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Chronological operation simulation framework for regional power system under high penetration of renewable energy using meteorological data

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  • Liao, Shiwu
  • Yao, Wei
  • Han, Xingning
  • Wen, Jinyu
  • Cheng, Shijie

Abstract

Chronological operation simulation (COS) is an essential tool for planning and analyzing power systems under high penetration of renewable energy. Conventional COS methods heavily depend on the availability of renewable power output data to obtain accurate results, and often require hours or even days of computational time while the sequential simulation could easily get infeasible for power systems with intensive flexibility. To cover the absence of output data for newly proposed wind and solar projects and accelerate the computation speed, this paper proposes a novel COS simulation framework for regional power systems with high penetration of renewable energies using meteorological data. The proposed simulation framework consists of the following three steps: data preparation, modeling and solving, and result output. In the data preparation step, wind, solar power output profiles and heat demands are converted from public accessible meteorological data. Then in the modeling and solving step, a unit commitment based COS model for simulating the hourly operation of power and heat sectors is proposed, and the proposed model is solved with a time domain partitioning (TDP) and a rollback mechanism to accelerate the computation speed as well as avoiding infeasible solutions. The accuracy of the wind and solar power output converted from meteorological data is verified through comparing with measured power output. Moreover, the feasibility and accuracy of utilizing the proposed COS framework to simulate the operation of a real regional power system is also verified through the 2015 annual operation statistics of the Northwest China Grid.

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  • Liao, Shiwu & Yao, Wei & Han, Xingning & Wen, Jinyu & Cheng, Shijie, 2017. "Chronological operation simulation framework for regional power system under high penetration of renewable energy using meteorological data," Applied Energy, Elsevier, vol. 203(C), pages 816-828.
  • Handle: RePEc:eee:appene:v:203:y:2017:i:c:p:816-828
    DOI: 10.1016/j.apenergy.2017.06.086
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