摘 要
随着全球能源需求的持续增长和可再生能源的大规模接入，电力系统调度面临复杂性增加、不确定性增强等多重挑战。为提升电力系统的运行效率与经济性，同时保障其安全性和稳定性，本研究基于智能算法提出了一种适用于现代电力系统的优化调度方法。研究以降低系统运行成本、提高可再生能源消纳能力为目标，综合考虑负荷波动、发电机组特性及网络约束等因素，构建了多目标优化模型。通过引入改进的粒子群优化算法和深度强化学习技术，有效解决了传统优化方法在高维复杂问题中的计算瓶颈和收敛困难问题。仿真结果表明，所提方法能够在保证调度方案可行性的前提下显著减少系统总运行成本，并实现对可再生能源出力的高效利用。此外，该方法还展现出较强的鲁棒性和适应性，能够应对多种不确定场景下的调度需求。
关键词：电力系统调度 智能算法 多目标优化
Abstract
With the continuous growth of global energy demand and the large-scale access of renewable energy, power system scheduling faces multiple challenges such as increasing complexity and increasing uncertainty. In order to improve the operation efficiency and economy of power system and ensure its safety and stability, this study proposes an optimal dispatching method for modern power system based on intelligent algorithm. With the goal of reducing the system operation cost and improving the absorption capacity of renewable energy, a multi-ob jective optimization model is constructed by comprehensively considering the factors such as load fluctuation, generator set characteristics and network constraints. By introducing an improved particle swarm optimization algorithm and a deep reinforcement learning technique, the computational bottleneck and convergence difficulties of traditional optimization methods in high-dimensional complex problems are effectively solved. The simulation results show that the proposed method can significantly reduce the total operating cost of the system and realize the efficient utilization of renewable energy output while ensuring the feasibility of the scheduling scheme. In addition, the method also shows strong robustness and adaptability, and can cope with the scheduling requirements in multiple uncertain scenarios.
Keyword:Power System Dispatch Intelligent Algorithm Multi-ob jective Optimization
目 录
引言 1
1电力系统优化调度基础 1
1.1电力系统调度概述 1
1.2优化调度的基本原理 2
1.3传统优化方法的局限性 2
1.4智能算法在调度中的优势 3
2智能算法在电力系统中的应用分析 3
2.1常见智能算法简介 3
2.2智能算法的适用场景 4
2.3算法性能评估指标 4
2.4应用案例分析 4
3基于智能算法的优化调度模型构建 5
3.1调度目标函数设计 5
3.2约束条件的数学表达 5
3.3模型参数的选择与调整 6
3.4模型验证与测试 6
4智能算法驱动的调度策略改进研究 7
4.1动态调度策略设计 7
4.2不确定性因素的处理方法 7
4.3多目标优化的实现路径 8
4.4实时调度与预测结合 8
结论 9
参考文献 10
致谢 11
