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Multi-agents Planner for Assistance in Conducting Energy Sharing Processes

  • Bilal Bou SalehEmail author
  • Ghazi Bou SalehEmail author
  • Mohammad HajjarEmail author
  • Abdellah El MoudniEmail author
  • Oussama BarakatEmail author
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 146)

Abstract

The purpose of this paper is to present an agents-based methodology that allows for the creation and optimization of schedule while taking into account a wide range of constraints or preferences. When some smart households benefit from a common energy source, if the available power is limited, the problem to be solved for improving energy efficiency is how to program the power-on time of the peripherals according to the power limits and taking into account the preferences of the users. The proposed operating system was developed as multi-agent systems (MAS) on the JADE platform. The implementation is discussed by describing in detail each agent and the control algorithm. In addition, complementary metrics are proposed, to evaluate the performance of the planning method. Finally, to illustrate the proposed method, some simulation results are presented.

Keywords

Multi-agents scheduler Planner Energetic efficiency Smart grid 

References

  1. 1.
    Redl, T.A.: On using graph coloring to create university schedules with essential and preferential conditions. http://cms.uhd.edu/faculty/redlt/iccis09proc.pdf
  2. 2.
    Nachtigall, K., pitz, J.: A modulo network simplex method for solving periodic schedule optimisation problems. In: Operations Research Proceedings (2007)Google Scholar
  3. 3.
  4. 4.
    Kragelund, L.V.: Solving a timetabling problem using hybrid genetic algorithms. Softw. Pract. Exper. 27(10), 1121–1134 (1996)Google Scholar
  5. 5.
    Cardoen, B., Demeulemeester, E., Beliën, J.: Optimizing a multiple objective surgical case sequencing problem. Int. J. Prod. Econ. 119(2), 354–366 (2009)zbMATHCrossRefGoogle Scholar
  6. 6.
    Cardoen, B., Demeulemeester, E., Beliën, J.: Sequencing surgical cases in a day-care environment: an exact branch-and-price approach. Comput. Oper. Res. 36(9), 2660–2669 (2009)zbMATHCrossRefGoogle Scholar
  7. 7.
    Cardoen, B., Demeulemeester, E., Beliën, J.: Operating room planning and scheduling: A literature review. Eur. J. Oper. Res. 201(3), 921–932 (2010)zbMATHCrossRefGoogle Scholar
  8. 8.
    Dekhici, L., Belkadi, K.: Operating theatre scheduling under constraints. J. Appl. Sci. 10(14), 1380–1388 (2010)CrossRefGoogle Scholar
  9. 9.
    Saleh, B.B., El Moudni, A., Hajjar, M., Barakat, O.: A multi-agent architecture for dynamic scheduling of emergencies in operating theater. In: Advances in Intelligent Systems and Computing, vol. 869. Springer, Cham (2019)Google Scholar
  10. 10.
    Saleh, B.B., El Moudni, A., Hajjar, M., Barakat, O.: Towards an integral operating room management system (2018). ieeexplore.ieee.org/document/8394877
  11. 11.
    Tkaczyk, R., Ganzha, M., Paprzycki, M.: Agent-planner agent, based timetabling system. Informatica 40(1), (2016)Google Scholar
  12. 12.
    Saleh, B.B., El Moudni, A., Hajjar, M., Barakat, O.: A cooperative control model for operating theater scheduling (2018). ieeexplore.ieee.org/document/8394888/
  13. 13.
    Koutsopoulos, I., Hatzi, V.: Optimal energy storage control policies for the smart power grid. In: 2011 IEEE International Conference on Smart Grid Communications, pp. 475–480Google Scholar
  14. 14.
    Praça, I., Ramos, C., Vale, Z., Cordeiro, M.: Intelligent agents for negotiation and game-based decision support in electricity markets. researchgate.net/publication/267806879
  15. 15.
    Petersen, J., Shunturov, V., Janda, K.: Dormitory residents reduce electricity consumption when exposed to real time visual feedback and incentives. Int. J. Sustain. High. Educ. 8(1), 16–33 (2007)CrossRefGoogle Scholar
  16. 16.
    Gonzalez-Romera, E., Jaramillo-Moran, M.A., Carmona, D.: Forecasting of the electric energy demand trend and monthly fluctuation with neural networks. Comput. Ind. Eng. 52(3), 336–343 (2007)CrossRefGoogle Scholar
  17. 17.
    Carabelea, C., Boissier, O., Ramparany, F.: Benefits and requirements of using multi-agent systems on smart devices. Lecture Notes in Computer Science (2003)Google Scholar
  18. 18.
    Marik, V., Stepankova, O., Lazansky, J.: Artificial intelligence. In: J.ICIE 2015 3rd International Conference on Innovation and EntrepreneurshipGoogle Scholar
  19. 19.
    Budinská, I., Dang, T.T.: A case based reasoning in a multi agents support system. In: Proceedings of the 6th International Scientific-Technical Conference, Process Control (2004)Google Scholar
  20. 20.
    Dang, T.T.: Improving plan quality through agent coalitions. In: IEEE International Conference on Computational Cybernetics – ICCC (2004)Google Scholar
  21. 21.
    Druiven, S.: Knowledge development in games of imperfect information. University Maastricht Master Thesis, Institute for Knowledge and Agent Technology, University Maastricht (2002)Google Scholar
  22. 22.
  23. 23.
  24. 24.
    FIPA (2002) FIPA ACL Message Structure Specification. SC00061GGoogle Scholar
  25. 25.
    Dounis, A.I.: Artificial intelligence for energy conservation in buildings. Adv. Build. Energy Res. 4(1), 267–299 (2010)CrossRefGoogle Scholar
  26. 26.
    Pynadath, D.V., Tambe, M.: Multiagent teamwork: analyzing the optimality and complexity of key theories and models, pp. 873–880. ACM (2002)Google Scholar
  27. 27.
    Scerri, P., Pynadath, D.V., Tambe, M.: Towards adjustable autonomy for the real world. J. Artif. Intell. Res. 17, 171–228 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Statistisches Bundesamt, “Wirtschaftsbereich energie - erzeugung,” Statistisches Bundesamt, Technical Report, 2017Google Scholar
  29. 29.
    NIST: Roadmap for smart grid interoperability standards, vol. 1108. NIST Special Publication (2010)Google Scholar
  30. 30.
    Bruinenberg, J., et al.: Smart grid coordination group technical report reference architecture for the smart grid version 1.0 (draft) 2012-03-02. Technical Report (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Bourgogne Franche ComtéDijonFrance
  2. 2.Lebanese UniversityBeirutLebanon
  3. 3.Faculty of Technology-SAIDALebanese UniversityBeirutLebanon

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