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Microgrid cooperative scheduling optimization based on quality of service constraint and deep Q-network algorithm

Author

Listed:
  • Jie Zhou
  • Rong Lu

Abstract

This paper proposes a quality of service Cloud Workflow Firefly Algorithm (CWFA), which considers dynamic priorities. Whenever the algorithm experiences a new state–action pair, this experience is recorded as part of the training data. Genetic algorithm (GA) is introduced to optimize the initial parameters of deep Q-network (DQN). Through GA, a better initial weight can be found, so as to improve the estimation of Q-value, making the overall workflow scheduling more efficient. The simulation results show that, compared with other methods, CWFA–GA–DQN can effectively improve the efficiency of microgrid cooperative scheduling.

Suggested Citation

  • Jie Zhou & Rong Lu, 2025. "Microgrid cooperative scheduling optimization based on quality of service constraint and deep Q-network algorithm," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 47-57.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:47-57.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctae258
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