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Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems

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  • Yin, Linfei
  • Zhang, Bin

Abstract

The randomness of renewable energy connected to power systems cannot satisfy the frequency stability and economic dispatch of integrated energy systems. The conventional combined dispatch and control framework can benefit the interests of only a part of energy systems, such as gas/heat/cold systems. Real-time economic smart generation dispatch and control are applied to solve these two problems of integrated energy systems. This paper proposes a relaxed deep generative adversarial network method for the real-time control framework to replace conventional combined methods with multiple time-scale combined frameworks. The proposed approach combines deep learning and relaxed operation with generative adversarial networks as the proposed method. Because of the powerful representation capability of deep learning and the advantages of amplitude constraints of the relaxation operation, the proposed method can simultaneously generate multiple convergent and high-performance generation commands. The proposed method is simulated for simple and large-scale complex integrated energy systems based on a 14-bus with ten natural-gas systems. Furthermore, the generation controller based on the proposed method can economically satisfy the frequency stability for integrated energy systems, mitigate uneven energy distribution and excess energy, improve energy efficiency, and achieve multi-energy complementarity.

Suggested Citation

  • Yin, Linfei & Zhang, Bin, 2023. "Relaxed deep generative adversarial networks for real-time economic smart generation dispatch and control of integrated energy systems," Applied Energy, Elsevier, vol. 330(PA).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pa:s0306261922015574
    DOI: 10.1016/j.apenergy.2022.120300
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    Cited by:

    1. Yin, Linfei & Lin, Chen, 2024. "Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems," Energy, Elsevier, vol. 298(C).
    2. Zhu, Yilin & Xu, Yujie & Chen, Haisheng & Guo, Huan & Zhang, Hualiang & Zhou, Xuezhi & Shen, Haotian, 2023. "Optimal dispatch of a novel integrated energy system combined with multi-output organic Rankine cycle and hybrid energy storage," Applied Energy, Elsevier, vol. 343(C).
    3. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).

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