Energy Systems

, Volume 8, Issue 3, pp 643–667 | Cite as

An economic dispatch algorithm for congestion management of smart power networks

An oblivious routing approach
  • Kianoosh G. Boroojeni
  • M. Hadi Amini
  • S. S. Iyengar
  • Mohsen Rahmani
  • Panos M. Pardalos
Original Paper


We present a novel oblivious routing economic dispatch (ORED) algorithm for power systems. The method is inspired by the oblivious network design which works perfectly for networks in which different sources (generators) send power flow toward their destinations (load points) while they are unaware of the current network state and other flows. Basically, our focus is on the economic dispatch while managing congestion and mitigating power losses. Furthermore, we studied a loss-minimizing type of the economic dispatch which aims to minimize the emission by optimizing the total power generation rather than system cost. Comparing to state-of-the-art economic dispatch methods, our algorithm is independent of network topology and works for both radial and non-radial networks. Our algorithm is thus suited for large-scale economic dispatch problems that will emerge in the future smart distribution grids with host of small, decentralized, and flexibly controllable prosumers; i.e., entities able to consume and produce electricity. The effectiveness of the proposed ORED is evaluated via the IEEE 57-bus standard test system. The simulation results verify the superior performance of the proposed method over the current methods in the literature in terms of congestion management and power loss minimization.


Congestion management Economic dispatch Loss minimization Oblivious network design Smart power network 


  1. 1.
    Kar, S., Hug, G., Mohammadi, J., Moura, J.M.F.: Distributed state estimation and energy management in smart grids: a consensus+ innovations approach. IEEE Trans. Select. Topics Signal Process. 8(6), 1022–1038 (2014)CrossRefGoogle Scholar
  2. 2.
    U.S. Department of Energy: The Smart Grid: An Introduction. (2008)Google Scholar
  3. 3.
    Zidan, A., El-Saadany, E.F.: A cooperative multi-agent framework for self-healing mechanisms in distribution systems. IEEE Trans. Smart Grid 3(3), 1525–1539 (2012)CrossRefGoogle Scholar
  4. 4.
    Brown, R.E.: Impact of smart grid on distribution system design. In: Proceedings of IEEE Power and Energy Society General Meeting, pp. 1–4, Pittsburgh, PA (2008)Google Scholar
  5. 5.
    Chowdhury, B.H., Rahman, S.: A review of recent advances in economic dispatch. IEEE Trans. Power Syst. 5(4), 1248–1259 (1990)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Pappu, V., Carvalho, M., Pardalos, P.: Optimization and Security Challenges in Smart Power Grids. Springer, New York (2013)CrossRefGoogle Scholar
  7. 7.
    Zhu, J.: Optimization of Power System Operation. Wiley, Amsterdam (2014)Google Scholar
  8. 8.
    Padhy, N.: Unit commitment: a bibliographical survey. IEEE Trans. Power Syst. 19(2), 1196–1205 (2004)CrossRefGoogle Scholar
  9. 9.
    Selvakumar, A.I., Thanushkodi, K.: A new particle swarm optimization solution to nonconvex economic dispatch problems. IEEE Trans. Power Syst. 22(1), 42–51 (2007)CrossRefGoogle Scholar
  10. 10.
    Amini, M.H., Nabi, B., Haghifam, M.-R.: Load management using multi-agent systems in smart distribution network. In: Proceedings of IEEE Power and Energy Society General Meeting, Vancouver, BC, Canada, pp. 1–5 (2013)Google Scholar
  11. 11.
    Lavaei, J., Tse, D., Zhang, B.: Geometry of power flows and optimization in distribution networks. IEEE Trans. Power Syst. 29(2), 572–583 (2014)CrossRefGoogle Scholar
  12. 12.
    Sanjari, M.J., Karami, H., Gooi, H.B.: Micro-generation dispatch in a smart residential multi-carrier energy system considering demand forecast error. Energy Convers. Manag. 120, 90–99 (2016)CrossRefGoogle Scholar
  13. 13.
    Kellerer, E., Steinke, F.: Scalable economic dispatch for smart distribution networks. IEEE Trans. Power Syst. 99, 1–8 (2014)Google Scholar
  14. 14.
    Carrion, M., Arroyo, J.: A computationally efficient mixed-integer linear formulation for the thermal unit commitment problem. IEEE Trans. Power Syst. 21(3), 1371–1378 (2006)CrossRefGoogle Scholar
  15. 15.
    Botterud, A., Zhi, Z., Wang, J., et al.: Demand dispatch and probabilistic wind power forecasting in unit commitment and economic dispatch: a case study of Illinois. IEEE Trans. Sustain. Energy 4(1), 250–261 (2012)CrossRefGoogle Scholar
  16. 16.
    Li, Z., Guo, Q., Sun, H., Wang, J.: Sufficient conditions for exact relaxation of complementarity constraints for storage-concerned economic dispatch. IEEE Trans. Power Syst. 99, 1–2 (2015)Google Scholar
  17. 17.
    Khator, S.K., Leung, L.C.: Power distribution planning: a review of models and issues. IEEE Trans. Power Syst. 12(3), 1151–1159 (1997)CrossRefGoogle Scholar
  18. 18.
    Liu, Y., Ul Hassan, N., Huang, S., Yuen, C.: Electricity cost minimization for a residential smart grid with distributed generation and bidirectional power transactions. IEEE Innovat. Smart Grid Technol. (ISGT), pp. 1–6, Washington, DC (2013)Google Scholar
  19. 19.
    Papavasiliou, A., Oren, S.S.: Supplying renewable energy to deferrable loads: algorithms and economic analysis. In: Proceedings of IEEE Power and Energy Society General Meeting, pp. 1–8, Minneapolis, MN (2010)Google Scholar
  20. 20.
    Boroojeni, K.G., et al.: Optimal two-tier forecasting power generation model in smart grids. Int. J. Inf. Process. 8(4), 79–88 (2014)Google Scholar
  21. 21.
    Iyengar, S.S., Boroojeni, K.G.: Oblivious Network Routing: Algorithms and Applications. MIT Press, New York (2015)MATHGoogle Scholar
  22. 22.
    Gupta, A., Hajiaghayi, M.T., Racke, H.: Oblivious network design. In: SODA 06: Proceedings of the 17th Annual ACM-SIAM Symposium on Discrete Algorithm. New York, NY, USA: ACM, pp. 970–979 (2006)Google Scholar
  23. 23.
    Maknouninejad, A., Lin, W., Harno, H.G., Qu, Z., Simaan, M.A.: Cooperative control for self-organizing microgrids and game strategies for optimal dispatch of distributed renewable generations. Energy Syst. 3(1), 23–60 (2012)CrossRefGoogle Scholar
  24. 24.
    Tuffaha, M., Gravdahl, J.T.: Discrete state-space model to solve the unit commitment and economic dispatch problems. Energy Syst. pp. 1–23, in Press (2016)Google Scholar
  25. 25.
    Frank, S., Rebennack, S.: An introduction to optimal power flow: theory, formulation, and examples. IIE Trans. to appear (2016)Google Scholar
  26. 26.
    Nolden, C., Schönfelder, M., Eßer-Frey, A., Bertsch, V., Fichtner, W.: Network constraints in techno-economic energy system models: towards more accurate modeling of power flows in long-term energy system models. Energy Syst. 4(3), 267–287 (2013)CrossRefGoogle Scholar
  27. 27.
    Kargarian, A., Mohammadi, J., Guo, J., Chakrabarti, S., Barati, M., Hug, G., Kar, S., Baldick, R.: Toward distributed/decentralized DC optimal power flow implementation in future electric power systems. IEEE Trans Smart Grid (2016, to appear)Google Scholar
  28. 28.
    Rebennack, S., Flach, B., Pereira, M.V.F., Pardalos, P.M.: Stochastic hydro-thermal scheduling under CO2 emissions constraints. IEEE Trans. Power Syst. 27(1), 58–68 (2012)CrossRefGoogle Scholar
  29. 29.
    Shortle, J., et al.: Transmission-capacity expansion for minimizing blackout probabilities. IEEE Trans. Power Syst. 29(1), 43–52 (2014)CrossRefGoogle Scholar
  30. 30.
    Tucker, A.: A note on convergence of the Ford–Fulkerson flow algorithm. Math. Oper. Res. 2(2), 143–144 (1977)MathSciNetCrossRefMATHGoogle Scholar
  31. 31.
    Kumar, A., Gupta, A., Roughgarden, T.: Simpler and better approximation algorithms for network design. In: Proceedings of the 35th STOC, pp. 365–372 (2003)Google Scholar
  32. 32.
    Bartal, Y.: On approximating arbitrary metrics by tree metrics. In: Proceedings of the 30th STOC, pp. 161–168 (1998)Google Scholar
  33. 33.
    Alman, F.S.S., Heriyan, J.C., Avi, R.R., Ubramanian, S.S.: Approximating the single-sink link-installation problem in network design. SIAM J. Optim. 11, 595–610 (2000)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Arger, D.R.K., Inkoff, M.M.: Building Steiner trees with incomplete global knowledge. In: Proceedings of the 41st FOCS, pp. 613–623 (2000)Google Scholar
  35. 35.
    Uha, S.G., Eyerson, A.M., Ungala, K.M.: Hierarchical placement and network design problems. In: Proceeding of the 41st FOCS, pp. 603–612 (2000)Google Scholar
  36. 36.
    Uha, S.G., Eyerson, A.M., Ungala, K.M.: A constant factor approximation for the single sink edge installation problem. In: Proceedings of the 33rd STOC, pp. 383–388 (2001)Google Scholar
  37. 37.
    Eyerson, A.M., Ungala, K.M., Lotkin, S.A.P.: Cost-distance: two metric network design. In: Proceedings of the 41st FOCS, pp. 624–630 (2000)Google Scholar
  38. 38.
    Rebennack, S., Nahapetyan, A., Pardalos, P.M.: Bilinear modeling solution approach for fixed charge network flow problems. Optim. Lett. 3(3), 347–355 (2009)MathSciNetCrossRefMATHGoogle Scholar
  39. 39.
    Srinivasagopalan, S., Busch, C., Iyengar, S.S.: An oblivious spanning tree for single-sink buy-at-bulk in low doubling-dimension graphs. IEEE Trans. Comput. 61(5), 700–712 (2012)MathSciNetCrossRefGoogle Scholar
  40. 40.
    Kumar, A., Gupta, A., Roughgarden, T.: Approximations via cost-sharing: a simple approximation algorithm for the multicommodity rent-or-buy problem. In: Proceedings of the 44th FOCS, pp. 606–615 (2003)Google Scholar
  41. 41.
    Garg, N., Khandekar, R., Konjevod, G., Ravi, R., Salman, F.S., Sinha, A.: On the integrality gap of a natural formulation of the single-sink buy-at-bulk network design formulation. In: Proceedings of the 8th IPCO, pp. 170–184 (2001)Google Scholar
  42. 42.
    Charikar, M., Karagiozova, A.: On non-uniform multicommodity buy-at-bulk network design. In: Proceedings of the 37th STOC, pp. 176–182 (2005)Google Scholar
  43. 43.
    Alwar, K.T.: Single-sink buy-at-bulk LP has constant integrality gap. In: Proceedings of the 9th IPCO, pp. 475–486 (2002)Google Scholar
  44. 44.
    Kumar, A., Gupta, A., Roughgarden, T.: A constant-factor approximation algorithm for the multicommodity rent-or-buy problem. In: Proceedings of the 43rd FOCS, pp. 333–342 (2002)Google Scholar
  45. 45.
    Werbuch, B.A., Zar, Y.A.: Buy-at-bulk network design. In: Proceedings of the 38th FOCS, pp. 542–547 (1997)Google Scholar
  46. 46.
    Frank, S., Steponavice, I., Rebennack, S.: Optimal power flow: a bibliographic survey I: formulations and deterministic methods. Energy Syst. 3(3), 221–258 (2012)CrossRefGoogle Scholar
  47. 47.
    Frank, S., Steponavice, I., Rebennack, S.: Optimal power flow: a bibliographic survey II: non-deterministic and hybrid methods. Energy Syst. 3(3), 259–289 (2012)CrossRefGoogle Scholar
  48. 48.
    Amini, M.H., et al.: Distributed security constrained economic dispatch. IEEE Innovative Smart Grid Technologies-Asia (ISGT ASIA) (2015)Google Scholar
  49. 49.
    Wood, A.J., Wollenberg, B.F.: Power Generation, Operation, and Control. Wiley, Amsterdam (2012)Google Scholar
  50. 50.
    Fakcharoenphol, J., Rao, S.B., Talwar, K.: A tight bound on approximating arbitrary metrics by tree metrics. In: Proceedings of the 35th STOC, pp. 448–455 (2003)Google Scholar
  51. 51.
    MATLAB version 8.5. Miami, Florida: The MathWorks Inc. (2015)Google Scholar
  52. 52.
    Abdollahi, A., Moghaddam, M.P., Rashidinejad, M., Sheikh-El-Eslami, M.K.: Investigation of economic and environmental-driven demand response measures incorporating UC. IEEE Trans. Smart Grids 3(1), 12–25 (2012)CrossRefGoogle Scholar
  53. 53.
    Power systems test case archive, 57 Bus Power Flow Test Case. [Online]. Available: (1993). Accessed 10 May 2016
  54. 54.
    Zimmerman, R.D., Murillo-Sanchez, C.E., Thomas, R.J.: MATPOWER: Steady-state operations, planning and analysis tools for power systems research and education. IEEE Trans. Power Syst. 26(1), 12–19 (2011)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA
  2. 2.Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA
  3. 3.SYSU-CMU Joint Institute of EngineeringGuangzhouChina
  4. 4.Departments of Electrical and Computer Engineering, Engineering and Public PolicyCarnegie Mellon UniversityPittsburghUSA
  5. 5.Department of Industrial and Systems Engineering at the University of FloridaGainesvilleUSA

Personalised recommendations