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<p>To improve modeling accuracy and operational response in generator–storage hybrid power systems, this paper proposes an AI-enhanced collaborative modeling method integrating a multi-task attention neural predictor and a policy-gradient reinforcement learning scheduler. The predictor adopts an encoder–attention–decoder structure, and the scheduler employs the Proximal Policy Optimization (PPO) algorithm for adaptive decision-making under dynamic disturbances. Experiments were conducted in a MATLAB/Simulink–Python co-simulation environment using 2.4 × 10⁵ mixed samples from public and laboratory datasets. Compared with MPC, Heuristic-EMS, and NN-Predict baselines, the proposed method achieved a MAPE of 4.2%, an energy utilization efficiency of 92.4%, a SOC-RMSE of 3.6%, and an average response time of 36 ms. Repeated experiments over ten independent random seeds further showed that the proposed method maintained stable performance under load fluctuation, renewable-output disturbance, and temporary generator-failure scenarios, confirming its robustness and engineering adaptability. The results verify that the method effectively enhances the intelligent operation and optimization capability of generator–storage integrated systems.</p>

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Collaborative modeling and scheduling for generator and energy storage hybrid power systems using multitask prediction and reinforcement learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月25日

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  • Yiqun Zhang,
  • Yan Tao,
  • Xiaotao Yu,
  • Mingxing Tang,
  • Zhongyu Zhang

摘要

To improve modeling accuracy and operational response in generator–storage hybrid power systems, this paper proposes an AI-enhanced collaborative modeling method integrating a multi-task attention neural predictor and a policy-gradient reinforcement learning scheduler. The predictor adopts an encoder–attention–decoder structure, and the scheduler employs the Proximal Policy Optimization (PPO) algorithm for adaptive decision-making under dynamic disturbances. Experiments were conducted in a MATLAB/Simulink–Python co-simulation environment using 2.4 × 10⁵ mixed samples from public and laboratory datasets. Compared with MPC, Heuristic-EMS, and NN-Predict baselines, the proposed method achieved a MAPE of 4.2%, an energy utilization efficiency of 92.4%, a SOC-RMSE of 3.6%, and an average response time of 36 ms. Repeated experiments over ten independent random seeds further showed that the proposed method maintained stable performance under load fluctuation, renewable-output disturbance, and temporary generator-failure scenarios, confirming its robustness and engineering adaptability. The results verify that the method effectively enhances the intelligent operation and optimization capability of generator–storage integrated systems.