With the rise of a new round of energy revolution, the distribution network with distributed generation (DG) has become an important form of the future power grid. However, DG itself has the characteristics of randomness and intermittence, which brings impacts and challenges to the distribution network planning. Based on the uncertainty theory, the fuzzy simulation of DG and load are used to model the distribution network. The model takes the minimum annual investment cost and the minimum cost of network loss as the optimization target. Single parent genetic algorithm based on spanning tree is optimized and verified by 18 node system simulation.


Distribution generation Network planning Uncertainty theory Genetic algorithm 


  1. 1.
    Zhao MA, Ting AN, Yuwei S (2016) State of the art and development trends of power distribution technologies. Proc CSEE 36(6):1552–1567Google Scholar
  2. 2.
    Zhang LM, Tang W, Zhao Y et al (2010) The integrated evaluation of impact of distributed generation on distribution network. Power Syst Protect Control 38(21):132–135, 140Google Scholar
  3. 3.
    Fuchun H, Mingkai Z (1994) Research on urban power network planning. Autom Electr Power Syst 18(11):57–62Google Scholar
  4. 4.
    Weidong T (1993) New development of foreign power planning. Energy of China 1(6–9):25Google Scholar
  5. 5.
    Zhang W, Cheng H, Cheng Z (2008) Review of distribution network optimal planning. Autom Electr Power Syst 20(5):16–23 (in Chinese)Google Scholar
  6. 6.
    De Figueiredo LH, Stolfi J (2004) Affine arithmetic: concepts and applications. Numer Algorithms 37(1):147–158MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Tang N (2015) A study on the expansion planning of distribution systems considering distributed generations. Beijing Jiaotong UniversityGoogle Scholar
  8. 8.
    Zhang H (2015) Dynamic optimal dispatch of active distribution network with electric vehicle aggregators. School of Electrical and Electronic EngineeringGoogle Scholar
  9. 9.
    Liu BD, Zhao RQ, Wang G (2003) Uncertain programming with applications. Tsinghua University Press, BeijingGoogle Scholar
  10. 10.
    Karaki SH, Chedid RB, Ramadan R (1999) Probabilistic performance assessment of autonomous solar-wind energy conversion systems. IEEE Trans Energy Convers 14(3):766–772CrossRefGoogle Scholar
  11. 11.
    Abouzahr I, Ramakumar R (1991) Loss of power supply probability of stand-alone photovoltaic systems: a closed form solution approach. IEEE Trans Energy Convers 6(1):1–11CrossRefGoogle Scholar
  12. 12.
    Maojun L (2002) Theory and application of partheno genetic algorithm. Hunan UniversityGoogle Scholar
  13. 13.
    Wang X (1990) Optimal planning of power system. China Water Power Press, BeijingGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electrical EngineeringBeijing Jiaotong UniversityHaidian District, BeijingChina

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