Unit Commitment with Joint Chance Constraints in Multi-area Power Systems with Wind Power Based on Partial Sample Average Approximation  

作　　者：Jinghua Li Hongyu Zeng Yutian Xie 

机构地区：[1]School of Electrical Engineering,Guangxi University,Nanning 530004,China

出　　处：《Journal of Modern Power Systems and Clean Energy》2025年第1期241-252,共12页现代电力系统与清洁能源学报(英文)

基　　金：supported by the National Natural Science Foundation of China(No.51977042)。

摘　　要：Joint chance constraints(JCCs)can ensure the consistency and correlation of stochastic variables when participating in decision-making.Sample average approximation(SAA)is the most popular method for solving JCCs in unit commitment(UC)problems.However,the typical SAA requires large Monte Carlo(MC)samples to ensure the solution accuracy,which results in large-scale mixed-integer programming(MIP)problems.To address this problem,this paper presents the partial sample average approximation(PSAA)to deal with JCCs in UC problems in multi-area power systems with wind power.PSAA partitions the stochastic variables and historical dataset,and the historical dataset is then partitioned into non-sampled and sampled sets.When approximating the expectation of stochastic variables,PSAA replaces the big-M formulation with the cumulative distribution function of the non-sampled set,thus preventing binary variables from being introduced.Finally,PSAA can transform the chance constraints to deterministic constraints with only continuous variables,avoiding the large-scale MIP problem caused by SAA.Simulation results demonstrate that PSAA has significant advantages in solution accuracy and efficiency compared with other existing methods including traditional SAA,SAA with improved big-M,SAA with Latin hypercube sampling(LHS),and the multi-stage robust optimization methods.

关 键 词：Unit commitment joint chance constraint renewable energy multi-area power system wind power sample average approximation partial sample average approximation 

分 类 号：TM73[电气工程—电力系统及自动化]