用改进算法优化电热协同系统调度，提升风电消纳与电网稳定
Optimized scheduling of electricity-heat cooperative system considering wind energy consumption and peak shaving and valley filling
- 基于改进的PVTD3算法，引入购电波动惩罚项优化调度
- 在30%风电渗透率下降低综合成本13.59%，购电波动减少12.8%
- 有效管理储热罐状态，兼顾经济性与设备安全
在全球能源转型和可再生能源快速发展的背景下，新能源接入下的电热协同系统调度优化面临多重不确定性挑战。本文提出一种基于改进双延迟深度确定性策略梯度（PVTD3）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，在风电渗透率分别为10%、20%、30%的三种典型场景下，该算法相较传统TD3算法分别降低系统综合成本6.93%、12.68%、13.59%，同时将平均购电波动幅度降低12.8%。在储能管理方面，低温度储热罐末端状态值降低7.67-17.67单位，而高温储热罐始终维持在3.59-4.25的安全运行区间内。多场景对比验证显示，该算法在经济性、电网稳定性及储能设备可持续调度能力上均表现优异。
原文摘要 · Abstract (English)
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.
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