Multiple energy carrier optimisation with intelligent agents

Multiple energy carrier systems stem from the need to evolve traditional electricity, gas and other energy systems to more efficient, integrated energy systems. An approach is presented, for controlling multiple energy carriers, including electricity (AC or DC), heat, natural gas and hydrogen, with the objective to minimise the overall cost and/or emissions, while adhering to technical and commercial constraints, such as network limits and market contracts. The technique of multi-agent systems (MAS) was used. The benefits of this approach are discussed and include a reduction of more than 50% in the balancing costs of a potential deviation. An implementation of this methodology is also presented. In order to validate the operation of the developed system, a number of experiments were performed using both software and hardware. The results validated the efficient operation of the developed system, proving its ability to optimise the operation of multiple energy carrier inputs within the context of an energy hub, using a hierarchical multi-agent system control structure.

[1]  Jianzhong Wu,et al.  Combined gas and electricity network expansion planning , 2014 .

[2]  Jianzhong Wu,et al.  A total energy approach to integrated community infrastructure design , 2011, 2011 IEEE Power and Energy Society General Meeting.

[3]  Kristina Orehounig,et al.  Integration of decentralized energy systems in neighbourhoods using the energy hub approach , 2015 .

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

[5]  Taha Selim Ustun,et al.  Recent developments in microgrids and example cases around the world—A review , 2011 .

[6]  G. Andersson,et al.  Optimal Power Flow of Multiple Energy Carriers , 2007, IEEE Transactions on Power Systems.

[7]  Pierluigi Mancarella,et al.  Distributed multi-generation: A comprehensive view , 2009 .

[8]  Barbara Messing,et al.  An Introduction to MultiAgent Systems , 2002, Künstliche Intell..

[9]  Spyros Skarvelis-Kazakos,et al.  Agent-based control of multiple energy carriers and energy hubs , 2014, IEEE PES Innovative Smart Grid Technologies, Europe.

[10]  Koen Steemers,et al.  Can microgrids make a major contribution to UK energy supply , 2006 .

[11]  G. Andersson,et al.  Demand Management of Grid Connected Plug-In Hybrid Electric Vehicles (PHEV) , 2008, 2008 IEEE Energy 2030 Conference.

[12]  Ivo Bouwmans,et al.  Agent-based control of distributed electricity generation with micro combined heat and power - Cross-sectoral learning for process and infrastructure engineers , 2008, Comput. Chem. Eng..

[13]  K. Strunz,et al.  Virtual Power Plant Control concepts with Electric Vehicles , 2011, 2011 16th International Conference on Intelligent System Applications to Power Systems.

[14]  Goran Strbac,et al.  Multi-time period combined gas and electricity network optimisation , 2008 .

[15]  P. Favre-Perrod,et al.  A vision of future energy networks , 2005, 2005 IEEE Power Engineering Society Inaugural Conference and Exposition in Africa.

[16]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[17]  Liana Mirela Cipcigan,et al.  Micro-generation for 2050: emissions performances of micro-generation sources during operation , 2009 .

[18]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[19]  Athula D. Rajapakse,et al.  Microgrids research: A review of experimental microgrids and test systems , 2011 .

[20]  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.

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

[22]  G. Andersson,et al.  Towards multi-source multi-product energy systems , 2007 .

[23]  Audrius Bagdanavicius,et al.  Combined analysis of electricity and heat networks , 2014 .

[24]  M. Hernandez-Tejera,et al.  Infrastructure based on supernodes and software agents for the implementation of energy markets in demand-response programs , 2015 .

[25]  Kai Strunz,et al.  A BENCHMARK LOW VOLTAGE MICROGRID NETWORK , 2005 .

[26]  Nick Jenkins,et al.  Carbon optimized Virtual Power Plant with Electric Vehicles , 2010, 45th International Universities Power Engineering Conference UPEC2010.

[27]  Alfredo Núñez,et al.  Load profile generator and load forecasting for a renewable based microgrid using Self Organizing Maps and neural networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[28]  Daniel Favrat,et al.  Multi-criteria optimization of a district cogeneration plant integrating a solid oxide fuel cell–gas turbine combined cycle, heat pumps and chillers , 2003 .

[29]  Enrico Zio,et al.  An integrated framework of agent-based modelling and robust optimization for microgrid energy management , 2014 .

[30]  Nick Jenkins,et al.  Coordination of the Charging of Electric Vehicles Using a Multi-Agent System , 2013, IEEE Transactions on Smart Grid.

[31]  Evangelos Rikos,et al.  Implementing agent-based emissions trading for controlling Virtual Power Plant emissions , 2013 .

[32]  C. Martin.  2015 , 2015, Les 25 ans de l’OMC: Une rétrospective en photos.

[33]  Danny Pudjianto,et al.  Virtual power plant and system integration of distributed energy resources , 2007 .

[34]  A.L. Dimeas,et al.  Agent based control of Virtual Power Plants , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[35]  Wolfgang Ketter,et al.  The Power Trading Agent Competition , 2011 .

[36]  G. Andersson,et al.  Energy hubs for the future , 2007, IEEE Power and Energy Magazine.

[37]  Hongwei Li,et al.  Thermal-economic optimization of a distributed multi-generation energy system¿A case study of Beijing , 2006 .

[38]  Panagiotis Papadopoulos,et al.  Management of electric vehicle battery charging in distribution networks with multi-agent systems , 2014 .

[39]  Pierluigi Mancarella,et al.  Multi-energy systems : An overview of concepts and evaluation models , 2015 .

[40]  Alfredo Vaccaro,et al.  Multiple-Energy Carriers: Modeling of Production, Delivery, and Consumption , 2011, Proceedings of the IEEE.

[41]  Mahmud Fotuhi-Firuzabad,et al.  Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability , 2014, IEEE Transactions on Sustainable Energy.

[42]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[43]  Siobhán Clarke,et al.  Residential electrical demand forecasting in very small scale: An evaluation of forecasting methods , 2013, 2013 2nd International Workshop on Software Engineering Challenges for the Smart Grid (SE4SG).

[44]  Ruzhu Wang,et al.  Energy optimization model for a CCHP system with available gas turbines , 2005 .

[45]  Alfredo Vaccaro,et al.  A robust optimization approach to energy hub management , 2012 .

[46]  Goran Andersson,et al.  Integration of Plug-In Hybrid Electric Vehicles into energy networks , 2009, 2009 IEEE Bucharest PowerTech.