Cluster-based distributed algorithm for energy management in smart grids

Abstract

In recent years the share of renewable energy sources has risen massively. This leads to for example supply fluctuations, thus forcing power grids to balance their energy demand and generation in order to guarantee operability. This paper proposes a cluster-based and privacy-friendly distributed algorithm for energy management in smart grids. Two clustering approaches are shown. The first one balances the energy consumption and generation of each cluster in a first stage, while adhering to a load limit. After that remaining capacities are distributed among unsatisfied clusters. The second one firstly reports the adaption potential and distributes it among all clusters. Simulation results show that both clustering approaches can improve the energy management, while establishing scalability. This is reached without drawbacks for the communication parameters and allows for an energy management interval of 1 min and a cluster size of 100 participants with a variable amount of clusters.

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References

  1. 1.

    CDU, CSU, SPD (2013) Deutschlands Zukunft gestalten—Koalitionsvertrag zwischen CDU, CSU und SPD. Legislaturperiode, Berlin DAAD Deutscher Akademischer Austauschdienst 18

  2. 2.

    Morais H, Kádár P, Faria P, Vale ZA, Khodr HM (2010) Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming. Renew Energy 35(1):151–156

    Article  Google Scholar 

  3. 3.

    Bu S, Yu FR, Liu PX (2011) Stochastic unit commitment in smart grid communications. In: IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, pp 307–312

  4. 4.

    Mohsenian-Rad A-H, Wong V, Jatskevich J, Schober R (2010) Optimal and autonomous incentive-based energy consumption scheduling algorithm for smart grid. In: Innovative Smart Grid Technologies (ISGT) 2010, pp 1–6

  5. 5.

    Caron S, Kesidis G (2010) Incentive-based energy consumption scheduling algorithms for the smart grid. In: 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp 391–396

  6. 6.

    Behrens D, Gerwig C (2014) Selbstregulierende verbraucher im smart grid: Design einer infrastruktur mit hilfe eines multi-agenten-systems. In: Multikonferenz Wirtschaftsinformatik, pp 935–948

  7. 7.

    Mets K, Strobbe M, Verschueren T, Roelens T, De Turck F, Develder C (2012) Distributed multi-agent algorithm for residential energy management in smart grids. In: 2012 IEEE Network Operations and Management Symposium (NOMS), pp 435–443

  8. 8.

    Lehnhoff S (2010) Dezentrales Vernetztes Energiemanagement: Ein Ansatz Auf Basis Eines Verteilten Adaptiven Realzeit-Multiagentensystems. Springer

  9. 9.

    Samadi P, Mohsenian-Rad A-H, Schober R, Wong V, Jatskevich J (2010) Optimal real-time pricing algorithm based on utility maximization for smart grid. In: 2010 First IEEE International Conference on Smart Grid Communications (SmartGridComm), pp 415–420

  10. 10.

    Zhang Y, Gatsis N, Giannakis G (2012) Robust distributed energy managementfor microgrids with renewables. In: 2012 IEEE 3rd InternationalConference on Smart Grid Communications (SmartGridComm), pp 510–515

  11. 11.

    Kok JK, Warmer CJ, Kamphuis IG (2005) PowerMatcher: multiagent control in the electricity infrastructure. In: Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems, pp 75–82

  12. 12.

    Pournaras E, Warnier M, Brazier FM (2010) Local agent-based self-stabilisation in global resource utilisation. Int J Auton Comput 1:350–373

    Article  MATH  Google Scholar 

  13. 13.

    Brettschneider D, Tönjes R, Roer P, Hölker D (2014) Distributed algorithm for energy management in smart grids. In: World Telecommunications Congress (WTC 2014), Berlin

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Correspondence to Daniel Brettschneider.

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Brettschneider, D., Hölker, D., Roer, P. et al. Cluster-based distributed algorithm for energy management in smart grids. Comput Sci Res Dev 31, 17–23 (2016). https://doi.org/10.1007/s00450-014-0292-6

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Keywords

  • Smart grid
  • Energy management
  • Distributed
  • Clustering