Dispatch of highly renewable energy power system considering its utilization via a data-driven Bayesian assisted optimization algorithm

作     者：Yu, Chaofan Li, Yuanzheng Liu, Yun Ge, Leijiao Wang, Hao Luo, Yunfeng Pan, Linqiang 

作者机构：Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Key Lab Image Informat Proc & Intelligent Control Educ Minist China Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China South China Univ Technol Sch Elect Power Engn Guangzhou 510641 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Monash Univ Dept Data Sci & Artificial Intelligence Melbourne Vic 3800 Australia 

出 版 物：《KNOWLEDGE-BASED SYSTEMS》 (知识库系统)

年 卷 期：2023年第281卷

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Science and Technology Project of State Grid Headquarters [1400-202099523A-0-0-00] 

主　　题：Renewable energy Data-driven optimization algorithm Power system dispatch Bi-objective optimization Uncertainty 

摘      要：In recent years, renewable energy (RE) has been widely deployed, and the power system with high penetration RE is gradually formed. However, the high proportion of RE may threaten transmission security of power systems, which in turn limits its utilization. What is more, the interaction between RE penetration and power system transmission security has not been comprehensively investigated so far. To this end, we develop a bi-objective stochastic dispatch model to investigate the relationship between RE utilization and transmission security. It aims to solve the optimal power system dispatch (OPSD) problem with high penetration RE, in which the RE curtailment and the capacity margin of transmission lines are considered as two objectives of the dispatch problem and formulated in the probabilistic forms. With this, the proposed model is a complicated bi-objective stochastic optimization problem, which is difficult to be solved for traditional optimization algorithms. Therefore, we propose a data-driven Bayesian assisted optimization (DBAO) algorithm, based on Bayesian evolutionary optimization and estimation of distribution algorithm to improve the searching efficiency for the proposed model. Case studies on a modified Midwestern US power system verify the effectiveness of our proposed dispatch model and the optimization algorithm of DBAO.