Skip to main content
SpringerLink
Log in

Constrained Scheduling of Step-Controlled Buffering Energy Resources with Ant Colony Optimization

  • Conference paper
  • First Online:
Swarm Intelligence (ANTS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12421))

Included in the following conference series:

Abstract

The rapidly changing paradigm in energy supply with a shift of operational responsibility towards distributed and highly fluctuating renewables demands for proper integration and coordination of a broad variety of small generation and consumption units. Many use cased demand for optimized coordination of electricity production or consumption schedules. In the discrete case, this is an NP-hard problem for step-controlled devices if some sort of intermediate energy buffer is involved. Systematically constructing feasible solutions during optimization degenerates to a difficult task. We present a model-integrated approach based on ant colony optimization. By using a simulation model for deciding on feasible branches (follow-up power operation levels), ants construct the feasible search graph on demand, thus avoiding exponential growth in this combinatorial problem. Applicability and competitiveness are demonstrated in several simulation studies using a model for a co-generation plant as typical small sized smart grid generation unit.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Beaudin, M., Zareipour, H.: Home energy management systems: a review of modelling and complexity. Renew. Sustain. Energy Rev. 45, 318–335 (2015). https://doi.org/10.1016/j.rser.2015.01.046

    Article  Google Scholar 

  2. Behrangrad, M.: A review of demand side management business models in the electricity market. Renew. Sustain. Energy Rev. 47, 270–283 (2015). https://doi.org/10.1016/j.rser.2015.03.033

    Article  Google Scholar 

  3. Boynuegri, A.R., Yagcitekin, B., Baysal, M., Karakas, A., Uzunoglu, M.: Energy management algorithm for smart home with renewable energy sources. In: 4th International Conference on Power Engineering, Energy and Electrical Drives, pp. 1753–1758 (2013)

    Google Scholar 

  4. Bremer, J., Lehnhoff, S.: A decentralized PSO with decoder for scheduling distributed electricity generation. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9597, pp. 427–442. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31204-0_28

    Chapter  Google Scholar 

  5. Bremer, J., Lehnhoff, S.: Hybridizing S-metric selection and support vector decoder for constrained multi-objective energy management. In: Madureira, A.M., Abraham, A., Gandhi, N., Varela, M.L. (eds.) HIS 2018. AISC, vol. 923, pp. 249–259. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-14347-3_24

    Chapter  Google Scholar 

  6. Bremer, J., Rapp, B., Jellinghaus, F., Sonnenschein, M.: Tools for teaching demand-side management. In: EnviroInfo (1), pp. 475–483. Shaker Verlag, Aachen (2009)

    Google Scholar 

  7. Bremer, J., Sonnenschein, M.: Constraint-handling for optimization with support vector surrogate models-a novel decoder approach. In: International Conference on Agents and Artificial Intelligence, vol. 2, pp. 91–100. SciTePress (2013)

    Google Scholar 

  8. Bremer, J.: Agenten-basierte simulation des planungsverhaltens adaptiver verbraucher in stromversorgungssystemen mit real-time-pricing. Diploma thesis, C.v.O. Universität Oldenburg, Department für Informatik (Abteilung Umweltinformatik), March 2006

    Google Scholar 

  9. Capone, A., Barbato, A., Martignon, F., Chen, L., Paris, S.: A power scheduling game for reducing the peak demand of residential users, October 2013. https://doi.org/10.1109/OnlineGreenCom.2013.6731042

  10. De Angelis, F., Boaro, M., Fuselli, D., Squartini, S., Piazza, F., Wei, Q.: Optimal home energy management under dynamic electrical and thermal constraints. IEEE Trans. Ind. Inf. 9(3), 1518–1527 (2013)

    Article  Google Scholar 

  11. Deng, R., Yang, Z., Chow, M.Y., Chen, J.: A survey on demand response in smart grids: mathematical models and approaches. IEEE Trans. Ind. Inf. 11(3), 570–582 (2015)

    Article  Google Scholar 

  12. Dethlefs, T., Preisler, T., Renz, W.: Ant-colony based self-optimization for demand-side-management. In: Weber, C., Derksen, C. (eds.) Proceedings SmartER Europe Conference. Essen (2015)

    Google Scholar 

  13. Dorigo, M., Caro, G.D., Gambardella, L.M.: Ant algorithms for discreteoptimization. Artif. Life 5(2), 137–172 (1999). https://doi.org/10.1162/106454699568728

    Article  Google Scholar 

  14. Dorigo, M., Di Caro, G.: Ant colony optimization: a new meta-heuristic. In: Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406), vol. 2, pp. 1470–1477. IEEE (1999)

    Google Scholar 

  15. Dorigo, M., Stützle, T.: The ant colony optimization metaheuristic: algorithms, applications, and advances. In: Glover, F., Kochenberger, G.A. (eds.) Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 57, pp. 250–285. Springer, Boston (2003). https://doi.org/10.1007/0-306-48056-5_9

    Chapter  Google Scholar 

  16. Gellings, C.W., Parmenter, K.E.: Demand-side management. In: Energy Management and Conservation Handbook, pp. 399–420. CRC Press (2016)

    Google Scholar 

  17. Halton, J., Smith, G.: Radical inverse quasi-random point sequence, algorithm 247. Commun. ACM 7, 701 (1964)

    Article  Google Scholar 

  18. Hansen, N.: The CMA evolution strategy: a comparing review. In: Lozano, J., Larranaga, P., Inza, I., Bengoetxea, E. (eds.) Towards a New Evolutionary Computation. Advances on Estimation of Distribution Algorithms, vol. 192, pp. 75–102. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-32494-1_4

    Chapter  Google Scholar 

  19. Hansen, N.: The CMA evolution strategy: a tutorial. Technical report (2011)

    Google Scholar 

  20. 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). https://doi.org/10.1109/ISGTEurope.2013.6695312

  21. Hinrichs, C., Lehnhoff, S., Sonnenschein, M.: A decentralized heuristic for multiple-choice combinatorial optimization problems. In: Stefan, H., et al. (eds.) Operations Research Proceedings 2012. ORP, pp. 297–302. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-00795-3_43

    Chapter  Google Scholar 

  22. Hinrichs, C., Sonnenschein, M.: A distributed combinatorial optimisation heuristic for the scheduling of energy resources represented by self-interested agents. Int. J. Bio-Inspired Comput. 10, 69 (2017). https://doi.org/10.1504/IJBIC.2017.085895

    Article  Google Scholar 

  23. Khan, A.R., Mahmood, A., Safdar, A., Khan, Z.A., Khan, N.A.: Load forecasting, dynamic pricing and DSM in smart grid: a review. Renew. Sustain. Energy Rev. 54, 1311–1322 (2016)

    Article  Google Scholar 

  24. Koch, S., Zima, M., Andersson, G.: Potentials and applications of coordinated groups of thermal household appliances for power system control purposes. In: 2009 IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE), pp. 1–8 (2009)

    Google Scholar 

  25. Kuipers, L., Niederreiter, H.: Uniform Distribution of Sequences. Dover Books on Mathematics, Dover Publications (2006)

    MATH  Google Scholar 

  26. Li, Y., Rezgui, Y., Zhu, H.: District heating and cooling optimization and enhancement - towards integration of renewables, storage and smart grid. Renew. Sustain. Energy Rev. 72, 281–294 (2017). https://doi.org/10.1016/j.rser.2017.01.061

    Article  Google Scholar 

  27. Neugebauer, J., Kramer, O., Sonnenschein, M.: Classification cascades of overlapping feature ensembles for energy time series data. In: Woon, W.L., Aung, Z., Madnick, S. (eds.) DARE 2015. LNCS (LNAI), vol. 9518, pp. 76–93. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27430-0_6

    Chapter  Google Scholar 

  28. Nieße, A., et al.: Market-based self-organized provision of active power and ancillary services: an agent-based approach for smart distribution grids. In: Proceedings on Complexity in Engineering (COMPENG), pp. 1–5. IEEE (2012)

    Google Scholar 

  29. Nieße, A., Sonnenschein, M.: A fully distributed continuous planning approach for decentralized energy units. In: Cunningham, D.W., Hofstedt, P., Meer, K., Schmitt, I. (eds.) Informatik 2015. GI-Edition - Lecture Notes in Informatics (LNI), vol. 246, pp. 151–165. Bonner Köllen Verlag (2015)

    Google Scholar 

  30. Nieße, A., Sonnenschein, M., Hinrichs, C., Bremer, J.: Local soft constraints in distributed energy scheduling. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Proceedings of the 2016 Federated Conference on Computer Science and Information Systems. Annals of Computer Science and Information Systems, vol. 8, pp. 1517–1525. IEEE (2016). https://doi.org/10.15439/2016F76

  31. Nosratabadi, S.M., Hooshmand, R.A., Gholipour, E.: A comprehensive review on microgrid and virtual power plant concepts employed for distributed energy resources scheduling in power systems. Renew. Sustain. Energy Rev. 67, 341–363 (2017)

    Article  Google Scholar 

  32. Ostermeier, A., Gawelczyk, A., Hansen, N.: A derandomized approach to self-adaptation of evolution strategies. Evol. Comput. 2(4), 369–380 (1994)

    Article  Google Scholar 

  33. Palensky, P., Dietrich, D.: Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans. Industr. Inf. 7(3), 381–388 (2011)

    Article  Google Scholar 

  34. Ramchurn, S.D., Vytelingum, P., Rogers, A., Jennings, N.R.: Putting the ‘smarts’ into the smart grid: a grand challenge for artificial intelligence. Commun. ACM 55(4), 8697 (2012). https://doi.org/10.1145/2133806.2133825

    Article  Google Scholar 

  35. Ruiz-Romero, S., Colmenar-Santos, A., Mur-Pérez, F.: Putting the ‘smarts’ into the smart grid: a grand challenge for artificial intelligence. Renew. Sustain. Energy Rev. 38, 223–234 (2014). https://doi.org/10.1016/j.rser.2014.05.082

    Article  Google Scholar 

  36. Saboori, H., Mohammadi, M., Taghe, R.: Virtual power plant (VPP), definition, concept, components and types. In: Asia-Pacific Power and Energy Engineering Conference, pp. 1–4. IEEE (2011)

    Google Scholar 

  37. Sarstedt, M., et al.: Standardized evaluation of multi-level grid control strategies for future converter-dominated electric energy systems. In: at-Automatisierungstechnik, vol. 67 (2019)

    Google Scholar 

  38. Smith, A., Coit, D.: Handbook of Evolutionary Computation, chap. Penalty Functions, p. Section C5.2. Department of Industrial Engineering, University of Pittsburgh, USA. Oxford University Press and IOP Publishing (1997)

    Google Scholar 

  39. Sonnenschein, M., Stadler, M., Rapp, B., Bremer, J., Brunhorn, S.: A modelling and simulation environment for real-time pricing scenarios in energy markets. In: Managing Environmental Knowledge (2006)

    Google Scholar 

  40. Yu, T., Kim, D.S., Son, S.Y.: Home appliance scheduling optimization with time-varying electricity price and peak load limitation. In: The 2nd International Conference on Information Science and Technology, IST, pp. 196–199 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Division of Energy Informatics, Department of Computing Science, University of Oldenburg, Oldenburg, Germany

    Jörg Bremer & Sebastian Lehnhoff

  2. OFFIS Institute for Information Technology, Oldenburg, Germany

    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. Université Libre de Bruxelles, Brussels, Belgium

    Marco Dorigo

  2. Université Libre de Bruxelles, Brussels, Belgium

    Thomas Stützle

  3. Universitat Politècnica de Catalunya, Barcelona, Spain

    Maria J. Blesa

  4. Artificial Intelligence Research Institute, Bellaterra, Spain

    Christian Blum

  5. University of Lübeck, Lübeck, Germany

    Heiko Hamann

  6. Université Libre de Bruxelles, Brussels, Belgium

    Mary Katherine Heinrich

  7. Université Libre de Bruxelles, Brussels, Belgium

    Volker Strobel

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bremer, J., Lehnhoff, S. (2020). Constrained Scheduling of Step-Controlled Buffering Energy Resources with Ant Colony Optimization. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60376-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60375-5

  • Online ISBN: 978-3-030-60376-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation