  Marija D. Ilic,et al.  Challenges for process system engineering in infrastructure operation and control , 2006 .

[14]  Michael P. Wellman,et al.  Planning and Control , 1991 .

[15]  Michal Pechoucek,et al.  Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems , 2005, AAMAS 2005.

[16]  Weiming Shen,et al.  Applications of agent-based systems in intelligent manufacturing: An updated review , 2006, Adv. Eng. Informatics.

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

[18]  Henrik Eriksson,et al.  The evolution of Protégé: an environment for knowledge-based systems development , 2003, Int. J. Hum. Comput. Stud..

[19]  Michael J. North,et al.  Experiences creating three implementations of the repast agent modeling toolkit , 2006, TOMC.

[20]  H. Van Dyke Parunak,et al.  Industrial and practical applications of DAI , 1999 .

[21]  E. J. Bakker,et al.  Technisch energie- en CO2-besparingspotentieel van micro-wkk in Nederland (2010-2030). , 2006 .

[22]  David Riaño,et al.  The scope of application of multi-agent systems in the process industry: three case studies , 2004, Expert Syst. Appl..

[23]  Gerard P. J. Dijkema,et al.  An Agent Based Model of the System of Electricity Production Systems: Exploring the Impact of CO2 Emission-Trading , 2007, 2007 IEEE International Conference on System of Systems Engineering.

[24]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993, Knowl. Acquis..

[25]  Stefan Kirn,et al.  Cooperative Knowledge Processing , 1997, Computer Supported Cooperative Work.

[26]  Jaap A. Ottjes,et al.  Distributed intelligence in autonomous multi-vehicle systems , 2006, Int. J. Crit. Infrastructures.

[27]  Zofia Lukszo,et al.  Modelling Energy and Transport Infrastructures as a Multi-Agent System using a Generic Ontology , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[28]  Zofia Lukszo,et al.  Modelling an electricity infrastructure as a multi-agent system — Lessons learnt from manufacturing control , 2006 .

[29]  Nicholas R. Jennings,et al.  Intelligent agents: theory and practice , 1995, The Knowledge Engineering Review.

[30]  Igor Nikolic,et al.  Understanding and Shaping the Evolution of Sustainable Large-Scale Socio-Technical Systems , 2009 .

[31]  Dr. Stefan Bussmann,et al.  Multiagent Systems for Manufacturing Control , 2004, Springer Series on Agent Technology.

[32]  B. De Schutter,et al.  Least-cost model predictive control of residential energy resources when applying μmCHP , 2007, 2007 IEEE Lausanne Power Tech.

[33]  Ai Su Exploring the Unknown Market; The anticipated diffusion of domestic micro-combined heat and power (CHP) in the Netherlands , 2005 .

[34]  Woflgang Marquardt,et al.  16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering , 2006 .

[35]  G.P.J. Dijkema,et al.  Intelligent infrastructures in a changing environment: innovation and evolution of industry-infrastructure networks , 2005, Proceedings. 2005 IEEE Networking, Sensing and Control, 2005..

[36]  G. Andersson,et al.  A modeling and optimization approach for multiple energy carrier power flow , 2005, 2005 IEEE Russia Power Tech.