Two stage unit commitment considering multiple correlations of wind power forecast errors

Abstract

Abstract 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.

Keywords

• Other topics in statistics
• Optimisation techniques
• Power system management, operation and economics
• Power system planning and layout
• Wind power plants
• Other topics in statistics