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Sequential Predictive Scheduling in Partitioned Data Domains

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Recent Advances in Computational Optimization

Part of the book series: Studies in Computational Intelligence ((SCI,volume 717))

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Abstract

Following the long-term goal of substituting conventional, fossil power generation completely with cleaner, renewable energy will consequently lead to an integration of a large share of small energy generation units imposing large problem sizes for coordination. Hardly predictable, stochastic feed-in makes the problem even harder. Predictive scheduling is a frequent task in energy grid control and has been widely studied for some decades. But, the expected huge number of entities leads to a need for new techniques reducing the computational effort for coordination. For a group of energy resources, a schedule has to be found for each single entity in the group that fulfills several objectives at the same time and resembles jointly a wanted target schedule. Considering day-ahead scenarios with 96-dimensional schedules imposes additional challenges to this already hard combinatorial problem. We explore the effects of reducing complexity by partitioning the data domain of the optimization problem for a sequential approach that integrates energy models for constraint handling directly into the optimization process. We explore the effects of different partitioning schemes and evaluate the trade-off between accuracy and effort with several simulation studies.

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References

  1. Abarrategui, O., Marti, J., Gonzalez, A.: Constructing the active European power grid. In: Proceedings of WCPEE09, Cairo (2009)

    Google Scholar 

  2. Arteconi, A., Hewitt, N., Polonarac, F.: Domestic demand-side management (DSM): role of heat pumps and thermal energy storage (TES) systems. Appl. Thermal Eng. 51(1–2), pp. 155–165 (2013). doi:10.1016/j.applthermaleng.2012.09.023

  3. Bremer, J., Sonnenschein, M.: A distributed greedy algorithm for constraint-based scheduling of energy resources. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) FedCSIS, pp. 1285–1292 (2012)

    Google Scholar 

  4. Bremer, J., Sonnenschein, M.: Constraint-handling for optimization with support vector surrogate models – a novel decoder approach. In: Filipe, J., Fred, A. (eds.) ICAART 2013 – Proceedings of the 5th International Conference on Agents and Artificial Intelligence, SciTePress, Barcelona, Spain, vol. 2, pp. 91–100 (2013). doi:10.5220/0004241100910100

  5. Bremer, J., Sonnenschein, M.: Estimating shapley values for fair profit distribution in power planning smart grid coalitions. In: Klusch, M., Thimm, M., Paprzycki, M. (eds.) Multiagent System Technologies - 11th German Conference, MATES 2013, Koblenz, Germany, September 16–20, 2013, Proceedings. Lecture Notes in Computer Science, vol. 8076, pp. 208–221. Springer, Berlin (2013). doi:10.1007/978-3-642-40776-5_19

  6. Bremer, J., Sonnenschein, M.: Model-based integration of constrained search spaces into distributed planning of active power provision. Comput. Sci. Inf. Syst. 10(4), 1823–1854 (2013). doi:10.2298/CSIS130304073B

    Article  Google Scholar 

  7. Bremer, J., Sonnenschein, M.: Sampling the search space of energy resources for self-organized, agent-based planning of active power provision. EnviroInfo, Berichte aus der Umweltinformatik, pp. 214–222. Shaker, Germany (2013)

    Google Scholar 

  8. Bremer, J., Sonnenschein, M.: 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, pp. 51–58. IEEE (2014). doi:10.1109/CIASG.2014.7011551

  9. Bremer, J., Lehnhoff, S.: Decentralized coalition formation in agent-based smart grid applications. Highlights of Practical Applications of Scalable Multi-Agent Systems. The PAAMS Collection. Communications in Computer and Information Science, vol. 616, pp. 343–355. Springer, Berlin (2016)

    Google Scholar 

  10. Bremer, J., Rapp, B., Sonnenschein, M.: Encoding distributed search spaces for virtual power plants. In: IEEE Symposium Series on Computational Intelligence 2011 (SSCI 2011), Paris, France (2011). doi:10.1109/CIASG.2011.5953329

  11. Bremer, J., Rapp, B., Sonnenschein, M.: Encoding distributed search spaces for virtual power plants. In: Computational Intelligence Applications in Smart Grid (CIASG), 2011 IEEE Symposium Series on Computational Intelligence (SSCI), Paris, France (2011). doi:10.1109/CIASG.2011.5953329

  12. Brown, S., Head-Gordon, T.: Cool walking: a new markov chain monte carlo sampling method. J. Comput. Chem. 24(1), 68–76 (2003). doi:10.1002/jcc.10181

    Article  Google Scholar 

  13. Coello Coello, C.A.: Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Comput. Methods Appl. Mech. Eng. 191(11–12), 1245–1287 (2002). doi:10.1016/S0045-7825(01)00323-1

    Article  MathSciNet  MATH  Google Scholar 

  14. Colak, I., Fulli, G., Sagiroglu, S., Yesilbudak, M., Covrig, C.F.: Smart grid projects in Europe: current status, maturity and future scenarios. Appl. Energy 152, 58–70 (2015). http://dx.doi.org/10.1016/j.apenergy.2015.04.098

  15. Coll-Mayor, D., Picos, R., Garciá-Moreno, E.: State of the art of the virtual utility: the smart distributed generation network. Int. J. Energy Res. 28(1), 65–80 (2004). doi:10.1002/er.951

    Article  Google Scholar 

  16. Donoho, D.L.: High-dimensional data analysis: the curses and blessings of dimensionality. In: Aide-memoire of a Lecture at AMS Conference on Math Challenges of the 21st Century (2000)

    Google Scholar 

  17. 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

    Google Scholar 

  18. Hastings, W.K.: Monte carlo sampling methods using markov chains and their applications. Biometrika 57(1), 97–109 (1970). doi:10.1093/biomet/57.1.97

    Article  MathSciNet  MATH  Google Scholar 

  19. Hinrichs, C., Bremer, J., Sonnenschein, M.: 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). http://www-ui.informatik.uni-oldenburg.de/download/Publikationen/HBS13.pdf

  20. Hinrichs, C., Bremer, J., Martens, S., Sonnenschein, M.: partitioning the data domain of combinatorial problems for sequential optimization. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) 9th International Workshop on Computational Optimization, Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, Gdansk (2016, in press)

    Google Scholar 

  21. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 220(4598), 671–680 (1983). doi:10.1126/science.220.4598.671

    Article  MathSciNet  MATH  Google Scholar 

  22. Koziel, S., Michalewicz, Z.: Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol. Comput. 7, 19–44 (1999). doi:10.1162/evco.1999.7.1.19

    Article  Google Scholar 

  23. Li, Y., Protopopescu, V.A., Arnold, N., Zhang, X., Gorin, A.: Hybrid parallel tempering and simulated annealing method. Appl. Math. Comput. 212(1), 216–228 (2009). doi:10.1016/j.amc.2009.02.023

    MathSciNet  MATH  Google Scholar 

  24. Marinari, E., Parisi, G.: Simulated tempering: a new Monte Carlo scheme. Europhys. Lett. 19(6) (1992)

    Google Scholar 

  25. McArthur, S., Davidson, E., Catterson, V., Dimeas, A., Hatziargyriou, N., Ponci, F., Funabashi, T.: Multi-agent systems for power engineering applications–Part I: concepts, approaches, and technical challenges. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007). doi:10.1109/TPWRS.2007.908471

    Article  Google Scholar 

  26. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equation of state calculations by fast computing machines. J. Chem. Phys. 21(6), 1087–1092 (1953). doi:10.1063/1.1699114

    Article  Google Scholar 

  27. Müller, A., Schneider, J.J., Schömer, E.: Packing a multidisperse system of hard disks in a circular environment. Phys. Rev. E 79, 021102 (2009). doi:10.1103/PhysRevE.79.021102

    Article  MathSciNet  Google Scholar 

  28. Nieße, A., Lehnhoff, S., Tröschel, M., Uslar, M., Wissing, C., Appelrath, H.J., Sonnenschein, M.: 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, pp. 1–5 (2012). doi:10.1109/CompEng.2012.6242953

  29. Nieße, A., Beer, S., Bremer, J., Hinrichs, C., Lünsdorf, O., Sonnenschein, M.: Conjoint dynamic aggregation and scheduling for dynamic virtual power plants. In: Ganzha, M., Maciaszek, L.A., Paprzycki, M. (eds.) Federated Conference on Computer Science and Information Systems - FedCSIS 2014, Warsaw, Poland (2014). doi:10.15439/2014F76

  30. Nikonowicz, Ł.B., Milewski, J.: Virtual power plants – general review: structure, application and optimization. J. Power Technol. 92(3) (2012). http://papers.itc.pw.edu.pl/index.php/JPT/article/view/284/492

  31. Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Ind. Inform. 7(3), 381–388 (2011). doi:10.1109/TII.2011.2158841

    Article  Google Scholar 

  32. Shan, S., Wang, G.G.: Survey of modeling and optimization strategies to solve high-dimensional design problems with computationally-expensive black-box functions. Struct. Multidiscip. Optim. 41(2), 219–241 (2010). doi:10.1007/s00158-009-0420-2

    Article  MathSciNet  MATH  Google Scholar 

  33. Sonnenschein, M., Appelrath, H.J., Canders, W.R., Henke, M., Uslar, M., Beer, S., Bremer, J., Lünsdorf, O., Nieße, A., Psola, J.H., et al.: Decentralized provision of active power. In: Smart Nord - Final Report. Hartmann GmbH, Hannover (2015)

    Google Scholar 

  34. Sonnenschein, M., Hinrichs, C., Nieße, A., Vogel, U.: Supporting renewable power supply through distributed coordination of energy resources. In: Hilty, L.M., Aebischer, B. (eds.) ICT Innovations for Sustainability. Advances in Intelligent Systems and Computing, vol. 310, pp. 387–404. Springer, Berlin (2015). doi:10.1007/978-3-319-09228-7_23

    Google Scholar 

  35. Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004). doi:10.1023/B:MACH.0000008084.60811.49

    Article  MATH  Google Scholar 

  36. Vassilev, V.K., Fogarty, T.C., Miller, J.F.: Information characteristics and the structure of landscapes. Evol. Comput. 8(1), 31–60 (2000). doi:10.1162/106365600568095

    Article  Google Scholar 

  37. Verleysen, M., François, D.: The curse of dimensionality in data mining and time series prediction. Computational Intelligence and Bioinspired Systems. Lecture Notes in Computer Science, vol. 3512, pp. 758–770. Springer, Berlin (2005). doi:10.1007/11494669_93

    Chapter  Google Scholar 

  38. Vinay Kumar, K., Balakrishna, R.: Smart grid: advanced metering infrastructure (AMI) & distribution management systems (DMS). Int. J. Comput. Sci. Eng. 3(11) (2015)

    Google Scholar 

  39. Wong, W.H., Liang, F.: Dynamic weighting in Monte Carlo and optimization. Appl. Math. Proc. Nat. Acad. Sci. 94, 14220–14224 (1997)

    Google Scholar 

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Bremer, J., Hinrichs, C., Martens, S., Sonnenschein, M. (2018). Sequential Predictive Scheduling in Partitioned Data Domains. In: Fidanova, S. (eds) Recent Advances in Computational Optimization. Studies in Computational Intelligence, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-319-59861-1_1

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  • DOI: https://doi.org/10.1007/978-3-319-59861-1_1

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