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arXiv · 2511.15250

Optimized scheduling of electricity-heat cooperative system considering wind energy consumption and peak shaving and valley filling

Jin Ye·Lingmei Wang·Shujian Zhang·Haihang Wu
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Abstract

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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Jin Ye, Lingmei Wang, Shujian Zhang, Haihang Wu. 2025-11-26. Optimized scheduling of electricity-heat cooperative system considering wind energy consumption and peak shaving and valley filling. https://arxiv.org/abs/2511.15250

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