Multi-time-scale dispatch strategy for integrated energy systems based on deep reinforcement learning
基于深度强化学习的综合能源系统多时间尺度调度策略

摘要

本文提出了一种基于深度强化学习的综合能源系统多时间尺度调度策略，旨在提升能源系统调度效率与可靠性。通过构建多时间尺度模型，结合深度强化学习技术，能够动态优化能源生产、传输及消费的调度决策。通过模拟与实验验证，所提策略在面对复杂系统变化时表现出较传统方法更优的性能，不仅能降低系统成本，还能提高系统的能源利用率和灵活性。该方法具有较强的应用前景，尤其在智能电网和可再生能源接入的背景下，能够实现更加精细的调度优化。

Abstract

This paper proposes a multi-time-scale dispatch strategy for integrated energy systems based on deep reinforcement learning, aiming to enhance the efficiency and reliability of energy system dispatch. By constructing a multi-time-scale model and integrating deep reinforcement learning techniques, the strategy enables dynamic optimization of dispatch decisions for energy production, transmission, and consumption. Simulation and experimental results demonstrate that the proposed strategy outperforms traditional methods when dealing with complex system dynamics, not only reducing system costs but also improving energy utilization and flexibility. The method shows strong potential for practical applications, particularly in the context of smart grids and renewable energy integration, enabling more refined dispatch optimization.