Open AccessTwo stage unit commitment considering multiple correlations of wind power forecast errors
Author(s) -
Wang Chengfu,
Li Xijuan,
Zhang Yumin,
Dong Yunhui,
Dong Xiaoming,
Wang Mingqiang
Publication year - 2021
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12037
Subject(s) - power system simulation , interval (graph theory) , schedule , wind power , constraint (computer aided design) , computer science , mathematical optimization , stage (stratigraphy) , power (physics) , feature (linguistics) , electric power system , control theory (sociology) , mathematics , engineering , artificial intelligence , paleontology , linguistics , physics , geometry , philosophy , control (management) , combinatorics , quantum mechanics , electrical engineering , biology , operating system
When the correlation of wind power output among wind farms is not considered, the integrated stochastic characteristics of wind power will not be captured accurately. Using this inaccurate feature may lead to an impractical even a failing result of unit commitment (UC). Therefore, this paper proposes a multiple correlations model for wind power forecast errors (WPFEs), and to capture this multiple correlation feature in UC problem, a two‐stage chance‐constrained interval UC (CIUC) model is proposed. First, an analytical expression of multiple correlations, including spatial, temporal and conditional correlations, is presented to improve the description accuracy of stochastic WPFEs. To strike a balance between risk and operational cost, a chance‐constrained decision method is developed to optimize the time‐varying interval of wind power output in the first stage. Subsequently, an interval UC model is established to determine the optimal operational schedule in the second stage. Finally, the proposed CIUC model is solved using a solution strategy that combines column‐and‐constraint generation and sample average approximation. The effectiveness and practicality of the proposed method are verified via the numerical results for IEEE 39‐bus and 118‐bus systems.