Advertisement

Research on the Modern Power Grid Planning Method Based on the Nature and Characteristic of Power Network Planning

  • Ping ZhangEmail author
  • Jingbo Liu
  • Zhijun Wang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1074)

Abstract

City is the main power system load center, the city depends on the power grid is good or not urban power grid planning and construction is scientific, economic and reasonable, whether for fixed assets huge power supply enterprise, the city network planning work in the power supply enterprise’s survival and development is always plays a decisive role. In this paper, on the basis of analyzing the essence and characteristics of power grid planning, focus on simulated evolution, swarm intelligence, artificial intelligence, uncertain systems and other modern power grid planning method, various methods are discussed in detail the basic principle, characteristics and the comprehensive evaluation. Looking forward to the new economic and technological environment on the influence of the various methods and the development trend of power grid planning method.

Keywords

City power grid Planning method Intelligence 

References

  1. 1.
    Cui, J., Yoon, H.: An omnidirectional biomechanical energy harvesting (OBEH) sidewalk block for a self-generative power grid in a smart city. Int. J. Precis. Eng. Manuf.-Green Technol. 5(4), 507–517 (2018)CrossRefGoogle Scholar
  2. 2.
    Volkov, A., Korovkin, N., Sokolova, O., Sorokin, E., Frolov, O.: Method for optimizing control actions following emergencies in large-city electric power systems. Power Technol. Eng. 45(1), 50–52 (2011)Google Scholar
  3. 3.
    Odior, A., Omadudu, C.: Some factors responsible for erratic power supply in Benin City area of Edo state. Int. J. Syst. Manag. 4(1), 48–56 (2013)Google Scholar
  4. 4.
    Zmieva, A.: Modeling of an industrial enterprise power supply system using direct current. Russ. Electr. Eng. 86(5), 239–245 (2015)CrossRefGoogle Scholar
  5. 5.
    Kuznetsov, E., Trokin, A.: Solution extensions: optimal parameter method in optimal control problems. J. Math. Sci. 199(5), 564–570 (2014)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Benson, H.: On the optimal value function for certain linear programs with unbounded optimal solution sets. J. Optim. Theory Appl. 46(1), 55–66 (1985)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Hayalioglu, M.: Optimum load and resistance factor design of steel space frames using genetic algorithm. Struct. Multidiscip. Optim. 23(5), 404 (2002)CrossRefGoogle Scholar
  8. 8.
    Lin, F., Yang, Q.: Improved genetic operator for genetic algorithm. J. Zhejiang Univ.-SCIENCE A 3(4), 431–434 (2002)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Economic Technology Research Institute, State Grid Liaoning Electric Power Supply Co. Ltd.ShenyangChina
  2. 2.Jinzhou Power Supply Branch, State Grid Liaoning Electric Power Supply Co. Ltd.JinzhouChina

Personalised recommendations