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Optimal reserve management for restructured power generating systems

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  • Ding, Yi
  • Wang, Peng
  • Lisnianski, Anatoly

Abstract

This paper presents a technique to determine the optimal reserve structure (reserve providers and the corresponding reserve capacity) for a restructured power generating system (GS). The reserve of a GS can be provided by its own generating units and can also be purchased from other GSs through the reserve agreements. The objective of reserve management for a GS is to minimize its total reserve cost while satisfying the reliability requirement. The reserve management is a complex optimization problem, which requires a large amount of calculations. In order to simplify the evaluation, a complex generating system (CGS) consisting of different GSs and the corresponding transmitting network is represented by its multi-state reliability equivalents. The universal generating functions (UGFs) of these equivalents are developed and the special operators for these UGFs are defined to evaluate the reliability of a particular GS, which has reserve agreements with other GSs in the CGS. The genetic algorithm (GA) has been used to solve the optimization problem. An improved power system-IEEE reliability test system is used to illustrate the technique.

Suggested Citation

  • Ding, Yi & Wang, Peng & Lisnianski, Anatoly, 2006. "Optimal reserve management for restructured power generating systems," Reliability Engineering and System Safety, Elsevier, vol. 91(7), pages 792-799.
    • Handle: RePEc:eee:reensy:v:91:y:2006:i:7:p:792-799
    DOI: 10.1016/j.ress.2005.08.001
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    Cited by:

    1. Li, Yan-Fu & Zio, Enrico, 2012. "A multi-state model for the reliability assessment of a distributed generation system via universal generating function," Reliability Engineering and System Safety, Elsevier, vol. 106(C), pages 28-36.
    2. Li, Y.F. & Peng, R., 2014. "Availability modeling and optimization of dynamic multi-state series–parallel systems with random reconfiguration," Reliability Engineering and System Safety, Elsevier, vol. 127(C), pages 47-57.
    3. Xiao, Hui & Shi, Daimin & Ding, Yi & Peng, Rui, 2016. "Optimal loading and protection of multi-state systems considering performance sharing mechanism," Reliability Engineering and System Safety, Elsevier, vol. 149(C), pages 88-95.
    4. Lisnianski, Anatoly & Ding, Yi, 2016. "Using inverse Lz-transform for obtaining compact stochastic model of complex power station for short-term risk evaluation," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 19-27.

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