Advertisement

Economic Dispatch of Wind Farm Cluster Integrated Power System Considering High Energy Load

  • Xiaoying ZhangEmail author
  • Shun Liao
  • Kun Wang
  • Xiaolan Wang
  • Wei Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 582)

Abstract

It is difficult to realize the economic dispatch for the large-scale wind power integration system. This paper thus presents a more comprehensive economic dispatch scheme, which considers the high energy load as a schedulable resource. The coordinated dispatch is achieved with the high energy load and wind farm cluster through the coordination layer. The multiple target mathematical models of the economic dispatch are established for the wind integrated power system. In this method, the income of the colony coordination layer maximization and the cost of control center minimization are assumed as the objective functions on the condition of output restriction, with power system security and output power of wind farm. The multi-objective cuckoo algorithm is used to perform the optimal solution. Finally, the mathematical models and the optimization algorithm are simulated respectively. The results verify the correctness of the proposed dispatch scheme and the effectiveness of the algorithm.

Keywords

High energy load Wind power integration Economic dispatch scheme Multi-objective cuckoo algorithm 

Notes

Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 51867015 and 51767017). Innovation group project of basic research of Gansu province (No. 18JR3RA133) and Synergistic innovation team project of Gansu province university.

References

  1. 1.
    Yuan, Y., Zhang, X., Ju, P., et al.: Determination of economic dispatch of wind farm-battery energy storage system using Genetic Algorithm. Int. Trans. Electr. Energy Syst. 24, 264–280 (2014)CrossRefGoogle Scholar
  2. 2.
    Mohy-Ud-Din, G.: Hybrid dynamic economic emission dispatch of thermal, wind, and photovoltaic power using the hybrid backtracking search algorithm with sequential quadratic programming. J. Renew. Sustain. Energy 9(1), 015502 (2017)CrossRefGoogle Scholar
  3. 3.
    Nazari, M.E., Ardehali, M.M.: Optimal coordination of renewable wind and pumped storage with thermal power generation for maximizing economic profit with considerations for environmental emission based on newly developed heuristic optimization algorithm. J. Renew. Sustain. Energy 8(6), 065905 (2016)CrossRefGoogle Scholar
  4. 4.
    Jiang, Y., Xu, J., Sun, Y., et al.: Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system. Appl. Energy 190, 1126–1137 (2017)CrossRefGoogle Scholar
  5. 5.
    Li, G., Zhang, R., Jiang, T., et al.: Security-constrained bi-level economic dispatch model for integrated natural gas and electricity systems considering wind power and power-to-gas process. Appl. Energy (2016)Google Scholar
  6. 6.
    Li, Y., Min Yang, Q.: Optimal storage sizing of energy storage for peak shaving in presence of uncertainties in distributed energy management systems. Int. J. Modell. Ident. Control. 31, 72–80 (2019)CrossRefGoogle Scholar
  7. 7.
    He, G., Cao, N., Jiang, L.I., et al.: Research on peak-load regulating with participation of high-use industrial consumers in wind power rich area. Renew. Energy Resour. 33(4), 491–496 (2015)Google Scholar
  8. 8.
    Marichelvam, M.K., Tosun, Ö.: Using Cuckoo search algorithm for hybrid flow shop scheduling problems under makespan criterion. Int. J. Modell. Ident. Control 40, 819–827 (2018)Google Scholar
  9. 9.
    Liu, J., Zeng, M., Ge, Y., Wu, C., Wang, X.: Improved Cuckoo search algorithm for numerical function optimization. Int. J. Comput. Appl. Technol. 142, 34–39 (2018)zbMATHGoogle Scholar
  10. 10.
    Khalil, M., Wibowo, R.S., Penangsang, O.: Combined economic emission dispatch with cubic criterion function using Cuckoo search algorithm. In: 2018 International Conference on Information and Communications Technology, 36–40 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xiaoying Zhang
    • 1
    Email author
  • Shun Liao
    • 2
  • Kun Wang
    • 3
  • Xiaolan Wang
    • 1
  • Wei Chen
    • 1
  1. 1.College of Electrical and Information EngineeringLanzhou University of TechnologyLanzhouChina
  2. 2.Liangshan Power Supply Corporation, State Grid Sichuan Electric Power CompanyChengduChina
  3. 3.Electric Science Institute, State Grid Gansu Provincial Power CompanyLanzhouChina

Personalised recommendations