A fast data-driven optimization method of multi-area combined economic emission dispatch

Chenhao Lin, Huijun Liang and Aokang Pang

Applied Energy, 2023, vol. 337, issue C, No S0306261923002489

Abstract: To provide more feasible schemes for power system optimization management and operation, the interconnection of power grids in different areas is inevitable. Naturally, the multi-area combined economic/emission dispatch (MACEED) problem becomes a more important decision problem. However, as the dimensions of MACEED problems increase, existing studies may not obtain feasible scheduling decisions in a suitable time. On this background, MACEED problems are transferred into computational expensive problems. In order to solve high-dimensional, large-scale MACEED problems, a data-driven surrogate-assisted approach is proposed. First, a feature engineering-based support vector regression surrogate model is proposed to replace the traditional objective functions in high-dimensional MACEED problems. Second, knowledge distillation is applied as a freezing and fine-tuning mechanism for the improved support vector regression surrogate models, which significantly reduces the time required to build surrogate models. Third, an improved non-dominated sorting genetic algorithm-III is proposed to obtain feasible solutions to high-dimensional MACEED problem. The proposed algorithm enhances the convergence and diversity of optimal solutions. The effectiveness of the proposed data-driven approach is demonstrated through simulations of a four-area 40-unit test system under different constraints.

Keywords: Multi-area combined economic emission dispatch; Large-scale power system; Data driven; Surrogate model; Knowledge distillation (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.apenergy.2023.120884

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