Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Consensus-based distributed scheduling for cooperative operation of distributed energy resources and storage devices in smart grids

Optimal dispatch of storage devices is crucial for the economic operation of smart grids with distributed energy resources. Through appropriate scheduling, storage devices can store the energy when the renewable production is high or electricity price is low, and support the demand when electricity is expensive. Conventionally, this scheduling requires a control centre to gather information from the entire system and find the optimal schedule in the required horizon for the controllable devices. This study proposes a fully distributed scheduling methodology based on discrete-time optimal control, primal-dual gradient descent, and consensus networks. In the proposed approach, the requirement for the control centre is eliminated and the optimal schedule for all the devices is found solely through iterative coordination of each device with its neighbours. The application of the algorithm is demonstrated in a 5-bus system and its convergence to the global optimum is validated through Monte Carlo simulations. Further, it is shown that the algorithm is robust against communication link failures provided that the communications topology remains connected or reconnects after being disconnected.

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
      • 22. Bertsekas, D.: ‘Nonlinear Programming’, 1999.
    6. 6)
      • 12. Binetti, G., Davoudi, A., Naso, D., et al: ‘A distributed auction-based algorithm for the nonconvex economic dispatch problem’, IEEE Trans. Ind. Electron., 2014, 10, (2), pp. 11241132.
    7. 7)
    8. 8)
    9. 9)
      • 21. Boyd, S., Vandenberghe, L.: ‘Convex optimization’ (Cambridge university press, 2004).
    10. 10)
    11. 11)
    12. 12)
    13. 13)
    14. 14)
    15. 15)
    16. 16)
    17. 17)
      • 24. Zhang, Y., Chow, M.-Y.: ‘Distributed optimal generation dispatch considering transmission losses’. 2015 North American Power Symp., 2015.
    18. 18)
      • 2. ‘Grid Energy Storage’, US Dep. Energy, 2013.
    19. 19)
      • 19. Grainger, J.J.: ‘Power system analysis’ (McGraw-Hill Education, 2003).
    20. 20)
    21. 21)
    22. 22)
    23. 23)
      • 10. Kar, S., Hug, G.: ‘Distributed robust economic dispatch in power systems: a consensus + innovations approach’. Power and Energy Society General Meeting, 2012, pp. 18.
    24. 24)
      • 20. Zhu, M., Martinez, S.: ‘On distributed optimization under inequality constraints via Lagrangian primal-dual methods’. American Control Conf. 2010, Marriott Waterfront, Baltimore, MD, USA, 2010, pp. 48634868.
    25. 25)
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-gtd.2015.0159
Loading

Related content

content/journals/10.1049/iet-gtd.2015.0159
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address