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A new version of membrane search algorithm for hybrid renewable energy systems dynamic scheduling

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Listed:
  • Lai, Wenhao
  • Song, Qi
  • Zheng, Xiaoliang
  • Tao, Qiong
  • Chen, Hualiang
Abstract
It is a consensus that hydropower should be clean and renewable. The economic emission dynamic scheduling of hydro hybrid combined heat and power (Hydro-CHP) systems allows for further reduction of greenhouse gas emissions from the power sector. A new version of our Membrane Search Algorithm, denoted as IMSA, is proposed to solve the complex dynamic scheduling problems of CHP and hydro-thermal systems with spatio-temporal coupling, which can obtain competitive solutions. Furthermore, based on IMSA, we construct a multi-objective algorithm, called the Multi-objective Improved Membrane Search Algorithm (MOIMSA), which can solve the complex economic emission dynamic scheduling problem of CHP systems with ramp limits, and provide a solution set with relatively less emissions. Finally, a solution to the dynamic scheduling problem of the Hydro-CHP system is proposed. IMSA obtains the maximum power output of the hydro unit, and MOIMSA is used to search for the Pareto front of the economic emission of CHP. Compared with the unimproved MOMSA, the compromise solution provided by MOIMSA is better. Experiments show that this study contributes to the efficient use of renewable energy and the reduction of emissions.

Suggested Citation

  • Lai, Wenhao & Song, Qi & Zheng, Xiaoliang & Tao, Qiong & Chen, Hualiang, 2023. "A new version of membrane search algorithm for hybrid renewable energy systems dynamic scheduling," Renewable Energy, Elsevier, vol. 209(C), pages 262-276.
    • Handle: RePEc:eee:renene:v:209:y:2023:i:c:p:262-276
    DOI: 10.1016/j.renene.2023.04.003
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    1. Pang, Xinfu & Wang, Yibao & Yu, Yang & Liu, Wei, 2024. "Optimal scheduling of a cogeneration system via Q-learning-based memetic algorithm considering demand-side response," Energy, Elsevier, vol. 300(C).

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