Data-driven Surrogate-assisted Method for High-dimensional Multi-area Combined Economic/Emission Dispatch

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外文摘要：Multi-area combined economic/emission dispatch(MACEED)problems are generally studied using analytical functions.However,as the scale of power systems increases,ex-isting solutions become time-consuming and may not meet oper-ational constraints.To overcome excessive computational ex-pense in high-dimensional MACEED problems,a novel data-driven surrogate-assisted method is proposed.First,a cosine-similarity-based deep belief network combined with a back-propagation(DBN+BP)neural network is utilized to replace cost and emission functions.Second,transfer learning is applied with a pretraining and fine-tuning method to improve DBN+BP regression surrogate models,thus realizing fast con-struction of surrogate models between different regional power systems.Third,a multi-objective antlion optimizer with a novel general single-dimension retention bi-objective optimization poli-cy is proposed to execute MACEED optimization to obtain scheduling decisions.The proposed method not only ensures the convergence,uniformity,and extensibility of the Pareto front,but also greatly reduces the computational time.Finally,a 4-ar-ea 40-unit test system with different constraints is employed to demonstrate the effectiveness of the proposed method.

外文关键词：

Multi-area combined economic/emission dis-patchhigh-dimensional power systemdeep belief networkda-ta driventransfer learning

作者：

Chenhao Lin、Huijun Liang、Aokang Pang、Jianwei Zhong、Yongchao Yang

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作者单位：

College of Intelligent Systems Science and Engineering,Hubei Minzu University,Enshi,China

基金：

National Natural Science Foundation of ChinaNational Natural Science Foundation of Hubei Province

项目编号：

621630132021CFB542

出版年：

2024

DOI：

10.35833/MPCE.2023.000128

现代电力系统与清洁能源学报(英文版)

ISSN：

年,卷(期)：2024.12(1)

参考文献量45