Backgrounds and Literature Review

  • Zhengshuo LiEmail author
Part of the Springer Theses book series (Springer Theses)


It is well known that transmission and distribution sections of a real-world power system are physically coupled by distribution transformers. This implies that almost every large-scale power system is an integrated Transmission-Distribution (T-D) system that consists of a Transmission Power Subsystem (TPS) and multiple Distribution Power Subsystems (DPSs). Therefore, technically, only by considering the TPS and the DPS as a whole, can people accurately assess the state of the entire power system and optimally control the system.


Real-world Power System Transmission Control Center (TCCs) Distribution Control Center (DCCs) Boundary Buses ADMM Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Sun HB (1996) The studies on global reactive optimal control of power system. Ph.D. dissertation. Tsinghua University, Beijing, China (in Chinese)Google Scholar
  2. 2.
    Sun HB, Zhang BM, Xiang ND (1998) Global power flow calculation. Part 2: model and method. Power Syst Technol 22(12):41–44 (in Chinese)Google Scholar
  3. 3.
    D’Adamo C, Jupe S, Abbey C (2009) Global survey on planning and operation of active distribution networks—update of CIGRE C6.11 working group activities. 20th International Conference on Exhibition Electricity Distribution, Prague, Czech Republic, pp 1–4Google Scholar
  4. 4.
    FERC/NERC Staff Report on the September 8, 2011 Blackout[EB/OL] (2012).
  5. 5.
    Anderson RW, Gerber S, Reid E (2014) Distributed energy resources integration: summarizing the challenges and barriers. Olivine, Inc. San Ramon, CA[EB/OL].
  6. 6.
    Olson A, Mahone A, Hart E et al (2015) Halfway there can California achieve a 50% renewable grid? IEEE Power Energy Mag 13(4):41–52CrossRefGoogle Scholar
  7. 7.
    Zegers A, Brunner H (2014) TSO-DSO interaction: an overview of current interaction between transmission and distribution system operators and an assessment of their cooperation in smart grids[EB/OL].
  8. 8.
    ENTSO (European Network of Transmission System Operators) Report (2015) Towards smarter grids: developing TSO and DSO roles and interactions for the benefit of consumers[EB/OL].
  9. 9.
    ENTSO Report (2015) General guidelines for reinforcing the cooperation between TSOs and DSOs [EB/OL].
  10. 10.
    Sun HB, Zhang BM, Xiang ND (1999) Global state estimation for power system including transmission and distribution networks. J Tsinghua Univ (Sci & Tech) 39(7):20–24 (in Chinese)Google Scholar
  11. 11.
    Sun HB, Zhang BM, Xiang ND et al (1999) Global power flow calculation. Part 2: convergence, practical algorithm and numerical test. Power Syst Technol 23(1):50–53 (in Chinese)Google Scholar
  12. 12.
    Sun HB, Guo Y, Zhang BM (2008) Distributed global power flow calculation for whole transmission and looped distribution networks. Autom Electr Power Syst 32(13):11–15 (in Chinese)Google Scholar
  13. 13.
    Sun HB, Zhang BM (2008) Distributed power flow calculation for whole networks including transmission and distribution. IEEE PES Transmission and Distribution Conference and Exposition, Chicago, pp 1–6Google Scholar
  14. 14.
    Li ZS, Sun HB, Guo QL et al (2012) GPF-based method for evaluating EVs’ free charging impacts in distribution system. IEEE PES General Meeting, San Diego, pp 1–7Google Scholar
  15. 15.
    Sun HB, Guo QL, Zhang BM et al (2015) Master-slave-splitting based distributed global power flow method for integrated transmission and distribution analysis. IEEE Trans Smart Grid 6(3):1484–1492CrossRefGoogle Scholar
  16. 16.
    Kargarian A, Fu Y (2014) System of systems based security-constrained unit commitment incorporating active distribution grids. IEEE Trans Power Syst 29(5):2489–2498CrossRefGoogle Scholar
  17. 17.
    Tosserams S, Etman L, Papalambros PY et al (2006) An augmented Lagrangian relaxation for analytical target cascading using the alternating direction method of multipliers. Struct. Multidisc. Optim. 31(3):176–189MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Jia HJ, Qi WJ, Liu Z et al (2015) Hierarchical risk assessment of transmission system considering the influence of active distribution network. IEEE Trans Power Syst 30(2):1084–1093CrossRefGoogle Scholar
  19. 19.
    Kim BH, Baldick R (1997) Coarse-grained distributed optimal power flow. IEEE Trans Power Syst 12(2):932–939CrossRefGoogle Scholar
  20. 20.
    Wang X, Song YH (2001) Apply Lagrangian relaxation to multi-zone congestion management. IEEE PES Winter Meeting, Columbus, pp 399–404Google Scholar
  21. 21.
    Ruszczyński A (2006) Nonlinear optimization. Princeton University Press, New JerseyzbMATHGoogle Scholar
  22. 22.
    Aguado JA, Quintana VH (2001) Inter-utilities power-exchange coordination: a market-oriented approach. IEEE Trans Power Syst 16(3):513–519CrossRefGoogle Scholar
  23. 23.
    Conejo AJ, Aguado JA (1998) Multi-area coordinated decentralized DC optimal power flow. IEEE Trans Power Syst 13(4):1272–1278CrossRefGoogle Scholar
  24. 24.
    Biskas PN, Bakirtzis AG (2002) Decentralised congestion management of interconnected power systems. Proc Inst Elect Eng Gen Transm Distrib 149(4):432–438CrossRefGoogle Scholar
  25. 25.
    Kim BH, Baldick R (2000) A comparison of distributed optimal power flow algorithms. IEEE Trans Power Syst 15(2):599–604CrossRefGoogle Scholar
  26. 26.
    Li Z, Yang HG (2013) A full decomposition proximal center algorithm for decomposition and coordination of reactive power optimization. Proc CSEE 33(1):77–83 (in Chinese)Google Scholar
  27. 27.
    Batut J, Renaud A (1992) Daily generation scheduling optimization with transmission constraints: a new class of algorithms. IEEE Trans Power Syst 7(3):982–989CrossRefGoogle Scholar
  28. 28.
    Losi A, Russo M (2003) On the application of the auxiliary problem principle. J Opt Theory App 117(2):377–396MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Cheng XG, Li JW, Cao LX et al (2003) Distributed and parallel optimal power flow solution of electric power systems. Autom Electr Power Syst 27(24):23–27 (in Chinese)Google Scholar
  30. 30.
    Cheng XG, Li JW, Cao LX et al (2003) Multi-objective distributed parallel reactive power optimization based on subarea division of the power systems. Proc CSEE 23(10):109–113 (in Chinese)Google Scholar
  31. 31.
    Liu KY, Sheng WX, Li YH (2007) Multi-region transmission congestion management based on distributed optimal power flow algorithm. Proc CSEE 27(19):56–61 (in Chinese)Google Scholar
  32. 32.
    Liu BY, Yang RG (2009) Multi-subarea parallel reactive power optimization based on APP. Proc CSEE 279(7):47–51 (in Chinese)Google Scholar
  33. 33.
    Hur D, Park JK, Kim BH (2002) Evaluation of convergence rate in the auxiliary problem principle for distributed optimal power flow. Proc Inst Elect Eng Gen Transm Distrib 149(5):525–532CrossRefGoogle Scholar
  34. 34.
    Wang X, Song YH, Lu Q (2002) Lagrangian relaxation based multi-region transmission congestion management. Autom Electr Power Syst 26(13):8–13 (in Chinese)Google Scholar
  35. 35.
    Erseghe T (2014) Distributed optimal power flow using ADMM. IEEE Trans Power Syst 29(5):2370–2380CrossRefGoogle Scholar
  36. 36.
    Boyd S, Parikh N, Chu E et al (2010) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122CrossRefzbMATHGoogle Scholar
  37. 37.
    Conejo AJ, Nogales FJ, Prieto FJ (2002) A decomposition procedure based on approximate Newton directions. Math Program 93(3):495–515MathSciNetCrossRefzbMATHGoogle Scholar
  38. 38.
    Nogales FJ, Prieto FJ, Conejo AJ (2003) A decomposition methodology applied to the multi-area optimal power flow problem. Ann Oper Res 120(1–4):99–116MathSciNetCrossRefzbMATHGoogle Scholar
  39. 39.
    Hug-Glanzmann G, Andersson G (2009) Decentralized optimal power flow control for overlapping areas in power systems. IEEE Trans Power Syst 24(1):327–336CrossRefGoogle Scholar
  40. 40.
    Zhao WX, Liu MB (2007) A decomposition algorithm applied to multi-area reactive-power optimization based on approximate newton directions. Proc CSEE 27(25):18–24 (in Chinese)zbMATHGoogle Scholar
  41. 41.
    Cai DY, Bai FS (1996) Advanced numerical analysis. Tsinghua University Press, Beijing, China (in Chinese)Google Scholar
  42. 42.
    Yan W, Wen LL, Li W et al (2011) Decomposition-coordination interior point method and its application to multi-area optimal reactive power flow. Int J Elec Power 33(1):55–60MathSciNetCrossRefGoogle Scholar
  43. 43.
    Hu XQ, Yan W, Zhao L et al (2013) Model and algorithm of interconnection power flow for interconnected power grid. Autom Electr Power Syst 37(3):47–53 (in Chinese)Google Scholar
  44. 44.
    Zhao WX, Liu MB, Miu NL (2008) A decomposition algorithm for multi-area reactive-power optimization based on the block bordered diagonal model. Autom Electr Power Syst 32(4):25–29 (in Chinese)Google Scholar
  45. 45.
    Biskas PN, Bakirtzis AG (2006) Decentralised OPF of large multiarea power systems. Proc Inst Elect Eng Gen Transm Distrib 153(1):99–105CrossRefGoogle Scholar
  46. 46.
    Bakirtzis AG, Biskas PN (2003) A decentralized solution to the DC-OPF of interconnected power systems. IEEE Trans Power Syst 18(3):1007–1013CrossRefGoogle Scholar
  47. 47.
    Biskas PN, Bakirtzis AG (2004) Decentralised security constrained DC-OPF of interconnected power systems. Proc Inst Elect Eng Gen Transm Distrib 151(6):747–754CrossRefGoogle Scholar
  48. 48.
    Biskas PN, Bakirtzis AG, Macheras NI et al (2005) A decentralized implementation of DC optimal power flow on a network of computers. IEEE Trans Power Syst 20(1):25–33CrossRefGoogle Scholar
  49. 49.
    Liu KY, Sheng WX, Li YH (2006) Research on decomposition algorithm of dc optimal power flow in large scale interconnection power grids. Proc CSEE 26(12):21–25 (in Chinese)Google Scholar
  50. 50.
    Lai XW, Xie L, Xia Q et al (2015) Decentralized multi-area economic dispatch via dynamic multiplier-based lagrangian relaxation. IEEE Trans Power Syst 30(6):3225–3233CrossRefGoogle Scholar
  51. 51.
    Huang SW, Chen Y, Shen C (2008) Distributed OPF of large scale interaction power systems. New Delhi, International Conference Power System Technology and IEEE Power India Conference, pp 1–6Google Scholar
  52. 52.
    Huang SW (2011) Studies on unified modeling and simulation of power systems for smart grid: Ph.D. dissertation. Tsinghua University, Beijing (in Chinese)Google Scholar
  53. 53.
    Min L, Abur A (2006) A decomposition method for multi-area OPF problem. IEEE PES Power System Conference Exposition, Atlanta, pp 1689–1693Google Scholar
  54. 54.
    Lai XW, Zhong HW, Yang JF et al (2013) Decomposition optimization method over large-scale power system based on price response function. Autom Electr Power Syst 37(21):60–65 (in Chinese)Google Scholar
  55. 55.
    Li QF, Yang LQ, Lin SJ (2015) Coordination strategy for decentralized reactive power optimization based on a probing mechanism. IEEE Trans Power Syst 30(2):555–562CrossRefGoogle Scholar
  56. 56.
    Zhao WX, Liu MB, Sun B (2009) A Norton equivalence based decomposition and coordination algorithm of reactive power optimization for multi-regional power grid. Power Syst Technol 33(11):44–48 (in Chinese)Google Scholar
  57. 57.
    Liu ZW, Liu MB (2010) A decomposition and coordination algorithm for multi-area reactive power optimization based on Ward equivalent. Autom Electr Power Syst 34(14):63–69 (in Chinese)Google Scholar
  58. 58.
    Liu ZW, Liu MB, Lin SJ (2011) Research on application of REI equivalent technique into multi-area reactive power optimization computing. Trans China Electrotechnical Soc 26(11):191–200 (in Chinese)Google Scholar
  59. 59.
    Zhao F, Litvinov E, Zheng TX (2014) A marginal equivalent decomposition method and its application to multi-area optimal power flow problems. IEEE Trans Power Syst 29(1):53–61CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Tsinghua-Berkeley Shenzhen InstituteTsinghua UniversityShenzhenChina

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