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A data-driven approach towards fast economic dispatch in electricity–gas coupled systems based on artificial neural network

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  • Liu, Haizhou
  • Shen, Xinwei
  • Guo, Qinglai
  • Sun, Hongbin

Abstract

The optimization of electricity–gas coupled systems is typically complicated by the nonconvex relationships such as gas flow equations. Piecewise linearization, despite being one of the few solutions that guarantee exactness and optimality, is often discarded for an exceptionally long computational time. In this paper, we present a novel data-driven approach based on artificial neural networks, to enable fast economic dispatch in electricity–gas coupled systems, by utilizing simulation data from the piecewise-linearization-based model-driven method. Load profiles at each electric bus and gas node are fed into the artificial neural network as input neurons; optimal economic dispatch results are set as output neurons, where the dispatch results can be either continuous (e.g. power and gas output) or binary (e.g. scenario feasibility). In generating power and gas outputs, the slack generator method is proposed to further eliminate load mismatch. Case studies on an integrated Belgium 20-node gas/IEEE 24-bus power system show that, after the artificial neural network is properly trained, the data-driven economic dispatch method is 104∼105 times faster than model-driven piecewise linearization. It even outperforms second-order cone programming, a well-known convex relaxation technique to model natural gas systems, in terms of both the coupled system’s state recovery accuracy and computational efficiency. Furthermore, the data-driven method is applied to a multi-period dispatch problem to demonstrate its scalability.

Suggested Citation

  • Liu, Haizhou & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin, 2021. "A data-driven approach towards fast economic dispatch in electricity–gas coupled systems based on artificial neural network," Applied Energy, Elsevier, vol. 286(C).
    • Handle: RePEc:eee:appene:v:286:y:2021:i:c:s030626192100043x
    DOI: 10.1016/j.apenergy.2021.116480
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    References listed on IDEAS

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    1. Kim, Min Jae & Kim, Tong Seop & Flores, Robert J. & Brouwer, Jack, 2020. "Neural-network-based optimization for economic dispatch of combined heat and power systems," Applied Energy, Elsevier, vol. 265(C).
    2. Huang, Tian-en & Guo, Qinglai & Sun, Hongbin & Tan, Chin-Woo & Hu, Tianyu, 2019. "A deep spatial-temporal data-driven approach considering microclimates for power system security assessment," Applied Energy, Elsevier, vol. 237(C), pages 36-48.
    3. Bao, Shiyuan & Yang, Zhifang & Guo, Lin & Yu, Juan & Dai, Wei, 2020. "One-segment linearization modeling of electricity-gas system optimization," Energy, Elsevier, vol. 197(C).
    4. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
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    Citations

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    Cited by:

    1. Jiao, P.H. & Chen, J.J. & Cai, X. & Zhao, Y.L., 2024. "Fuzzy semi-entropy based downside risk to low-carbon oriented multi-energy dispatch considering multiple dependent uncertainties," Energy, Elsevier, vol. 287(C).
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    3. Tang, Xiongmin & Li, Zhengshuo & Xu, Xuancong & Zeng, Zhijun & Jiang, Tianhong & Fang, Wenrui & Meng, Anbo, 2022. "Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm," Energy, Elsevier, vol. 244(PA).
    4. Zhang, Bin & Hu, Weihao & Cao, Di & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Novel Data-Driven decentralized coordination model for electric vehicle aggregator and energy hub entities in multi-energy system using an improved multi-agent DRL approach," Applied Energy, Elsevier, vol. 339(C).
    5. Fatemeh Marzbani & Akmal Abdelfatah, 2024. "Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review," Energies, MDPI, vol. 17(3), pages 1-31, January.
    6. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    7. Fei Chen & Zhiyang Wang & Yu He, 2023. "A Deep Neural Network-Based Optimal Scheduling Decision-Making Method for Microgrids," Energies, MDPI, vol. 16(22), pages 1-17, November.
    8. Zhang, Bin & Wu, Xuewei & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: An improved soft actor–critic approach," Energy, Elsevier, vol. 271(C).

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