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Author:

Wang, Chunxiao (Wang, Chunxiao.) | Bie, Zhaohong (Bie, Zhaohong.) (Scholars:别朝红) | Yan, Chao (Yan, Chao.) | Wang, Can (Wang, Can.) | Li, Gengfeng (Li, Gengfeng.)

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Abstract:

With higher penetration of renewable energy sources (RES), more combined-cycle gas turbines (CCGTs) will be deployed. These flexible gas-fired generators can provide efficient system reserve to deal with rapid power fluctuation of renewable energy. Therefore, an accurate reserve allocation (RA) on CCGTs is necessary to guarantee efficiency and safety of integrated electricity and natural gas system operation. Traditional RA model ignores the non-dispatchable start-up and shut-down processes of combustion turbines and steam turbines (i.e. transition trajectories) in CCGTs and is less considered about the effect from natural gas system. A novel RA model is proposed considering the effect of transition trajectories of CCGTs, gas-electricity coupling and unit commitment to meet the demand of load and RES forecast error and N-1 contingencies. The simulation results show that transition trajectories and gas load level greatly affect the reserve scheduling of CCGTs. © 2019 IEEE.

Keyword:

Diesel and gas turbine combined power plants Electric equipment protection Electric load forecasting Electric power system protection Gases Gas turbines Natural gas Proven reserves Renewable energy resources Trajectories

Author Community:

  • [ 1 ] [Wang, Chunxiao]Xi'an Jiaotong University, Electrical Department, Xi'an, China
  • [ 2 ] [Bie, Zhaohong]Xi'an Jiaotong University, Electrical Department, Xi'an, China
  • [ 3 ] [Yan, Chao]Xi'an Jiaotong University, Electrical Department, Xi'an, China
  • [ 4 ] [Wang, Can]Xi'an Jiaotong University, Electrical Department, Xi'an, China
  • [ 5 ] [Li, Gengfeng]Xi'an Jiaotong University, Electrical Department, Xi'an, China

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Year: 2019

Page: 898-902

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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