With the global energy transition and rapid development of renewable energy, the scheduling optimization challenge for combined power-heat systems under new energy integration and multiple uncertainties has become increasingly prominent. Addressing this challenge, this study proposes an intelligent scheduling method based on the improved Dual-Delay Deep Deterministic Policy Gradient (PVTD3) algorithm. System optimization is achieved by introducing a penalty term for grid power purchase variations. Simulation results demonstrate that under three typical scenarios (10%, 20%, and 30% renewable penetration), the PVTD3 algorithm reduces the system's comprehensive cost by 6.93%, 12.68%, and 13.59% respectively compared to the traditional TD3 algorithm. Concurrently, it reduces the average fluctuation amplitude of grid power purchases by 12.8%. Regarding energy storage management, the PVTD3 algorithm reduces the end-time state values of low-temperature thermal storage tanks by 7.67-17.67 units while maintaining high-temperature tanks within the 3.59-4.25 safety operating range. Multi-scenario comparative validation demonstrates that the proposed algorithm not only excels in economic efficiency and grid stability but also exhibits superior sustainable scheduling capabilities in energy storage device management.

翻译：随着全球能源转型和可再生能源的快速发展，新能源接入与多重不确定性下电热联合系统的调度优化挑战日益凸显。针对这一问题，本研究提出了一种基于改进的双延迟深度确定性策略梯度（PVTD3）算法的智能调度方法。通过引入电网购电波动惩罚项实现系统优化。仿真结果表明，在三种典型场景（10%、20%和30%可再生能源渗透率）下，PVTD3算法相较于传统TD3算法分别将系统综合成本降低了6.93%、12.68%和13.59%，同时将电网购电平均波动幅度降低了12.8%。在储能管理方面，PVTD3算法将低温储热罐的终态值降低了7.67-17.67个单位，同时将高温储热罐维持在3.59-4.25的安全运行区间内。多场景对比验证表明，所提算法不仅在经济效益和电网稳定性方面表现优异，在储能设备管理方面也展现出卓越的可持续调度能力。