Analytical Reformulation for Stochastic Unit Commitment Considering Wind Power Uncertainty With Gaussian Mixture Model

作     者：Yang, Yue Wu, Wenchuan Wang, Bin Li, Mingjie 

作者机构：Tsinghua Univ Dept Elect Engn State Key Lab Power Syst Beijing 100084 Peoples R China State Grid Corp China Natl Elect Power Control Ctr Beijing 100031 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON POWER SYSTEMS》 (IEEE动力系统汇刊)

年 卷 期：2020年第35卷第4期

页      面：2769-2782页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

基　　金：National Key R&D Program of China [2018YFB0904200] State Grid Corporation of China [SGLND-KOOKJJS1800266] 

主　　题：Wind power generation Generators Wind farms Stochastic processes Optimization Mathematical model Wind forecasting Chance-constrained programming unit commit-ment stochastic optimization 

摘      要：To capture the stochastic characteristics of renewable energy generation output, chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical reformulation for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenario-based methods are used to approximate probability with heavy computational burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including reserve sufficiency and branch power flow bounds are ensured to be satisfied with predetermined probability. To solve this CCUC problem, we propose a Newton method based procedure to acquire the quantiles and transform chance constraints into deterministic constraints. Therefore, the CCUC model is efficiently solved as a mixed-integer quadratic programming problem. Numerical tests are performed on several systems to illustrate efficiency and scalability of the proposed method.