电力系统中的智能调度算法研究
摘 要
随着能源结构转型和可再生能源渗透率的提升，电力系统运行环境日益复杂，传统调度方法已难以满足高效、灵活的需求。为此，本文聚焦智能调度算法在电力系统中的应用研究，旨在通过引入先进的人工智能技术优化调度策略，提升系统的经济性和稳定性。研究基于深度强化学习框架设计了一种自适应智能调度算法，该算法能够实时处理大规模电网数据，并结合预测模型实现对负荷波动和新能源出力的精准响应。实验结果表明，所提算法在降低系统运行成本、减少弃风弃光率以及提高供电可靠性方面具有显著优势。与现有方法相比，其创新点在于融合多源信息的动态决策机制，有效平衡了调度效率与安全性。研究表明，智能调度算法为未来复杂电力系统的高效运行提供了可行的技术路径，具有重要的理论价值和实际应用前景。
关键词：智能调度算法；深度强化学习；可再生能源；负荷预测；弃风弃光率
Abstract
With the transformation of energy structures and the increasing penetration of renewable energy, the operational environment of power systems is becoming increasingly complex, making traditional scheduling methods insufficient to meet the demands for efficiency and flexibility. This study focuses on the application of intelligent scheduling algorithms in power systems, aiming to optimize scheduling strategies through advanced artificial intelligence technologies to enhance both the economic viability and stability of the system. Based on a deep reinforcement learning fr amework, an adaptive intelligent scheduling algorithm is designed, which can process large-scale grid data in real time and integrate predictive models to accurately respond to load fluctuations and renewable energy output variations. Experimental results demonstrate that the proposed algorithm exhibits significant advantages in reducing system operating costs, minimizing wind and solar curtailment rates, and improving power supply reliability. Compared with existing methods, its innovation lies in the dynamic decision-making mechanism that incorporates multi-source information, effectively balancing scheduling efficiency and safety. The research indicates that intelligent scheduling algorithms provide a feasible technical pathway for the efficient operation of future complex power systems, possessing important theoretical value and practical application prospects.
Keywords: Intelligent Scheduling Algorithm；Deep Reinforcement Learning；Renewable Energy；Load Forecasting；Wind And Solar Abandonment Rate
目 录
摘 要 I
Abstract II
引言 1
一、智能调度算法概述 1
（一）电力系统调度基础 1
（二）智能调度算法分类 2
（三）算法在电力系统中的作用 2
二、关键技术与方法分析 2
（一）数据驱动的优化技术 2
（二）人工智能算法的应用 3
（三）实时调度的技术挑战 3
三、算法性能评估与改进 4
（一）性能评估指标体系 4
（二）常见问题及优化策略 4
（三）改进算法的实际案例 5
四、智能调度的未来趋势 5
（一）新能源接入的影响 5
（二）分布式调度的发展方向 5
（三）多目标优化的研究前景 6
结 论 6
致 谢 8
参考文献 9
摘 要
随着能源结构转型和可再生能源渗透率的提升，电力系统运行环境日益复杂，传统调度方法已难以满足高效、灵活的需求。为此，本文聚焦智能调度算法在电力系统中的应用研究，旨在通过引入先进的人工智能技术优化调度策略，提升系统的经济性和稳定性。研究基于深度强化学习框架设计了一种自适应智能调度算法，该算法能够实时处理大规模电网数据，并结合预测模型实现对负荷波动和新能源出力的精准响应。实验结果表明，所提算法在降低系统运行成本、减少弃风弃光率以及提高供电可靠性方面具有显著优势。与现有方法相比，其创新点在于融合多源信息的动态决策机制，有效平衡了调度效率与安全性。研究表明，智能调度算法为未来复杂电力系统的高效运行提供了可行的技术路径，具有重要的理论价值和实际应用前景。
关键词：智能调度算法；深度强化学习；可再生能源；负荷预测；弃风弃光率
Abstract
With the transformation of energy structures and the increasing penetration of renewable energy, the operational environment of power systems is becoming increasingly complex, making traditional scheduling methods insufficient to meet the demands for efficiency and flexibility. This study focuses on the application of intelligent scheduling algorithms in power systems, aiming to optimize scheduling strategies through advanced artificial intelligence technologies to enhance both the economic viability and stability of the system. Based on a deep reinforcement learning fr amework, an adaptive intelligent scheduling algorithm is designed, which can process large-scale grid data in real time and integrate predictive models to accurately respond to load fluctuations and renewable energy output variations. Experimental results demonstrate that the proposed algorithm exhibits significant advantages in reducing system operating costs, minimizing wind and solar curtailment rates, and improving power supply reliability. Compared with existing methods, its innovation lies in the dynamic decision-making mechanism that incorporates multi-source information, effectively balancing scheduling efficiency and safety. The research indicates that intelligent scheduling algorithms provide a feasible technical pathway for the efficient operation of future complex power systems, possessing important theoretical value and practical application prospects.
Keywords: Intelligent Scheduling Algorithm；Deep Reinforcement Learning；Renewable Energy；Load Forecasting；Wind And Solar Abandonment Rate
目 录
摘 要 I
Abstract II
引言 1
一、智能调度算法概述 1
（一）电力系统调度基础 1
（二）智能调度算法分类 2
（三）算法在电力系统中的作用 2
二、关键技术与方法分析 2
（一）数据驱动的优化技术 2
（二）人工智能算法的应用 3
（三）实时调度的技术挑战 3
三、算法性能评估与改进 4
（一）性能评估指标体系 4
（二）常见问题及优化策略 4
（三）改进算法的实际案例 5
四、智能调度的未来趋势 5
（一）新能源接入的影响 5
（二）分布式调度的发展方向 5
（三）多目标优化的研究前景 6
结 论 6
致 谢 8
参考文献 9
