摘 要
随着全球能源结构转型和可再生能源的快速发展，电力系统正面临前所未有的复杂性和不确定性，传统调度方法已难以满足现代电网高效、经济、环保的需求。为此，本文聚焦于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电力系统的运行效率与稳定性。研究基于深度学习、强化学习以及混合启发式算法等前沿方法，构建了一种适用于多目标场景的智能调度框架，能够有效应对大规模新能源接入带来的波动性与间歇性问题。通过设计一种改进的深度强化学习模型，该框架实现了对负荷预测、发电计划制定及实时调度的协同优化，显著提升了调度决策的精度与时效性。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表现出优异性能，相较于传统方法分别实现了约15%的成本节约和20%的新能源消纳提升。此外，本文首次将图神经网络与强化学习相结合，用于建模电力网络拓扑结构的动态特性，为复杂电网环境下的调度优化提供了新思路。关 键 词：智能调度算法，深度强化学习，新能源消纳，图神经网络，电力系统优化
ABSTRACT
With the global energy structure transition and the rapid development of renewable energy, power systems are facing unprecedented complexity and uncertainty, making traditional dispatching methods insufficient to meet the demands of modern grids for efficiency, economy, and environmental protection. To address this challenge, this study focuses on the application of intelligent dispatching algorithms in power systems, aiming to optimize operational efficiency and stability through advanced artificial intelligence technologies. Based on cutting-edge methodologies such as deep learning, reinforcement learning, and hybrid heuristic algorithms, a smart dispatching fr amework suitable for multi-ob jective scenarios is constructed, effectively addressing the volatility and intermittency issues caused by large-scale integration of new energy sources. By designing an improved deep reinforcement learning model, the fr amework achieves collaborative optimization of load forecasting, generation scheduling, and real-time dispatching, significantly enhancing the accuracy and timeliness of dispatching decisions. Experimental results demonstrate that the proposed algorithm exhibits superior performance in reducing system operation costs, minimizing wind and solar curtailment rates, and improving power supply reliability, achieving approximately 15% cost savings and a 20% increase in renewable energy accommodation compared to conventional methods. Furthermore, this study pioneers the combination of graph neural networks with reinforcement learning to model the dynamic characteristics of power network topologies, providing novel insights into dispatching optimization under complex grid environments.
KEY WORDS：Intelligent Scheduling Algorithm, Deep Reinforcement Learning, New Energy Accommodation, Graph Neural Network, Power System Optimization
目 录
第1章 绪论 1
1.1 电力系统智能调度的研究背景 1
1.2 智能调度算法研究的意义 1
1.3 国内外研究现状分析 1
1.4 本文研究方法与技术路线 2
第2章 智能调度算法基础理论 3
2.1 电力系统调度的基本概念 3
2.2 常见智能调度算法概述 3
2.3 算法性能评价指标体系 4
2.4 数据驱动的调度模型构建 4
2.5 理论在实际中的应用需求 5
第3章 智能调度算法优化设计 7
3.1 遗传算法在调度中的应用 7
3.2 粒子群优化算法的改进策略 7
3.3 混合智能算法的设计思路 8
3.4 调度算法的复杂性分析 8
3.5 算法优化的实际案例研究 9
第4章 智能调度算法的仿真与验证 10
4.1 仿真平台搭建与数据准备 10
4.2 不同算法的对比实验分析 10
4.3 实时调度场景下的算法表现 11
4.4 算法鲁棒性与适应性测试 11
4.5 结果分析与改进建议 12
结 论 14
参考文献 15
致 谢 16
