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

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

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number111059
Number of pages11
JournalKnowledge-Based Systems
Publication statusPublished - 3 Dec 2023


  • Bi-objective optimization
  • Data-driven optimization algorithm
  • Power system dispatch
  • Renewable energy
  • Uncertainty

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