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<p>In this paper, the storage battery and photovoltaic generator set are integrated into the combined cooling, heating, and power (CCHP) system to reduce its operating cost. Four scenarios with or without storage battery are investigated for the CCHP system. QTPSO that combines the Q-learning algorithm and tangent flight strategy is proposed for the four CCHP scenarios in spring and winter. The Q-learning algorithm enables the population to utilize information from previous iterations, adaptively selecting the optimal speed and position update formula based on the environment and population state. The tangent flight strategy expands the search space of the population and improves the global search capability of the algorithm. Experimental results indicate that the QTPSO outperforms four other algorithms, achieving the lowest operating cost among the four CCHP scenarios. Additionally, the CCHP system with battery exhibits a lower operating cost. In spring, the operating cost of the CCHP system with battery is 8.2% lower than that the system without battery. In winter, the operating costs for CCHP systems with battery is 8.42% lower than that the system without battery.</p>

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An improved particle swarm optimization using Q-learning and tangent flight strategy for the combined cooling, heating, and power system

  • 期刊论文

  • 发表日期：

    2025年4月10日

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  • Zhaojun Zhang,
  • Hongjie Luo,
  • Simeng Tan,
  • Kuansheng Zou,
  • Shengwu Zhou

摘要

In this paper, the storage battery and photovoltaic generator set are integrated into the combined cooling, heating, and power (CCHP) system to reduce its operating cost. Four scenarios with or without storage battery are investigated for the CCHP system. QTPSO that combines the Q-learning algorithm and tangent flight strategy is proposed for the four CCHP scenarios in spring and winter. The Q-learning algorithm enables the population to utilize information from previous iterations, adaptively selecting the optimal speed and position update formula based on the environment and population state. The tangent flight strategy expands the search space of the population and improves the global search capability of the algorithm. Experimental results indicate that the QTPSO outperforms four other algorithms, achieving the lowest operating cost among the four CCHP scenarios. Additionally, the CCHP system with battery exhibits a lower operating cost. In spring, the operating cost of the CCHP system with battery is 8.2% lower than that the system without battery. In winter, the operating costs for CCHP systems with battery is 8.42% lower than that the system without battery.