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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 18

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

Towards fully Decentralized Multi-Objective Energy Scheduling

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DOI: http://dx.doi.org/10.15439/2019F160

Citation: Proceedings of the 2019 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 18, pages 193–201 ()

Full text

Abstract. Future demand for managing a huge number of individually operating small and often volatile energy resources within the smart grid is preponderantly answered by involving decentralized orchestration methods for planning and scheduling. Many planning and scheduling problems are of a multi-objective nature. For the single-objective case -- e.g. predictive scheduling with the goal of jointly resembling a wanted target schedule -- fully decentralized algorithms with self-organizing agents exist. We extend this paradigm towards fully decentralized agent-based multi-objective scheduling for energy resources e.g. in virtual power plants for which special local constraint-handling techniques are needed. We integrate algorithmic elements from the well-known S-metric selection evolutionary multi-objective algorithm into a gossiping-based combinatorial optimization heuristic that works with agents for the single-objective case and derive a number of challenges that have to be solved for fully decentralized multi-objective optimization. We present a first solution approach based on the combinatorial optimization heuristics for agents and demonstrate viability and applicability in several simulation scenarios.

References

  1. 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.
  2. Ł. B. Nikonowicz and J. Milewski, “Virtual power plants – general review: structure, application and optimization.” Journal of Power Technologies, vol. 92, no. 3, 2012.
  3. 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.
  4. J. Bremer and M. Sonnenschein, “Automatic reconstruction of performance indicators from support vector based search space models in distributed real power planning scenarios,” in Informatik 2013, 43. Jahrestagung der Gesellschaft für Informatik e.V. (GI), Informatik angepasst an Mensch, Organisation und Umwelt, 16.-20. September 2013, Koblenz, ser. LNI, M. Horbach, Ed., vol. 220. GI, 2013, pp. 1441–1454.
  5. G. Narzisi, V. Mysore, and B. Mishra, “Multi-objective evolutionary optimization of agent-based models: An application to emergency response planning,” in Computational Intelligence. IASTED/ACTA Press, 2006, pp. 228–232.
  6. Y. Gu, “Multi-objective optimization of multi-agent elevator group control system based on real-time particle swarm optimization algorithm,” Engineering, vol. 04, no. 07, pp. 368–378, 2012.
  7. “Adaptive multi-objective reinforcement learning with hybrid exploration for traffic signal control based on cooperative multi-agent framework,” Engineering Applications of Artificial Intelligence, vol. 29, pp. 134 – 151, 2014.
  8. A. illah Mouaddib, M. Boussard, and M. Bouzid, “Towards a formal framework for multi-objective multi-agent planning,” in In Proc. of the 6th Int. Conf. on Autonomous Agents and Multiagent Systems, 2007, pp. 801–808.
  9. L. T. Bui, H. A. Abbass, and D. Essam, “Local models–an approach to distributed multi-objective optimization,” Comput. Optim. Appl., vol. 42, no. 1, pp. 105–139, Jan. 2009.
  10. 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, Volume 2, Barcelona, Spain, 15-18 February, 2013, J. Filipe and A. L. N. Fred, Eds. SciTePress, 2013, pp. 91–100.
  11. 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.
  12. S. Koziel and Z. Michalewicz, “Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization,” Evol. Comput., vol. 7, pp. 19–44, 03 1999.
  13. C. Hinrichs, J. Bremer, S. Martens, and M. Sonnenschein, “Partitioning the data domain of combinatorial problems for sequential optimization,” in FedCSIS, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., 2016, pp. 551–559.
  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. A. Nieße, C. Hinrichs, J. Bremer, and M. Sonnenschein, “Local Soft Constraints in Distributed Energy Scheduling,” in 5th International Workshop on Smart Energy Networks & Multi-Agent Systems, Pro- ceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, and M. Paprzycki, Eds., Gdansk, 2016.
  16. A. Nieße and M. Tröschel, “Controlled self-organization in smart grids,” in Proceedings of the 2016 IEEE International Symposium on Systems Engineering (ISSE). IEEE, 2016, pp. S. 1–6.
  17. J. Bremer and M. Sonnenschein, “Parallel tempering for constrained many criteria optimization in dynamic virtual power plants,” in 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG 2014, Orlando, FL, USA, December 9-12, 2014. IEEE, 2014, pp. 51–58.
  18. C.-S. Karavas, G. Kyriakarakos, K. Arvanitis, and G. Papadakis, “A multi-agent decentralized energy management system based on distributed intelligence for the design and control of autonomous polygeneration microgrids,” Energy Conversion and Management, vol. 103, 10 2015.
  19. C. A. Coello Coello, G. B. Lamont, and D. A. V. Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation). Berlin, Heidelberg: Springer-Verlag, 2006.
  20. A. Zhou, B.-Y. Qu, H. Li, S.-Z. Zhao, P. N. Suganthan, and Q. Zhang, “Multiobjective evolutionary algorithms: A survey of the state of the art,” Swarm and Evolutionary Computation, vol. 1, no. 1, pp. 32 – 49, 2011.
  21. E. Zitzler and L. Thiele, “Multiobjective optimization using evolutionary algorithms — a comparative case study,” in Parallel Problem Solving from Nature — PPSN V, A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998, pp. 292–301.
  22. M. Emmerich, N. Beume, and B. Naujoks, “An emo algorithm using the hypervolume measure as selection criterion,” in Evolutionary MultiCriterion Optimization, C. A. Coello Coello, A. Hernández Aguirre, and E. Zitzler, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005, pp. 62–76.
  23. K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: Nsga-ii,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197, Apr 2002.
  24. J. J. Durillo, J. Garcı́a-Nieto, A. J. Nebro, C. A. Coello, F. Luna, and E. Alba, “Multi-objective particle swarm optimizers: An experimental comparison,” in Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization, ser. EMO ’09. Berlin, Heidelberg: Springer-Verlag, 2009, pp. 495–509.
  25. 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.
  26. F. Snyman and M. Helbig, “Solving constrained multi-objective optimization problems with evolutionary algorithms,” in Advances in Swarm Intelligence, Y. Tan, H. Takagi, Y. Shi, and B. Niu, Eds. Cham: Springer International Publishing, 2017, pp. 57–66.
  27. N. Srinivas and K. Deb, “Multiobjective optimization using nondominated sorting in genetic algorithms,” Evolutionary Computation, vol. 2, no. 3, pp. 221–248, 1994.
  28. 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.
  29. K. Deb, Multi-Objective Optimization Using Evolutionary Algorithms. New York, NY, USA: John Wiley & Sons, Inc., 2001.
  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. J. Bremer and S. Lehnhoff, “Hybridizing s-metric selection and support vector decoder for constrained multi-objective energy management,” in Hybrid Intelligent Systems, A. M. Madureira, A. Abraham, N. Gandhi, and M. L. Varela, Eds. Cham: Springer International Publishing, 2020, pp. 249–259.
  32. M. Fleischer, “The measure of pareto optima applications to multi-objective metaheuristics,” in Evolutionary Multi-Criterion Optimization, C. M. Fonseca, P. J. Fleming, E. Zitzler, L. Thiele, and K. Deb, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 519–533.
  33. A. Nieße, S. Lehnhoff, M. Tröschel, 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.
  34. S. D. Ramchurn, P. Vytelingum, A. Rogers, and N. R. Jennings, “Putting the ’smarts’ into the smart grid: A grand challenge for artificial intelligence,” Commun. ACM, vol. 55, no. 4, pp. 86–97, Apr. 2012.
  35. 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.
  36. 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.
  37. R. Poli, J. Kennedy, and T. Blackwell, “Particle swarm optimization,” Swarm Intelligence, vol. 1, no. 1, pp. 33–57, 2007.
  38. 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.
  39. T. Lust and J. Teghem, “The multiobjective multidimensional knapsack problem: a survey and a new approach,” CoRR, vol. abs/1007.4063, 2010.
  40. D. Watts and S. Strogatz, “Collective dynamics of ’small-world’ networks,” Nature, no. 393, pp. 440–442, 1998.
  41. J. Liu, B. Anderson, M. Cao, and A. Morse, “Analysis of accelerated gossip algorithms,” Automatica, vol. 49, no. 4, pp. 873–883, 4 2013.
  42. C. Hinrichs, “Selbstorganisierte Einsatzplanung dezentraler Akteure im Smart Grid,” Ph.D. dissertation, Department for Computing Science, 2014. [Online]. Available: http://oops.uni-oldenburg.de/1960/
  43. 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, pp. 214–222.
  44. A. Nieße and M. Sonnenschein, “Using grid related cluster schedule resemblance for energy rescheduling - goals and concepts for rescheduling of clusters in decentralized energy systems.” in SMARTGREENS, B. Donnellan, J. F. Martins, M. Helfert, and K.-H. Krempels, Eds. SciTePress, 2013, pp. 22–31.
  45. 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, 2015, vol. 310, pp. 387–404.
  46. J. Bremer and M. Sonnenschein, “Automatic reconstruction of performance indicators from support vector based search space models in distributed real power planning scenarios,” in Informatik 2013, 43. Jahrestagung der Gesellschaft für Informatik e.V. (GI), Informatik angepasst an Mensch, Organisation und Umwelt, 16.-20. September 2013, Koblenz, ser. LNI, M. Horbach, Ed., vol. 220. GI, 2013, pp. 1441–1454.
  47. J. Bremer, “Ontology based description of der’s learned environmental performance indicators,” in Proceedings of the 1st International Conference on Smart Grids and Green IT Systems – SmartGreens 2012, B. Donnellan, J. P. Lopes, J. Martins, and J. Filipe, Eds. Porto, Portugal: SciTePress, 04 2012, pp. 107–112.
  48. K. Deb, S. Agrawal, A. Pratap, and T. Meyarivan, “A fast elitist nondominated sorting genetic algorithm for multi-objective optimization: Nsga-ii,” in Parallel Problem Solving from Nature PPSN VI, M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo, and H.-P. Schwefel, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000, pp. 849–858.
  49. R. Lyndon While, L. Bradstreet, and L. Barone, “A fast way of calculating exact hypervolumes,” IEEE Trans. Evolutionary Computation, vol. 16, pp. 86–95, 02 2012.
  50. 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.
  51. J. Bremer and S. Lehnhoff, Decentralized Surplus Distribution Estimation with Weighted k-Majority Voting Games. Cham: Springer International Publishing, 2017, pp. 327–339.
  52. A. Nieße, J. Bremer, and S. Lehnhoff, “On local minima in distributed energy scheduling,” in Position Papers of the 2017 Federated Conference on Computer Science and Information Systems, FedCSIS 2017, Prague, Czech Republic, September 3-6, 2017., ser. Annals of Computer Science and Information Systems, M. Ganzha, L. A. Maciaszek, and M. Paprzycki, Eds., vol. 12.