摘 要
随着电力系统规模的不断扩大和结构的日益复杂，传统静态调度方式难以适应现代电力系统的动态变化需求，为提高电力系统运行效率与稳定性，本研究聚焦于电力系统负荷优化中的动态调度策略。以实现电力系统经济、稳定、高效运行为目的，在分析电力系统负荷特性及影响因素的基础上，提出一种基于改进粒子群算法的动态调度模型，该模型综合考虑了发电成本、网损、污染物排放等多目标因素，并引入滚动时域优化机制以增强模型对系统动态特性的适应能力。通过仿真实验对比分析，结果表明所提模型能够有效降低发电成本，减少网损，优化污染物排放，较传统调度方式具有明显优势。创新性地将改进粒子群算法应用于电力系统动态调度中，解决了多目标优化问题，为电力系统负荷优化提供了新的思路与方法，对提升电力系统运行质量、促进节能减排有着重要意义。
关键词：电力系统动态调度 改进粒子群算法 多目标优化
Abstract
As the scale of power systems continues to expand and their structures become increasingly complex, traditional static scheduling methods struggle to meet the dynamic requirements of modern power systems. To enhance the operational efficiency and stability of power systems, this study focuses on dynamic scheduling strategies for power system load optimization. Aiming to achieve economic, stable, and efficient operation of power systems, an improved particle swarm optimization (PSO)-based dynamic scheduling model is proposed after analyzing the characteristics of power system loads and their influencing factors. This model comprehensively considers multiple ob jectives such as generation costs, network losses, and pollutant emissions, while incorporating a rolling horizon optimization mechanism to strengthen its adaptability to system dynamics. Simulation experiments and comparative analyses demonstrate that the proposed model effectively reduces generation costs, minimizes network losses, and optimizes pollutant emissions, showing significant advantages over traditional scheduling methods. Innovatively applying the improved PSO algorithm to dynamic scheduling in power systems, this study addresses multi-ob jective optimization challenges, providing new approaches and methodologies for power system load optimization. The findings are of great significance for improving the quality of power system operations and promoting energy conservation and emission reduction.
Keyword:Power System Dynamic Scheduling Improved Particle Swarm Optimization Multi-ob jective Optimization
目 录
1绪论 1
1.1电力系统负荷优化的研究背景 1
1.2动态调度策略的意义与价值 1
1.3国内外研究现状综述 1
1.4本文研究方法与技术路线 2
2负荷预测与需求响应分析 2
2.1负荷预测模型构建 2
2.2需求响应机制设计 3
2.3用户侧参与度评估 3
2.4预测结果准确性验证 4
3动态调度算法设计 5
3.1基于智能算法的调度模型 5
3.2实时数据处理与决策支持 5
3.3多目标优化函数构建 6
3.4算法性能测试与改进 6
4系统稳定性与经济性评价 7
4.1系统运行稳定性分析 7
4.2成本效益综合评价 8
4.3环境影响因素考量 8
4.4案例分析与实证研究 9
结论 9
参考文献 11
致谢 12
