Article | Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017 | A Platform for the Agent-based Control of HVAC Systems Linköping University Electronic Press Conference Proceedings
Göm menyn

Title:
A Platform for the Agent-based Control of HVAC Systems
Author:
Roozbeh Sangi: Institute for Energy Efficient Buildings and Indoor Climate, E.ON Energy Research Center, RWTH Aachen University, Germany Felix Bünning: Institute for Energy Efficient Buildings and Indoor Climate, E.ON Energy Research Center, RWTH Aachen University, Germany Johannes Fütterer: Institute for Energy Efficient Buildings and Indoor Climate, E.ON Energy Research Center, RWTH Aachen University, Germany Dirk Müller: Institute for Energy Efficient Buildings and Indoor Climate, E.ON Energy Research Center, RWTH Aachen University, Germany
DOI:
10.3384/ecp17132799
Download:
Full text (pdf)
Year:
2017
Conference:
Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017
Issue:
132
Article no.:
087
Pages:
799-808
No. of pages:
10
Publication type:
Abstract and Fulltext
Published:
2017-07-04
ISBN:
978-91-7685-575-1
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


Export in BibTex, RIS or text

The amount of energy used for heating and cooling in the building sector is about one third of the total energy consumed in the world. The finiteness of natural energy resources on the one hand, and the ever-increasing demand for energy in the world on the other hand, necessitate the development of systematic approaches for improving the efficiency of building energy systems as well as minimizing the usage of primary energy resources and the damaging impacts on the environment. Attempts to tackle these problems have led to modern complex energy concepts for buildings, which have consequently initiated a need for new control strategies for them. Multi-agent control, which is known with other names like agent-based control, offers a promising solution to these challenges. To the knowledge of the authors, there are 96 platforms in different programming languages available, which are mostly java-based and mainly used in logistic applications, but there is no platform in the modeling language Modelica, which is widely used for simulation of dynamic systems, especially buildings performance simulation. This lack motivated the authors to develop a platform for agent-based control of HAVC systems. The platform eliminates the dependency of models developed in Modelica on an extra interface, which is usually required to couple the models to the platforms written in any programming languages other than Modelica. This paper presents the structure of the platform and explains how the agents’ communications work. The flexibility of the optimization objective is ensured through the definition of readily interchangeable cost functions. The applicability and functionality of the platform are proved by applying the platform in the control of building energy systems examples.

Keywords: Agent-based control, Building energy systems, Control, HVAC, Modelica, Multi-Agent System

Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017

Author:
Roozbeh Sangi, Felix Bünning, Johannes Fütterer, Dirk Müller
Title:
A Platform for the Agent-based Control of HVAC Systems
DOI:
http://dx.doi.org/10.3384/ecp17132799
References:

Afram, A. and Janabi-Sharifi, F. (2014). Theory and applications of hvac control systems - review of model predictive control (mpc). Building and Environment, 72:343–355.

Ali, M., Vukovic, V., Sahir, M. H., and Fontanella, G. (2013). Energy analysis of chilled water system configurations using simulation-based optimization. Energy and Buildings, 59:111–122.

Allan, R. (2010). Survey of agent based modelling and simulation tools. Technical report, Science and Technology Facilities Council.

Ansari, J., Gholami, A., and Kazemi, A. (2016). Multi-agent systems for reactive power control in smart grids. International Journal of Electrical Power & Energy Systems, 83:411–425.

Bellifemine, F. L., Caire, G., and Greenwood, D. (2007). Developing Multi-Agent Systems with JADE. Wiley Series in Agent Technology. Wiley.

Bünning, F. (2015). Development of a modelica-library for the agent-based control of hvac systems. Bachelorthesis, RWTH Aachen University.

Caire, G. (2009). Jade tutorial - jade programming for beginners. TILAB.

Dassault Systemes (2016). Dymola. http://www.3ds.com/productsservices/catia/products/dymola.

Davidsson, P. and Boman, M. (2000). Saving energy and providing value added services in intelligent buildings: A mas approach. In Agent Systems, Mobile Agents, and Applications, pages 166–177. Springer.

Divenyi, D. (2013). Agent-based modeling of distributed generation in power system control. IEEE Transactions on Sustainable Energy, 4:886–889.

FIPA (2002a). Fipa acl message structure specification. FIPA (2002b). Fipa communicative act library specification.

FIPA (2002c). Fipa contract net interaction protocol specification.

Fuchs, M., Teichmann, J., Lauster, M., Remmen, P., Streblow, R., and Müller, D. (2016). Workflow automation for combined modeling of buildings and district energy systems. Energy.

Huber, M., Brust, S., Schütz, T., Constantin, A., Streblow, R., and Müller, D. (2015). Purely agent based control of building energy supply systems. In ECOS - International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems.

Huberman, B. A. and Clearwater, S. H. (1995). A multi-agent system for controlling building environments. In ICMAS, pages 171–176.

Hurtado, L., Nguyen, P., and Kling, W. (2015). Smart grid and smart building inter-operation using agent-based particle swarm optimization. Sustainable Energy, Grids and Networks, 2:32–40.

Jiang, Z. (2006). Agent-based control framework for distributed energy resources microgrids. In IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

Karavas, C.-S., Kyriakarakos, G., Arvanitis, K. G., and Papadakis, G. (2015). A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids. Energy Conversion and Management, 103:166–179.

Khan, M. R. B., Jidin, R., and Pasupuleti, J. (2016). Multiagent based distributed control architecture for microgrid energy management and optimization. Energy Conversion and Management, 112:288–307.

Kirn, S. (2002). Kooperierende intelligente softwareagenten. Wirtschaftsinformatik, 44(1):53–63.

Kok, K., Roossien, B., MacDougall, P., van Pruissen, O., Venekamp, G., Kamphuis, R., Laarakkers, J., and Warmer, C. (2012). Dynamic pricing by scalable energy management systems field experiences and simulation results using powermatcher. In Power and Energy Society General Meeting, 2012 IEEE, pages 1–8. IEEE.

Kuznetsova, E., Li, Y.-F., Ruiz, C., and Zio, E. (2014). An integrated framework of agent-based modelling and robust optimization for microgrid energy management. Applied Energy, 129:70–88.

Mokhtar, M., Liu, X., and Howe, J. (2014). Multi-agent gaussian adaptive resonance theory map for building energy control and thermal comfort management of uclan’s westlakes samuel lindow building. Energy and Buildings, 80:504–516.

Mokhtar, M., Stables, M., Liu, X., and Howe, J. (2013). Intelligent multi-agent system for building heat distribution control with combined gas boilers and ground source heat pump. Energy and Buildings, 62:615–626.

Otter, M., Arzen, K., and Dressler, I. (2005). Stategraph - a modelica library for hierarchical state machines. In Proceedings of the 4th International Modelica Conference, pages 569–578.

Perera, D., Winkler, D., and Skeie, N.-O. (2016). Multi-floor building heating models in matlab and modelica environments. Applied Energy, 171:46–57.

Qiao, B., Liu, K., and Guy, C. (2006). A multi-agent system for building control. In Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology, pages 653–659. IEEE Computer Society.

Radhakrishnan, B. M. and Srinivasan, D. (2016). A multi-agent based distributed energy management scheme for smart grid applications. Energy, 103:192–204.

Rahman, M., Mahmud, M., Oo, A., Pota, H., and Hossain, M. (2016). Agent-based reactive power management of power distribution networks with distributed energy generation. Energy Conversion and Management, 120:120–134.

Rosenschein, J. (1985). Rational Interaction: Cooperation among Intelligent Agents. PhD thesis, Stanford University. RWTH-EBC (2015). Aixlib - a modelica model library for building performance simulations. https://github.com/rwthebc/aixlib.

Sangi, R., Baranski, M., Oltmanns, J., Streblow, R., and Müller, D. (2016). Modeling and simulation of the heating circuit of a multi-functional building. Energy and Buildings, 110:13–22.

Sangi, R., Streblow, R., and Müller, D. (2014). Approaches for a fair exergetic comparison of renewable and non-renewable building energy systems. In The 27th international conference on efficiency, cost, optimization, simulation and environmental impact of energy systems. Turku, Finland.

Thiele, B. and Bellmann, T. (2015). Modelica DeviceDrivers. https://github.com/modelica/Modelica_DeviceDrivers.

van Pruissen, O., van der Togt, A., and Werkman, E. (2014). Energy efficiency comparison of a centralized and a multiagent market based heating system in a field test. Energy Procedia, 62:170–179.

Verein Deutscher Ingenieure (2010). Vdi 2653 blatt 1.

Wang, Z., Wang, L., Dounis, A. I., and Yang, R. (2012). Multi- agent control system with information fusion based comfort model for smart buildings. Applied Energy, 99:247–254.

Wang, Z., Yang, R., and Wang, L. (2011). Intelligent multiagent control for integrated building and micro-grid systems. In Innovative Smart Grid Technologies (ISGT), 2011 IEEE PES, pages 1–7. IEEE.

Wernstedt, F. (2005). Multi-Agent Systems for Distributed Control of District Heating Systems. PhD thesis, Blekinge Institute of Technology, Department of Systems and Software Engineering.

Wetter, M., Zuo, W., Nouidui, T. S., and Pang, X. (2014). Modelica buildings library. Journal of Building Performance Simulation, 7(4):253–270.

Xydas, E., Marmaras, C., and Cipcigan, L. M. (2016). A multiagent based scheduling algorithm for adaptive electric vehicles charging. Applied Energy, 177:354–365.

Yang, R. and Wang, L. (2013). Development of multi-agent system for building energy and comfort management based on occupant behaviors. Energy and Buildings, 56:1–7.

Ye, D., Zhang, M., and Sutanto, D. (2015). Decentralised dispatch of distributed energy resources in smart grids via multiagent coalition formation. Journal of Parallel and Distributed Computing, 83:30–43.

Proceedings of the 12th International Modelica Conference, Prague, Czech Republic, May 15-17, 2017

Author:
Roozbeh Sangi, Felix Bünning, Johannes Fütterer, Dirk Müller
Title:
A Platform for the Agent-based Control of HVAC Systems
DOI:
https://doi.org10.3384/ecp17132799
Note: the following are taken directly from CrossRef
Citations:
  • Roozbeh Sangi, Pooyan Jahangir & Dirk Müller (2019). A combined moving boundary and discretized approach for dynamic modeling and simulation of geothermal heat pump systems. Thermal Science and Engineering Progress, 9: 215. DOI: 10.1016/j.tsep.2018.11.015


  • Responsible for this page: Peter Berkesand
    Last updated: 2019-11-06