Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 15

Proceedings of the 2018 Federated Conference on Computer Science and Information Systems

Sensitivity in Multi-Ensemble Scheduling


DOI: http://dx.doi.org/10.15439/2018F159

Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 215223 ()

Full text

Abstract. Future smart grid control demands delegation of liabilities to distributed, rather small energy resources in contrast to today's traditional large control power units. Distributed energy scheduling constitutes a complex task for optimization algorithms regarding the underlying high-dimensional, multimodal and non-linear problem structure. For predictive scheduling with high penetration of renewable energy resources, agent-based approaches using classifier-based decoders for modeling individual flexibilities have shown good performance. On the other hand, such decoder-based methods are currently designed for single entities and not able to cope with ensembles of energy resources. Aggregating training sets sampled from individually modeled energy units results in folded distributions with unfavorable properties for training a decoder. Nevertheless, this happens to be a quite frequent use case, \eg when a hotel, a small business, a school or similar with an ensemble of co-generation, heat pump, solar power, and controllable consumers wants to take part in decentralized predictive scheduling. Recently, an extension to an established agent approach for scheduling individual single energy units has been proposed that is based on second level optimization. The agents' decision routine may be enhanced by a covariance matrix adaption evolution strategy that is hybridized with decoders. In this way, locally managed ensembles of energy units can be included. The applicability has already been demonstrated, but the effects of ensemble composition are so far unknown. Here, we give an widened view on the underlying power level distribution problem and extend the results by conducting a sensitivity analysis on the impact of ensemble size and penetration on communication overhead and residual error.


  1. European Parliament & Council, “Directive 2009/28/ec of 23 april 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing directives 2001/77/ec and 2003/30/ec.”
  2. O. Abarrategui, J. Marti, and A. Gonzalez, “Constructing the active european power grid,” in Proceedings of WCPEE09, Cairo, 2009.
  3. A. Nieße, S. Lehnhoff, M. Trschel, M. Uslar, C. Wissing, H. J. Appelrath, and M. Sonnenschein, “Market-based self-organized provision of active power and ancillary services: An agent-based approach for smart distribution grids,” in Complexity in Engineering (COMPENG), 2012, June 2012, pp. 1–5.
  4. K. Vinay Kumar and R. Balakrishna, “Smart grid: Advanced metering infrastructure (ami) & distribution management systems (dms),” International Journal of Computer Science and Engineering, vol. 3, no. 11, 2015.
  5. I. Colak, G. Fulli, S. Sagiroglu, M. Yesilbudak, and C.-F. Covrig, “Smart grid projects in europe: Current status, maturity and future scenarios,” Applied Energy, vol. 152, pp. 58 – 70, 2015.
  6. S. Awerbuch and A. M. Preston, Eds., The Virtual Utility: Accounting, Technology & Competitive Aspects of the Emerging Industry, ser. Topics in Regulatory Economics and Policy. Kluwer Academic Publishers, 1997, vol. 26.
  7. M. Sonnenschein, O. Lünsdorf, J. Bremer, and M. Tröschel, “Decentralized control of units in smart grids for the support of renewable energy supply,” Environmental Impact Assessment Review, 2014, in press.
  8. R. Kamphuis, C. Warmer, M. Hommelberg, and K. Kok, “Massive coordination of dispersed generation using powermatcher based software agents,” in 19th International Conference on Electricity Distribution, 05 2007.
  9. K. Kok, Z. Derzsi, J. Gordijn, M. Hommelberg, C. Warmer, R. Kamphuis, and H. Akkermans, “Agent-based electricity balancing with distributed energy resources, a multiperspective case study,” Hawaii International Conference on System Sciences, vol. 0, p. 173, 2008.
  10. A. Kamper and A. Esser, “Strategies for decentralised balancing power,” in Biologically-inspired Optimisation Methods: Parallel Algorithms, Systems and Applications, ser. Studies in Computational Intelligence, M. R. A. Lewis, S. Mostaghim, Ed. Berlin, Heidelberg: Springer, Juni 2009, no. 210, pp. 261–289.
  11. R.-C. Mihailescu, M. Vasirani, and S. Ossowski, “Dynamic coalition adaptation for efficient agent-based virtual power plants,” in Proceedings of the 9th German conference on Multiagent system technologies, ser. MATES’11. Berlin, Heidelberg: Springer-Verlag, 2011, pp. 101–112.
  12. S. D. Ramchurn, P. Vytelingum, A. Rogers, and N. R. Jennings, “Agent-based control for decentralised demand side management in the smart grid,” in AAMAS, L. Sonenberg, P. Stone, K. Tumer, and P. Yolum, Eds. IFAAMAS, 2011, pp. 5–12.
  13. J. Bremer, B. Rapp, and M. Sonnenschein, “Support vector based encoding of distributed energy resources’ feasible load spaces,” in IEEE PES Conference on Innovative Smart Grid Technologies Europe, Chalmers Lindholmen, Gothenburg, Sweden, 2010.
  14. J. Bremer and M. Sonnenschein, “A distributed greedy algorithm for constraint-based scheduling of energy resources,” in Federated Conference on Computer Science and Information Systems - FedCSIS 2012, Wroclaw, Poland, 9-12 September 2012, Proceedings, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2012, pp. 1285–1292.
  15. 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, pp. 91–105.
  16. A. Nieße and M. Sonnenschein, “A fully distributed continuous planning approach for decentralized energy units,” in Multiagent System Technologies, ser. Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2015.
  17. A. Nieße, S. Beer, J. Bremer, C. Hinrichs, O. Lünsdorf, and M. Sonnenschein, “Conjoint dynamic aggrgation and scheduling for dynamic virtual power plants,” in Federated Conference on Computer Science and Information Systems - FedCSIS 2014, Warsaw, Poland, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 9 2014.
  18. J. Bremer and M. Sonnenschein, “Parallel tempering for constrained many criteria optimization in dynamic virtual power plants,” in Computational Intelligence Applications in Smart Grid (CIASG), 2014 IEEE Symposium on, Dec 2014, pp. 1–8.
  19. 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, Warsaw, Poland, September 7-10, 2014, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2014, pp. 1529–1538.
  20. C. Hinrichs, “Selbstorganisierte Einsatzplanung dezentraler Akteure im Smart Grid,” Ph.D. dissertation, Carl von Ossietzky Universitt Oldenburg, 2014.
  21. J. Bremer and S. Lehnhoff, “Decentralized coalition formation in agent-based smart grid applications,” in Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection, ser. Communications in Computer and Information Science, vol. 616. Springer, 2016, pp. 343–355.
  22. ——, Hybrid Multi-ensemble Scheduling. Cham: Springer International Publishing, 2017, pp. 342–358.
  23. S. McArthur, E. Davidson, V. Catterson, A. Dimeas, N. Hatziargyriou, F. Ponci, and T. Funabashi, “Multi-agent systems for power engineering applications – Part I: Concepts, approaches, and technical challenges,” IEEE Transactions on Power Systems, vol. 22, no. 4, pp. 1743–1752, 2007.
  24. M. Sonnenschein, C. Hinrichs, A. Nieße, and U. Vogel, “Supporting renewable power supply through distributed coordination of energy resources,” in ICT Innovations for Sustainability, ser. Advances in Intelligent Systems and Computing, L. M. Hilty and B. Aebischer, Eds. Springer International Publishing, 2015, vol. 310, pp. 387–404.
  25. 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.
  26. 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.
  27. C. A. Coello 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.
  28. C. Hinrichs, M. Sonnenschein, and S. Lehnhoff, “Evaluation of a Self-Organizing Heuristic for Interdependent Distributed Search Spaces,” in International Conference on Agents and Artificial Intelligence (ICAART 2013), J. Filipe and A. L. N. Fred, Eds., vol. Volume 1 – Agents. SciTePress, 2013, pp. 25–34.
  29. C. Hinrichs, S. Lehnhoff, and M. Sonnenschein, “A Decentralized Heuristic for Multiple-Choice Combinatorial Optimization Problems,” in Operations Research Proceedings 2012. Springer, 2014, pp. 297–302.
  30. C. Hinrichs, J. Bremer, and M. Sonnenschein, “Distributed Hybrid Constraint Handling in Large Scale Virtual Power Plants,” in IEEE PES Conference on Innovative Smart Grid Technologies Europe (ISGT Europe 2013). IEEE Power & Energy Society, 2013.
  31. A. Nieße and M. Sonnenschein, “A fully distributed continuous planning approach for decentralized energy units,” in Informatik 2015. GI-Edition - Lecture Notes in Informatics (LNI), D. W. Cunningham, P. Hofstedt, K. Meer, and I. Schmitt, Eds., vol. 246. Bonner Köllen Verlag, 2015, pp. 151–165.
  32. R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intelligence, vol. 1, no. 1, pp. 33–57, 2007.
  33. D. Karaboga and B. Basturk, “A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm,” Journal of Global Optimization, vol. 39, no. 3, pp. 459–471, Nov. 2007.
  34. T. Lust and J. Teghem, “The multiobjective multidimensional knapsack problem: a survey and a new approach,” CoRR, vol. abs/1007.4063, 2010.
  35. D. Watts and S. Strogatz, “Collective dynamics of ’small-world’ networks,” Nature, vol. 393, no. 6684, pp. 440–442, 1998.
  36. 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, EnviroInfo 2013, B. Page, A. G. Fleischer, J. Göbel, and V. Wohlgemuth, Eds. Shaker, 2013, pp. 214–222.
  37. P. Hall, “The distribution of means for samples of size n drawn from a population in which the variate takes values between 0 and 1, all such values being equally probable,” Biometrika, vol. 19, no. 3/4, pp. pp. 240–245, 1927.
  38. D. M. J. Tax and R. P. W. Duin, “Support vector data description,” Mach. Learn., vol. 54, no. 1, pp. 45–66, 2004.
  39. A. Ostermeier, A. Gawelczyk, and N. Hansen, “A derandomized approach to self-adaptation of evolution strategies,” Evolutionary Computation, vol. 2, no. 4, pp. 369–380, 1994.
  40. N. Hansen, “The CMA evolution strategy: a comparing review,” in Towards a new evolutionary computation. Advances on estimation of distribution algorithms, J. Lozano, P. Larranaga, I. Inza, and E. Bengoetxea, Eds. Springer, 2006, pp. 75–102.
  41. N. Hansen, “The CMA Evolution Strategy: A Tutorial,” Tech. Rep., 2011.
  42. O. Kramer, A. Barthelmes, and G. Rudolph, “Surrogate constraint functions for cma evolution strategies,” in Proceedings of the 32Nd Annual German Conference on Advances in Artificial Intelligence, ser. KI’09. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 169–176.
  43. D. V. Arnold and N. Hansen, “A (1+1)-cma-es for constrained optimisation,” in Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, ser. GECCO ’12. New York, NY, USA: ACM, 2012, pp. 297–304.
  44. N. Hansen and A. Ostermeier, “Completely derandomized self-adaptation in evolution strategies,” Evol. Comput., vol. 9, no. 2, pp. 159–195, Jun. 2001.
  45. J. Bremer, B. Rapp, and M. Sonnenschein, “Encoding distributed search spaces for virtual power plants,” in IEEE Symposium Series on Computational Intelligence 2011 (SSCI 2011), Paris, France, 4 2011.
  46. J. Neugebauer, O. Kramer, and M. Sonnenschein, “Classification cascades of overlapping feature ensembles for energy time series data,” in Proceedings of the 3rd International Workshop on Data Analytics for Renewable Energy Integration (DARE’15). Springer, 2015.
  47. M. Sonnenschein, H.-J. Appelrath, W.-R. Canders, M. Henke, M. Uslar, S. Beer, J. Bremer, O. Lünsdorf, A. Nieße, J.-H. Psola et al., “Decentralized provision of active power,” in Smart Nord - Final Report. Hartmann GmbH, Hannover, 2015.
  48. S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Gossip algorithms: Design, analysis and applications,” in Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies., vol. 3. IEEE, 2005, pp. 1653–1664.