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

TL;DR

This paper introduces an improved deep reinforcement learning algorithm for optimizing the scheduling of integrated power-heat systems, effectively reducing costs and stabilizing grid power under high renewable energy penetration.

Contribution

It proposes a novel PVTD3 algorithm that enhances system scheduling by incorporating penalty terms, improving economic efficiency and energy storage management in renewable-rich environments.

Findings

Reduces system cost by up to 13.59% under high renewable scenarios

Decreases grid power fluctuation amplitude by 12.8%

Improves energy storage management stability

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.