Skip to main content
View expanded cover

International Conference on the Applications of Evolutionary Computation

EvoApplications 2018: Applications of Evolutionary Computation pp 222–230Cite as

Phase-Space Sampling of Energy Ensembles with CMA-ES

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 10784)

Abstract

Smart grid control demands delegation of liabilities to distributed, small energy resources. Resource independent algorithm design demands abstraction from individual capabilities for integration into a general optimization model. For predictive scheduling with high penetration of renewable energy resources, agent approaches have shown good performance especially when using classifier-based decoders for modeling flexibilities. Such decoder-based methods currently are not able to cope with ensembles of individually acting energy resources. Aggregating training sets that are randomly sampled from phase-spaces of single units results in folded distributions with unfavorable properties for training a decoder. Nevertheless, for integrating e. g. a hotel, a small business, or similar with an ensemble of co-generation, heat pump, solar power, or controllable consumers a combined training set is needed. Thus, we improved the training process. We present an approach using evolution strategies for sampling ensembles that moves new instances to better positions according to spread and coverage of the feasible region. As a test case we use CMA-ES and present preliminary results demonstrating the applicability of the proposed approach.

Keywords

  • CMA-ES
  • Smart grid
  • Flexibility modeling
  • Folded distributions

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-77538-8_16
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-77538-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   143.00
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.

References

  1. Awerbuch, S., Preston, A.M. (eds.): The Virtual Utility: Accounting, Technology and Competitive Aspects of the Emerging Industry, Topics in Regulatory Economics and Policy, vol. 26. Kluwer Academic Publishers, New York (1997)

    Google Scholar 

  2. Padhy, N.: Unit commitment - a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004)

    CrossRef  Google Scholar 

  3. Nieße, A., Lehnhoff, S., Trschel, 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: 2012 Complexity in Engineering (COMPENG), pp. 1–5 (2012)

    Google Scholar 

  4. 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)

    Google Scholar 

  5. 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, vol. 2, pp. 91–105. SciTePress, Barcelona (2013)

    Google Scholar 

  6. Bremer, J., Lehnhoff, S.: Hybrid multi-ensemble scheduling. In: Squillero, G., Sim, K. (eds.) EvoApplications 2017. LNCS, vol. 10199, pp. 342–358. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55849-3_23

    CrossRef  Google Scholar 

  7. 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)

    Google Scholar 

  8. McArthur, S., Davidson, E., Catterson, V., Dimeas, A., Hatziargyriou, N., Ponci, F., Funabashi, T.: Multi-agent systems for power engineering applications - part I. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007)

    CrossRef  Google Scholar 

  9. Sonnenschein, M., Lünsdorf, O., Bremer, J., Tröschel, M.: Decentralized control of units in smart grids for the support of renewable energy supply. Environ. Impact Assess. Rev. 52, 40–52 (2014)

    Google Scholar 

  10. Bremer, J., Sonnenschein, M.: Sampling the search space of energy resources for self-organized, agent-based planning of active power provision. In: Page, B., Fleischer, A.G., Göbel, J., Wohlgemuth, V. (eds.) EnviroInfo 2013, pp. 214–222. Shaker (2013)

    Google Scholar 

  11. Hall, P.: 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 19(3/4), 240–245 (1927)

    CrossRef  MATH  Google Scholar 

  12. Duckham, M., Kulik, L., Worboys, M., Galton, A.: Efficient generation of simple polygons for characterizing the shape of a set of points in the plane. Patt. Recogn. 41(10), 3224–3236 (2008). http://www.sciencedirect.com/science/article/pii/S0031320308001180

    CrossRef  MATH  Google Scholar 

  13. Hansen, N.: The CMA Evolution Strategy: A Tutorial. Technical report, Inria Research Centre Saclay (2011). www.lri.fr/~hansen/cmatutorial.pdf

Download references

Author information

Authors and Affiliations

  1. University of Oldenburg, 26129, Oldenburg, Germany

    Jörg Bremer & Sebastian Lehnhoff

Authors
  1. Jörg Bremer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sebastian Lehnhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jörg Bremer .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

Rights and permissions

Reprints and Permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Bremer, J., Lehnhoff, S. (2018). Phase-Space Sampling of Energy Ensembles with CMA-ES. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77538-8_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)