Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Local Soft Constraints in Distributed Energy Scheduling

, , ,

DOI: http://dx.doi.org/10.15439/2016F76

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 15171525 ()

Full text

Abstract. In this contribution we present an approach on how to include local soft constraints in the fully distributed algorithm COHDA for the task of energy units scheduling in virtual power plants (VPP). We show how a flexibility representation based on surrogate models is extended and trained using soft constraints like avoiding frequent cold starts of combined heat and power plants. During the task of energy scheduling, the agents representing these machines include indicators in their choice for a new operation schedule. Using an example VPP we show that our approach enables the agents to reflect local soft constraints without sacrificing the global result quality.

References

  1. H.-J. Appelrath, H. Kagermann, and C. Mayer, Eds., Future Energy Grid. (acatech STUDY). acatech, Munich, 2012.
  2. S. D. J. McArthur, E. M. Davidson, V. M. Catterson, A. L. Dimeas, N. D. Hatziargyriou, F. Ponci, and T. Funabashi, “Multi-Agent Systems for Power Engineering Applications—Part 1: Concepts, Approaches, and Technical Challenges,” IEEE Transactions on Power Systems, vol. 22, pp. 1743–1752, 2007.
  3. ——, “Multi-Agent Systems for Power Engineering Applications—Part 2: Technologies, Standards, and Tools for Building Multi-Agent Systems,” IEEE Transactions on Power Systems, vol. 22, pp. 1753–1759, 2007.
  4. P. Vrba, V. Mařı́k, P. Siano, P. Leitão, G. Zhabelova, V. Vyatkin, and T. Strasser, “A Review of Agent and Service-Oriented Concepts Applied to Intelligent Energy Systems,” IEEE Transactions of Industrial Informatics, vol. 10, no. 3, pp. 1890–1903, 2014.
  5. A. Nieße, S. Beer, J. Bremer, C. Hinrichs, O. Lünsdorf, and M. Sonnenschein, “Conjoint Dynamic Aggregation and Scheduling Methods for Dynamic Virtual Power Plants,” in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 2. IEEE, 2014. http://dx.doi.org/10.15439/2014F76. ISBN 978-83-60810-58-3 pp. 1505–1514.
  6. A. Smith and D. Coit, Handbook of Evolutionary Computation. Department of Industrial Engineering, University of Pittsburgh, USA: Oxford University Press and IOP Publishing, 1997, ch. Penalty Functions, p. Section C5.2.
  7. G. E. Liepins and M. D. Vose, “Representational issues in genetic optimization,” Journal of Experimental and Theoretical Artificial Intelligence, vol. 2, 1990.
  8. O. Kramer, “A review of constraint-handling techniques for evolution strategies,” Appl. Comp. Intell. Soft Comput., vol. 2010, pp. 1–19, 01 2010. http://dx.doi.org/http://dx.doi.org/10.1155/2010/185063
  9. A. Schiendorfer, J.-P. Steghöfer, and W. Reif, “Synthesised Constraint Models for Distributed Energy Management,” in Proceedings of the 2014 Federated Conference on Computer Science and Information Systems, ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 2. IEEE, 2014. http://dx.doi.org/10.15439/2014F49. ISBN 978-83-60810-58-3 pp. 1529–1538. [Online]. Available: http://dx.doi.org/10.15439/2014F49
  10. F. Gieseke and O. Kramer, “Towards non-linear constraint estimation for expensive optimization,” in Applications of Evolutionary Computation, ser. Lecture Notes in Computer Science, A. Esparcia-Alcázar, Ed. Springer Berlin Heidelberg, 2013, vol. 7835, pp. 459–468. ISBN 978-3-642-37191-2
  11. J. Bremer and M. Sonnenschein, “Model-based integration of constrained search spaces into distributed planning of active power provision.” Comput. Sci. Inf. Syst., vol. 10, no. 4, pp. 1823–1854, 2013.
  12. C. A. Coello, “Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art,” Computer Methods in Applied Mechanics and Engineering, vol. 191, no. 11-12, pp. 1245–1287, Jan. 2002. http://dx.doi.org/10.1016/S0045-7825(01)00323-1
  13. H. Akkermans, F. Ygge, and R. Gustavsson, “Homebots: Intelligent decentralized services for energy management,” in Fourth International Symposium on the Management of Industrial and Corporate Knowledge ISMICK’96, Rotterdam, NL, Oktober 1996.
  14. R. Gustavsson, “Agents with Power,” Communications of the ACM, vol. 42, no. 3, pp. 41–47, 1999.
  15. S. Lehnhoff, Dezentrales vernetztes Energiemanagement - Ein Ansatz auf Basis eines verteilten Realzeit-Multiagentensystems. Vieweg + Teubner, 2010.
  16. C. Hinrichs, S. Lehnhoff, and M. Sonnenschein, “A Decentralized Heuristic for Multiple-Choice Combinatorial Optimization Problems,” in Operations Research Proceedings 2012. Springer, 2014. http://dx.doi.org/10.1007/978-3-319-00795-3 43. ISBN 978-3-319-00795-3 pp. 297–302.
  17. A. Nieße and M. Sonnenschein, “A Fully Distributed Continuous Planning Approach for Decentralized Energy Units,” in 45. Jahrestagung der Gesellschaft für Informatik e.V. (GI), Informatik, Energie und Umwelt. Cottbus: Gesellschaft für Informatik, Köllen Druck+Verlag, 2015.
  18. M. Tröschel, Aktive Einsatzplanung in holonischen Virtuellen Kraftwerken. Oldenburg: OlWIR, Oldenburger Verl. für Wirtschaft, Informatik und Recht, 2010. ISBN 978-3-939704-55-3
  19. G. Anders, F. Siefert, J.-P. Steghöfer, H. Seebach, F. Nafz, and W. Reif, “Structuring and Controlling Distributed Power Sources by Autonomous Virtual Power Plants,” in Proceedings of the IEEE Power and Energy Student Summit (PESS), 2010.
  20. E. Pournaras, “Multi-level Reconfigurable Self-organization in Overlay Services,” Ph.D. dissertation, TU Delft. ISBN 9789461860989 2013.
  21. S. Koziel and Z. Michalewicz, “Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization,” Evol. Comput., vol. 7, pp. 19–44, 03 1999. http://dx.doi.org/http://dx.doi.org/10.1162/evco.1999.7.1.19
  22. J. Bremer and M. Sonnenschein, “Constraint-handling for optimization with support vector surrogate models – a novel decoder approach,” in ICAART 2013 – Proceedings of the 5th International Conference on Agents and Artificial Intelligence, J. Filipe and A. Fred, Eds., vol. 2. Barcelona, Spain: SciTePress, 2013. http://dx.doi.org/10.5220/0004241100910100. ISBN 978-989-8565-38-9 pp. 91–105.
  23. J. Bremer, B. Rapp, and M. Sonnenschein, “Including environmental performance indicators into kernel based search space representations,” in Information Technologies in Environmental Engineering, ser. Environmental Science and Engineering, P. Golinska, M. Fertsch, and J. Marx-Gómez, Eds. Springer Berlin Heidelberg, 2011, vol. 3, pp. 275–288. ISBN 978-3-642-19535-8. http://dx.doi.org/10.1007/978-3-642-19536-5_22
  24. P. Juszczak, D. Tax, and R. P. W. Duin, “Feature scaling in support vector data description,” in Proc. ASCI 2002, 8th Annual Conf. of the Advanced School for Computing and Imaging, E. Deprettere, A. Belloum, J. Heijnsdijk, and F. van der Stappen, Eds., 2002, pp. 95–102.
  25. D. M. J. Tax and R. P. W. Duin, “Support vector data description,” Mach. Learn., vol. 54, no. 1, pp. 45–66, 2004. http://dx.doi.org/http://dx.doi.org/10.1023/B:MACH.0000008084.60811.49
  26. B. Schölkopf, S. Mika, C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, and A. Smola, “Input space vs. feature space in kernel-based methods,” IEEE Transactions on Neural Networks, vol. 10(5), pp. 1000–1017, 1999.
  27. A. Ben-Hur, H. T. Siegelmann, D. Horn, and V. Vapnik, “Support vector clustering,” Journal of Machine Learning Research, vol. 2, pp. 125–137, 2001.
  28. J. Bremer and M. Sonnenschein, “Sampling the search space of energy resources for self-organized, agent-based planning of active power provision,” in 27th International Conference on Environmental Informatics for Environmental Protection, Sustainable Development and Risk Management, EnviroInfo 2013, Hamburg, Germany, September 2-4, 2013. Proceedings, ser. Berichte aus der Umweltinformatik, B. Page, A. G. Fleischer, J. Göbel, and V. Wohlgemuth, Eds. Shaker, 2013. ISBN 978-3-8440-1676-5 pp. 214–222.
  29. J. Bremer, “Constraint-Handling mit Supportvektor-Dekodern in der verteilten Optimierung,” Ph.D. dissertation, 2015. http://oops.uni-oldenburg.de/2336/
  30. L. Grüne and J. Pannek, Nonlinear Model Predictive Control: Theory and Algorithms, 1st ed., ser. Communications and Control Engineering. Springer, 2011. http://www.springer.com/978-0-85729-500-2