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Optimal day‐ahead coordination on wind‐pumped‐hydro system by using multiobjective multistage model
Author(s) -
Yao Liming,
He Linhuan,
Chen Xudong,
Xiao Anran,
Wang Hongle
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5432
Subject(s) - wind power , scheduling (production processes) , electricity , mathematical optimization , operator (biology) , structural basin , operations research , computer science , environmental science , engineering , mathematics , geology , electrical engineering , paleontology , biochemistry , chemistry , repressor , transcription factor , gene
Summary This research is motivated by the practice of a well‐known basin—the Yalong River Basin located in the eastern part of the Qinghai‐Tibet Plateau in China, which requires a more reasonable day‐ahead scheduling and operation of a wind‐pumped‐hydro system. Optimal electricity flow remains a widely‐cultivated topic within power system research community since its inception about half‐a‐century ago. In contrast to previous studies, this article proposes a multiobjective multistage nonlinear model to not only for the technical pump‐operating constraints of the coordinated system, but also for lessening the variations on the active power output coming from the intermittence variability of the wind energy resource. Specifically, minimizing the fluctuations of the power output and minimizing the “abandoned rate” of the wind and hydro power are considered two objectives. A state transition equation to compute the energy balance in the reservoir is introduced in the constraint for scheduling the pump‐hydro system. Moreover, the expected operator and chance constraint are used to deal with the stochastic wind energy output. Finally, the scheduling of a wind‐pumped‐hydro system of the Yalong River Basin is proposed as a practical example to illustrate the effectiveness, feasibility, and validity of the proposed model.