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

Abstract

Multi-area combined economic/emission dispatch (MACEED) problems are generally studied using analytical functions. However, as the scale of power systems increases, existing solutions become time-consuming and may not meet operational constraints. To overcome excessive computational expense in high-dimensional MACEED problems, a novel datadriven surrogate-assisted method is proposed. First, a cosinesimilarity-based deep belief network combined with a backpropagation (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 construction of surrogate models between different regional power systems. Third, a multi-objective antlion optimizer with a novel general single-dimension retention bi-objective optimization policy 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-area 40-unit test system with different constraints is employed to demonstrate the effectiveness of the proposed method.Index Terms--Multi-area combined economic/emission dispatch, high-dimensional power system, deep belief network, data driven, transfer learning.Huijun Liang received the B.E. degree in industrial automation from Wuhan University of Hydraulic and Electric Engineering, Wuhan, China, in 2000, the M.E. degree in control theory and control engineering from Wuhan University, Wuhan, China, in 2004, the Ph.D. degree in control theory and control engineering from Shandong University, Jinan, China, in 2020. He is currently working as a Lecturer in Hubei Minzu University, Enshi, China. His research interests include computational intelligence and optimization in power systems.Aokang Pang received the B.E. degree in electrical engineering and its automation from Xi'an Kedagaoxin University, Xi'an, China, in 2021. is currently pursuing the M.E. degree in electrical engineering in Hubei Minzu University, Enshi, China. His current research interests include computational intelligence and its application to economic dispatch of power systems.