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access icon free Distributed algorithm for dynamic economic power dispatch with energy storage in smart grids

The dynamic economic dispatch problem with energy storage in a smart grid scenario is studied, which aims at minimising the aggregate generation costs over multiple periods on condition that the time-varying demand is met, while physical constraints on generation and storage as well as system spinning reserve requirement are satisfied. In our model, energy storage devices are incorporated for not only inter-temporal energy arbitrage to reduce total generation cost, but also providing spinning reserve to share generators' burden. To solve this problem, we assume that the communication networks are strongly connected directed graphs and propose a fully distributed algorithm based on the ‘consensus-like’ iterative algorithm and the alternating direction method of multipliers. Our algorithm is distributed in the sense that no leader or master nodes are needed, while all the nodes conduct local computation and communicate merely with their neighbours. Numerical simulation is included to show the effectiveness of the proposed algorithm.

References

    1. 1)
      • 28. Boyd, S.P., Vandenberghe, L.: ‘Convex optimization’ (Cambridge University Press, 2004).
    2. 2)
      • 25. Castillo, A., Gayme, D.F.: ‘Grid-scale energy storage applications in renewable energy integration: a survey’, Energy Convers. Manage., 2014, 87, pp. 885894.
    3. 3)
      • 20. Xing, H., Mou, Y., Fu, M., et al: ‘Distributed bisection method for economic power dispatch in smart grid’, IEEE Trans. Power Syst., 2015, 30, (6), pp. 30243035.
    4. 4)
      • 4. Attaviriyanupap, P., Kita, H., Tanaka, E., et al: ‘A hybrid EP and SQP for dynamic economic dispatch with nonsmooth fuel cost function’, IEEE Trans. Power Syst., 2002, 17, (2), pp. 411416.
    5. 5)
      • 1. Wood, A.J., Wollenberg, B.F.: ‘Power generation, operation, and control’ (John Wiley & Sons, 1996).
    6. 6)
      • 6. Denholm, P., Ela, E., Kirby, B., et al: ‘The role of energy storage with renewable electricity generation’ (National Renewable Energy Laboratory, 2010).
    7. 7)
      • 11. Giselsson, P., Doan, M.D., Keviczky, T., et al: ‘Accelerated gradient methods and dual decomposition in distributed model predictive control’, Automatica, 2013, 49, (3), pp. 829833.
    8. 8)
      • 17. Zhang, Z., Chow, M.Y.: ‘Incremental cost consensus algorithm in a smart grid environment’. Proc. IEEE Power and Energy Society General Meeting, San Diego, CA, 2011, pp. 16.
    9. 9)
      • 22. Horn, R.A., Johnson, C.R.: ‘Matrix analysis’ (Cambridge University Press, 1985).
    10. 10)
      • 13. Smith, R.S., Hadaegh, F.Y.: ‘Distributed estimation, communication and control for deep space formations’, IET Control Theory Applic., 2007, 1, (2), pp. 445451.
    11. 11)
      • 15. Moslehi, K., Kumar, R.: ‘A reliability perspective of the smart grid’, IEEE Trans. Smart Grid, 2010, 1, (1), pp. 5764.
    12. 12)
      • 2. Xia, X., Elaiw, A.: ‘Optimal dynamic economic dispatch of generation: a review’, Electr. Power Syst. Res., 2010, 80, (8), pp. 975986.
    13. 13)
      • 19. Xing, H., Mou, Y., Fu, M., et al: ‘Consensus based bisection approach for economic power dispatch’. Proc. IEEE Conf. on Decision and Control, 2014, pp. 37893794.
    14. 14)
      • 7. Xiaoping, L., Ming, D., Jianghong, H., et al: ‘Dynamic economic dispatch for microgrids including battery energy storage’. Proc. 2nd IEEE Int. Symp. on Power Electronics for Distributed Generation Systems (PEDG), 2010, pp. 914917.
    15. 15)
      • 18. Yang, S., Tan, S., Xu, J.X.: ‘Consensus based approach for economic dispatch problem in a smart grid’, IEEE Trans. Power Syst., 2013, 28, (4), pp. 44164426.
    16. 16)
      • 21. Lin, Z.: ‘Distributed control and analysis of coupled cell systems’ (VDM Publishing, 2008).
    17. 17)
      • 5. Pothiya, S., Ngamroo, I., Kongprawechnon, W.: ‘Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints’, Energy Convers. Manage., 2008, 49, (4), pp. 506516.
    18. 18)
      • 23. Boyd, S., Parikh, N., Chu, E., et al: ‘Distributed optimization and statistical learning via the alternating direction method of multipliers’, Found. Trends Mach. Learn., 2011, 3, (1), pp. 1122.
    19. 19)
      • 24. California Independent System Operator: ‘Flexible ramping product, business requirements specification’, 2016, http://www.caiso.com/Documents/BusinessRequirementsSpecification-FlexibleRampingProduct-Redline.pdf, accessed 1 February 2017.
    20. 20)
      • 29. Christie, R.D.: ‘Power system test case archive’, 1993, http://www.ee.washington.edu/research/pstca/, accessed 1 February 2017.
    21. 21)
      • 12. Mahmoud, M.S., Khalid, H.M.: ‘Distributed Kalman filtering: a bibliographic review’, IET Control Theory Applic., 2013, 7, (4), pp. 483501.
    22. 22)
      • 10. Giovanini, L.: ‘Game approach to distributed model predictive control’, IET Control Theory Applic., 2011, 5, (15), pp. 17291739.
    23. 23)
      • 3. Han, X., Gooi, H., Kirschen, D.S.: ‘Dynamic economic dispatch: feasible and optimal solutions’, IEEE Trans. Power Syst., 2001, 16, (1), pp. 2228.
    24. 24)
      • 8. Nikmehr, N., Ravadanegh, S.N.: ‘Optimal power dispatch of multi-microgrids at future smart distribution grids’, IEEE Trans. Smart Grid, 2015, 6, (4), pp. 16481657.
    25. 25)
      • 27. Xiao, L., Boyd, S.: ‘Optimal scaling of a gradient method for distributed resource allocation’, J. Optim. Theory Appl., 2006, 129, (3), pp. 469488.
    26. 26)
      • 14. Bakule, L.: ‘Decentralized control: an overview’, Annu. Rev. Control, 2008, 32, (1), pp. 8798.
    27. 27)
      • 9. Irwin, G.W., Chen, J.J., McKernan, A., et al: ‘Co-design of predictive controllers for wireless network control’, IET Control Theory Appl., 2010, 4, (2), pp. 186196.
    28. 28)
      • 26. Castillo, A., Gayme, D.F.: ‘Profit maximizing storage allocation in power grids’. Proc. 52nd IEEE Conf. on Decision and Control, 2013, pp. 429435.
    29. 29)
      • 16. Zhang, Z., Ying, X., Chow, M.Y.: ‘Decentralizing the economic dispatch problem using a two-level incremental cost consensus algorithm in a smart grid environment’. Proc. IEEE North American Power Symp. (NAPS), Boston, MA, 2011, pp. 17.
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