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Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy

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  • Yin, Linfei
  • Wu, Yunzhi

Abstract

The large-scale application of renewable energy can promote the global goal of carbon neutrality. However, the stochastic nature of wind and solar energy aggravates the active power imbalance and increases the frequency deviation. These obstacles hinder the load frequency control with the traditional proportional-integral-derivative as the primary approach for automatic generation control. Inspired by the “Divide and Conquer” strategy, a mode-decomposition memory reinforcement network strategy is proposed to reduce the impact of random fluctuations and uncertainties on power systems. The proposed strategy combines the traditional methods and intelligent algorithms for smart generation control. The proposed strategy includes empirical mode decomposition, proportional-integral-derivative, long short-term memory networks, and reinforcement learning algorithms. Firstly, the historical data that has been decomposed by the empirical mode decomposition is utilized to train long short-term memory networks. Then, the trained long short-term memory networks decompose and reorganize the frequency deviation into the high-frequency and low-frequency signals in real-time. Finally, reinforcement learning and proportional-integral-derivative respectively optimize the generation commands by the high-frequency and low-frequency signals to mitigate frequency deviation. Two cases results prove that the mode-decomposition memory reinforcement network has a higher control effect and lower generation cost than the other four strategies. Significantly, the frequency deviation and generation cost are respectively reduced by at least 9.77% and 4.39% in the four-area power system.

Suggested Citation

  • Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    • Handle: RePEc:eee:appene:v:307:y:2022:i:c:s0306261921015282
    DOI: 10.1016/j.apenergy.2021.118266
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    2. Gao, Yuan & Matsunami, Yuki & Miyata, Shohei & Akashi, Yasunori, 2022. "Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system," Applied Energy, Elsevier, vol. 326(C).
    3. Jin, Jingliang & Wen, Qinglan & Zhao, Liya & Zhou, Chaoyang & Guo, Xiaojun, 2023. "Measuring environmental performance of power dispatch influenced by low-carbon approaches," Renewable Energy, Elsevier, vol. 209(C), pages 325-339.
    4. Gong, Linjuan & Hou, Guolian & Li, Jun & Gao, Haidong & Gao, Lin & Wang, Lin & Gao, Yaokui & Zhou, Junbo & Wang, Mingkun, 2023. "Intelligent fuzzy modeling of heavy-duty gas turbine for smart power generation," Energy, Elsevier, vol. 277(C).
    5. Yin, Linfei & Li, Yu, 2022. "Hybrid multi-agent emotional deep Q network for generation control of multi-area integrated energy systems," Applied Energy, Elsevier, vol. 324(C).

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