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

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  • Lin, Chenhao
  • Liang, Huijun
  • Pang, Aokang

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.

Suggested Citation

  • Lin, Chenhao & Liang, Huijun & Pang, Aokang, 2023. "A fast data-driven optimization method of multi-area combined economic emission dispatch," Applied Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:appene:v:337:y:2023:i:c:s0306261923002489
    DOI: 10.1016/j.apenergy.2023.120884
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    References listed on IDEAS

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    1. Yin, Linfei & Sun, Zhixiang, 2021. "Multi-layer distributed multi-objective consensus algorithm for multi-objective economic dispatch of large-scale multi-area interconnected power systems," Applied Energy, Elsevier, vol. 300(C).
    2. Secui, Dinu Calin, 2015. "The chaotic global best artificial bee colony algorithm for the multi-area economic/emission dispatch," Energy, Elsevier, vol. 93(P2), pages 2518-2545.
    3. Lin, Jian & Wang, Zhou-Jing, 2019. "Multi-area economic dispatch using an improved stochastic fractal search algorithm," Energy, Elsevier, vol. 166(C), pages 47-58.
    4. Meng, Anbo & Xu, Xuancong & Zhang, Zhan & Zeng, Cong & Liang, Ruduo & Zhang, Zheng & Wang, Xiaolin & Yan, Baiping & Yin, Hao & Luo, Jianqiang, 2022. "Solving high-dimensional multi-area economic dispatch problem by decoupled distributed crisscross optimization algorithm with population cross generation strategy," Energy, Elsevier, vol. 258(C).
    5. Ghasemi, Mojtaba & Aghaei, Jamshid & Akbari, Ebrahim & Ghavidel, Sahand & Li, Li, 2016. "A differential evolution particle swarm optimizer for various types of multi-area economic dispatch problems," Energy, Elsevier, vol. 107(C), pages 182-195.
    6. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    7. Zhou, Kaile & Yang, Shanlin & Shao, Zhen, 2016. "Energy Internet: The business perspective," Applied Energy, Elsevier, vol. 178(C), pages 212-222.
    8. Gan, Wei & Yan, Mingyu & Yao, Wei & Guo, Jianbo & Ai, Xiaomeng & Fang, Jiakun & Wen, Jinyu, 2021. "Decentralized computation method for robust operation of multi-area joint regional-district integrated energy systems with uncertain wind power," Applied Energy, Elsevier, vol. 298(C).
    Full references (including those not matched with items on IDEAS)

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