Multi-agent Based Planning Considering the Behavior of Individual End-Users

  • Jan Kays
Part of the Power Systems book series (POWSYS)


The volatile feed-in of distributed generation based on renewable energy sources as well as new and intelligent loads and storages require an appropriate consideration in the distribution grid planning process. With the conventional planning method being dependent on extreme scenarios, the consideration is very limited. Therefore, a new planning tool based on the concept of a multi-agent system is presented. In this system, every network user is represented by an agent, allowing not only the consideration of the volatile feed-in characteristics of renewable energy sources but also of the dependencies between the network users and their environment. Every network user is modeled as an agent of its own, guaranteeing the preservation of its individual character. Within this chapter, a system overview is given and the agent design process demonstrated on the example of the household load agent and the storage agent, including negotiations. This multi-agent system generates time series for all relevant system variables, defining detailed input parameters in the distribution grid planning process. The probabilities of occurrence of loading situations can be derived from the time series. For the first time, this allows for a detailed determination of the conditions in the up to now rarely measured medium and low voltage grids. As a consequence, new assumptions for the planning process are derivable, permitting a demand- and future-oriented grid planning and avoiding over-dimensioning of the grids.


Distribution grid planning Multi agent system Time series Storage systems Distributed energy resources 


  1. 1.
    C. Baudot, G. Roupioz, A. Billet, Modernizing distribution network management with linky smart meters—lessons learned in greenlys project, in Proceedings Of CIRED 2015 (Lyon, France, 15–18 June, 2015)Google Scholar
  2. 2.
    G. Celli et al., A Comparison of distribution network planning solutions: traditional reinforcement versus integration of distributed energy storage, in IEEE PowerTech 2013 (Grenoble, France, 16–20 June 2013)Google Scholar
  3. 3.
    S. You et al., An overview of trends in distribution network planning: A movement towards smart planning, in T&D Conference and Exposition, 2014 IEEE PES (Chicago, USA, 14–17 April, 2014)Google Scholar
  4. 4.
    Cigré Task Force C6.19, Planning and Optimization Methods for Active Distribution Systems (2014)Google Scholar
  5. 5.
    A. Keane et al., State-of-the-art techniques and challenges ahead for distributed generation planning and optimization. IEEE Trans. Power Syst. 28(2), 1493–1502 (2013)CrossRefGoogle Scholar
  6. 6.
    P.S. Georgilakis, N.D. Hatziargyriou, Optimal distributed generation placement in power distribution networks: models, methods, and future research. IEEE Trans. Power Syst. 28(3), 3420–3428 (2013)CrossRefGoogle Scholar
  7. 7.
    S. Prabhakar Karthikeyan et al., A review on soft computing techniques for location and sizing of distributed generation systems, in 2012 International Conference on, Computing, Electronics and Electrical Technologies (ICCEET) (21–22 March, 2012)Google Scholar
  8. 8.
    P. Wiest et al., New Hybrid planning approach for distributions grids with a high penetration of RES, in Proceedings of CIRED 2015 (Lyon, France, 15–18 June, 2015Google Scholar
  9. 9.
    I. Ziari et al., Optimal distribution network reinforcement considering load growth, line loss, and reliability. IEEE Tran. Power Syst. 28(2), 587–597 (2013)CrossRefGoogle Scholar
  10. 10.
    V. Klonari etal., Probabilistic analysis tool of the voltage profile in low voltage grids, in Proceedings of CIRED 2015 (Lyon, France, 15–18 June, 2015)Google Scholar
  11. 11.
    D.F. Frame, G.W. Ault, S. Huang, The uncertainties of probabilistic LV network analysis, in IEEE Power and Energy Society General Meeting 2012 (San Diego, USA, 22–26 July, 2012)Google Scholar
  12. 12.
    C. Engels, L. Jendernalik, M. Osthues, H. Spitzer, ‘Smart planning’—an integrated approach for distribution system planning to cope with its future requirements, in Proceedinds of CIRED (Stockholm, Sweden, 10–13 June, 2013)Google Scholar
  13. 13.
    R.F. Arritt, R.C. Dugan, Value of sequential-time simulations in distribution planning. IEEE Trans. Ind. Appl. 50(6), 4216–4220 (2014)CrossRefGoogle Scholar
  14. 14.
    E.Tønne, J.A. Foosnæs, T.Pynten, Power system planning in distribution networks today and in the future with Smart Grids, in Proceedings of CIRED (Stockholm, Sweden, 10–13 June, 2013)Google Scholar
  15. 15.
    G. Roupioz, X. Robe, F. Gorgette, First use of smart grid data in distribution network planning, in Proceedings of CIRED (Stockholm, Sweden, 10–13 June, 2013)Google Scholar
  16. 16.
    M. Nick et al., On the optimal placement of distributed storage systems for voltage control in active distribution networks, in IEEE PES Innovative Smart Grid Technologies (ISGT Europe) (Berlin, Germany, 14–17 Oct, 2012)Google Scholar
  17. 17.
    N. Siebert et al., Scheduling demand response and Smart Battery flexibility in a market environment: results from the Reflexe demonstrator project, in IEEE PowerTech 2015 (Eindhoven, Netherlands, June 29–July 2, 2015)Google Scholar
  18. 18.
    R. Roche et al., A multi-agent model and strategy for residential demand response coordination, in IEEE PowerTech 2015 (Eindhoven, Netherlands, June 29–July 2, 2015)Google Scholar
  19. 19.
    N.G. Paterakis et al., Distribution system operation enhancement through household consumption coordination in a dynamic pricing environment, in IEEE PowerTech 2015 (Eindhoven, Netherlands, June 29–July 2, 2015)Google Scholar
  20. 20.
    S. Bashash, H.K. Fathy, Cost-optimal charging of plug-in hybrid electric vehicles under time-varying electricity price signals. IEEE Trans. Intell. Transp. Syst. 15(5), 1958–1968 (2014)CrossRefGoogle Scholar
  21. 21.
    F. Boulaire et al., A hybrid simulation framework to assess the impact of renewable generators on a distribution network, in Proceedings of the 2012 Winter Simulation Conference (WSC) (Berlin, Germany, 9–12 Dec, 2012)Google Scholar
  22. 22.
    P. Jahangiri et al., Development of an agent-based distribution test feeder with smart-grid functionality, in IEEE Power and Energy Society General Meeting 2012 (San Diego, USA, 22–26 July, 2012)Google Scholar
  23. 23.
    C. Chengrui et al., Agent-based simulation of distribution systems with high penetration of photovoltaic generation, in Power and Energy Society General Meeting, 2011 IEEE (24–29 July, 2011)Google Scholar
  24. 24.
    J. Hu et al., Multi-agent based modeling for electric vehicle integration in a distribution network operation. Electr. Power Syst. Res. 136, 341–351 (2016)CrossRefGoogle Scholar
  25. 25.
    T. Pinto et al., Smart grid and electricity market joint simulation using complementary multi-agent platforms, in IEEE PowerTech 2015 (Eindhoven, Netherlands, June 29–July 2, 2015)Google Scholar
  26. 26.
    L. Hattam, D.V. Greetham, Green neighbourhoods in low voltage networks: measuring impact of electric vehicles and photovoltaics on load profiles. J. Mod. Power Syst. Clean Energy 5(1), 105–116 (2017)CrossRefGoogle Scholar
  27. 27.
    E.F. Bompard, B. Han, Market-based control in emerging distribution system operation. IEEE Trans. Power Delivery 28(4), 2373–2382 (2013)CrossRefGoogle Scholar
  28. 28.
    S.D.J McArthur et al., Multi-agent systems for power engineering applications—part I: concepts, approaches, and technical challenges. IEEE Trans. Power Syst. 22(4), 1743–1752 (2007)CrossRefGoogle Scholar
  29. 29.
    C. Rehtanz, Autonomous Systems and Intelligent Agents in Power System Control and Operation (Springer, Berlin, New York, 2003). ISBN 3540402020CrossRefGoogle Scholar
  30. 30.
    A. Prostejovsky et al., Demonstration of a multi-agent-based control system for active electric power distribution grids, in IEEE International Workshop on Intelligent Energy Systems (IWIES) 2013 (Vienna, Austria, 14 Nov, 2013)Google Scholar
  31. 31.
    F.I. Hernandez et al., Active power management in multiple microgrids using a multi-agent system with JADE, in International Conference on Industry Applications (INDUSCON), 2014 11th IEEE/IAS (Juiz de Fora, Brazil, 7–10 Dec, 2014)Google Scholar
  32. 32.
    E. Polymeneas, M. Benosman, Multi-agent coordination of DG inverters for improving the voltage profile of the distribution grid, in IEEE PES General Meeting 2014 (National Harbor, USA, 27–31 July, 2014)Google Scholar
  33. 33.
    E.A.M. Klaassen et al., Integration of in-home electricity storage systems in a multi-agent active distribution network, in IEEE PES General Meeting 2014 (National Harbor, USA, 27–31 July, 2014)Google Scholar
  34. 34.
    I. Pisica et al., A multi-agent model for assessing electricity tariffs, in Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2014 IEEE PES (Istanbul, Turkey, 12–15 Oct, 2014)Google Scholar
  35. 35.
    G.H. Merabet et al., Applications of multi-agent systems in smart grids: a survey, in International Conference on Multimedia Computing and Systems (ICMCS) 2014 (Marrakech, Marocco, 14–16 April, 2014)Google Scholar
  36. 36.
    J. Kays, Agent-based Simulation Environment for Improving the Planning of Distribution Grids, Ph.D. thesis, TU Dortmund University, Dortmund, Germany, 2014, ISBN 9783868446623Google Scholar
  37. 37.
    A. Seack, Time-series Based Distribution Grid Planning Considering Interaction of Network Participants with A Multi-agent System, Ph.D. thesis, TU Dortmund University, Dortmund, Germany, 2016Google Scholar
  38. 38.
    J.Kays, A. Seack, C. Rehtanz, Consideration of smart-meter measurements in a multi-agent simulation environment for improving distribution grid planning, in Innovative Smart Grid Technologies Conference (ISGT), 2016 IEEE PES (Minneapolis, USA, 6–9 Sept, 2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Amprion GmbHDortmundGermany
  2. 2.TU Dortmund UniversityDortmundGermany

Personalised recommendations