JOURNAL ARTICLE

An improved memetic algorithm with Q-learning for low carbon economic scheduling of cogeneration system.

  • Published In: Journal of Intelligent & Fuzzy Systems, 2023, v. 45, n. 6. P. 11585 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wang, Liming; Liu, Yingming; Pang, Xinfu; Wang, Qimin; Wang, Xiaodong 3 of 3

Abstract

The article focuses on a low-carbon economic scheduling method for cogeneration systems using a Q-learning-based multi-objective memetic algorithm (Q-MOMA) to enhance operational economy and reduce carbon emissions. The model integrates a carbon capture device, heat storage, and demand response mechanisms to increase system flexibility and wind power utilization. The Q-MOMA algorithm dynamically adjusts crossover and mutation probabilities via Q-learning to improve search efficiency, and employs a fuzzy membership function for multi-objective decision-making balancing economy and low carbon. Simulation results demonstrate that the proposed model and algorithm outperform other optimization methods in improving wind power consumption, reducing carbon emissions, and achieving better convergence and diversity in scheduling solutions.

Additional Information

  • Source:Journal of Intelligent & Fuzzy Systems. 2023/12, Vol. 45, Issue 6, p11585
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2023
  • ISSN:1064-1246
  • DOI:10.3233/JIFS-231824
  • Accession Number:174544523
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