Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling

Jörg Bremer, Sebastian Lehnhoff

2016

Abstract

Robust proactive planning of day-ahead real power provision must incorporate uncertainty in feasibility when trading off different schedules against each other during the predictive planning phase. Imponderabilities like weather, user interaction, projected heat demand, and many more have a major impact on feasibility – in the sense of being technically operable by a specific energy unit. Deviations from the predicted initial operational state of an energy unit may easily foil a planned schedule commitment and provoke the need for ancillary services. In order to minimize control power and cost arising from deviations from agreed energy product delivery, it is advantageous to a priori know about individual uncertainty. We extend an existing surrogate model that has been successfully used in energy management for checking feasibility during constraint-based optimization. The surrogate is extended to incorporate confidence scores based on expected feasibility under changed operational conditions. We demonstrate the superiority of the new surrogate model by results from several simulation studies.

References

  1. Alharbi, W. and Raahemifar, K. (2015). Probabilistic coordination of microgrid energy resources operation considering uncertainties. Electric Power Systems Research, 128:1 - 10.
  2. Ben-Hur, A., Siegelmann, H. T., Horn, D., and Vapnik, V. (2001). Support vector clustering. Journal of Machine Learning Research, 2:125-137.
  3. Blank, M. and Lehnhoff, S. (2014). Correlations in reliability assessment of agent-based ancillary-service coalitions. In Power Systems Computation Conference (PSCC), 2014, pages 1-7.
  4. Braubach, L., van der Hoek, W., Petta, P., and Pokahr, A., editors (2009). Towards Reactive Scheduling for Large-Scale Virtual Power Plants., volume 5774 of Lecture Notes in Computer Science. Springer.
  5. Bremer, J., Andreßen, S., Rapp, B., Sonnenschein, M., and Stadler, M. (2008). A modelling tool for interaction and correlation in demand-side market behaviour. New methods for energy market modelling, pages 77- 92.
  6. Bremer, J., Rapp, B., and Sonnenschein, M. (2010). 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.
  7. Bremer, J., Rapp, B., and Sonnenschein, M. (2011). Encoding distributed search spaces for virtual power plants. In IEEE Symposium Series on Computational Intelligence 2011 (SSCI 2011), Paris, France.
  8. Bremer, J. and Sonnenschein, M. (2013). Constrainthandling for optimization with support vector surrogate models - a novel decoder approach. In Filipe, J. and Fred, A., editors, ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence, volume 2, pages 91-105, Barcelona, Spain. SciTePress.
  9. Bremer, J. and Sonnenschein, M. (2014). Constrainthandling with support vector decoders. In Filipe, J. and Fred, A., editors, Agents and Artificial Intelligence, volume 449 of Communications in Computer and Information Science, pages 228-244. Springer Berlin Heidelberg.
  10. Chang, W.-C., Lee, C.-P., , and Lin, C.-J. (2013). A revisit to support vector data description (svdd). technical report, Department of Computer Science, National Taiwan University, Taipei 10617, Taiwan.
  11. Coello Coello, C. A. (2002). 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, 191(11-12):1245-1287.
  12. Coll-Mayor, D., Picos, R., and Garciá-Moreno, E. (2004). State of the art of the virtual utility: the smart distributed generation network. International Journal of Energy Research, 28(1):65-80.
  13. GhasemiGol, M., Sabzekar, M., Monsefi, R., Naghibzadeh, M., and Yazdi, H. S. (2010). A new support vector data description with fuzzy constraints. In Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation, ISMS 7810, pages 10-14, Washington, DC, USA. IEEE Computer Society.
  14. Hinrichs, C., Bremer, J., and Sonnenschein, M. (2013a). 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.
  15. Hinrichs, C., Sonnenschein, M., and Lehnhoff, S. (2013b). Evaluation of a Self-Organizing Heuristic for Interdependent Distributed Search Spaces. In Filipe, J. and Fred, A. L. N., editors, International Conference on Agents and Artificial Intelligence (ICAART 2013), volume Volume 1 - Agents, pages 25-34. SciTePress.
  16. Ilic, M. D. (2007). From hierarchical to open access electric power systems. Proceedings of the IEEE, 95(5):1060- 1084.
  17. Koziel, S. and Michalewicz, Z. (1999). Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evol. Comput., 7:19-44.
  18. Liu, B., Xiao, Y., Cao, L., Hao, Z., and Deng, F. (2013). Svdd-based outlier detection on uncertain data. Knowledge and Information Systems, 34(3):597-618.
  19. Lukovic, S., Kaitovic, I., Mura, M., and Bondi, U. (2010). Virtual power plant as a bridge between distributed energy resources and smart grid. Hawaii International Conference on System Sciences, 0:1-8.
  20. McArthur, S., Davidson, E., Catterson, V., Dimeas, A., Hatziargyriou, N., Ponci, F., and Funabashi, T. (2007). Multi-agent systems for power engineering applications - Part I: Concepts, approaches, and technical challenges. IEEE Transactions on Power Systems, 22(4):1743-1752.
  21. Neugebauer, J., Kramer, O., and Sonnenschein, M. (2015). 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), ECML/ PKDD 2015. Springer.
  22. Nieße, A., Beer, S., Bremer, J., Hinrichs, C., L ünsdorf, O., and Sonnenschein, M. (2014). Conjoint dynamic aggrgation and scheduling for dynamic virtual power plants. In Ganzha, M., Maciaszek, L. A., and Paprzycki, M., editors, Federated Conference on Computer Science and Information Systems - FedCSIS 2014, Warsaw, Poland.
  23. Nieße, A., Lehnhoff, S., Tr öschel, M., Uslar, M., Wissing, C., Appelrath, H.-J., and Sonnenschein, M. (2012). Market-based self-organized provision of active power and ancillary services: An agent-based approach for smart distribution grids. In COMPENG, pages 1-5. IEEE.
  24. Nieße, A. and Sonnenschein, M. (2013). Using grid related cluster schedule resemblance for energy rescheduling - goals and concepts for rescheduling of clusters in decentralized energy systems. In Donnellan, B., Martins, J. F., Helfert, M., and Krempels, K.-H., editors, SMARTGREENS, pages 22-31. SciTePress.
  25. Nikonowicz, L. B. and Milewski, J. (2012). Virtual power plants - general review: structure, application and optimization. Journal of Power Technologies, 92(3).
  26. Park, J., Kang, D., Kim, J., Kwok, J. T., and Tsang, I. W. (2007). Svdd-based pattern denoising. Neural Computing, 19(7):1919-1938.
  27. Powers, D. M. W. (2008). Evaluation evaluation. In Proceedings of the 2008 Conference on ECAI 2008: 18th European Conference on Artificial Intelligence, pages 843-844, Amsterdam, The Netherlands, The Netherlands. IOS Press.
  28. Powers, D. M. W. (2011). Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. Journal of Machine Learning Technologies, 2(1):37-63.
  29. Ramchurn, S. D., Vytelingum, P., Rogers, A., and Jennings, N. R. (2012). Putting the 'smarts' into the smart grid: a grand challenge for artificial intelligence. Commun. ACM, 55(4):86-97.
  30. Rapp, B. and Bremer, J. (2012). Design of an event engine for next generation cemis: A use case. In HansKnud Arndt, Gerlinde Knetsch, W. P. E., editor, EnviroInfo 2012 - 26th International Conference on Informatics for Environmental Protection, pages 753-760. Shaker Verlag. ISBN 978-3-8440-1248-4.
  31. Schölkopf, B. (1997). Support Vector Learning. Dissertation, Fachbereich 13 Informatik der Technischen Universität Berlin, Oldenbourg Verlag, München.
  32. Schölkopf, B., Mika, S., Burges, C., Knirsch, P., Müller, K.- R., Rätsch, G., and Smola, A. (1999). Input space vs. feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5):1000-1017.
  33. Sri, M., Huld, T., Dunlop, E. D., Albuisson, M., Lefevre, M., and Wald, L. (2007). Uncertainties in photovoltaic electricity yield prediction from fluctuation of solar radiation. In 22nd European Photovoltaic Solar Energy Conference.
  34. Stadler, P. D.-I. I. (2005). Demand Response: Nichtelektrische Speicher fr Elektrizittsversorgungssysteme mit hohem Anteil erneuerbarer Energien. Habilitation, Fachbereich Elektrotechnik, Universitt Kassel.
  35. Tax, D. M. J. and Duin, R. P. W. (2004). Support vector data description. Mach. Learn., 54(1):45-66.
  36. Wang, J., Botterud, A., Bessa, R., Keko, H., Carvalho, L., Issicaba, D., Sumaili, J., and Miranda, V. (2011). Wind power forecasting uncertainty and unit commitment. Applied Energy, 88(11):4014 - 4023.
  37. Wildt, T. (2014). Modelling uncertainty of household decision - making process in smart grid appliances adoption. In Behave Energy Conference, Oxford, UK.
  38. Witten, I. H., Frank, E., and Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Amsterdam, 3 edition.
  39. Wu, F., Moslehi, K., and Bose, A. (2005). Power system control centers: Past, present, and future. Proceedings of the IEEE, 93(11):1890-1908.
  40. Zhang, J., Hodge, B.-M., Gomez-Lazaro, E., Lovholm, A., Berge, E., Miettinen, J., Holttinen, H., and Cutululis, N. (2013). Analysis of Variability and Uncertainty in Wind Power Forecasting: An International Comparison. Energynautics GmbH.
  41. Zheng, E.-H., Yang, M., Li, P., and Song, Z.-H. (2006). Fuzzy support vector clustering. In Wang, J., Yi, Z., Zurada, J. M., Lu, B.-L., and Yin, H., editors, ISNN (1), volume 3971 of Lecture Notes in Computer Science, pages 1050-1056. Springer.
  42. Zio, E. and Aven, T. (2011). Uncertainties in smart grids behavior and modeling: What are the risks and vulnerabilities? how to analyze them? Energy Policy, 39(10):6308 - 6320. Sustainability of biofuels.
Download


Paper Citation


in Harvard Style

Bremer J. and Lehnhoff S. (2016). Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling . In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-172-4, pages 42-50. DOI: 10.5220/0005691600420050


in Bibtex Style

@conference{icaart16,
author={Jörg Bremer and Sebastian Lehnhoff},
title={Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2016},
pages={42-50},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005691600420050},
isbn={978-989-758-172-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Modeling Uncertainty in Support Vector Surrogates of Distributed Energy Resources - Enabling Robust Smart Grid Scheduling
SN - 978-989-758-172-4
AU - Bremer J.
AU - Lehnhoff S.
PY - 2016
SP - 42
EP - 50
DO - 10.5220/0005691600420050