电力系统负荷预测与调度优化方法研究
Research on Load Forecasting and Scheduling Optimization Methods for Power Systems
李梦鸽1
作者信息
- 1. 国网陕西省电力有限公司澄城县供电分公司,陕西澄城 715200
- 折叠
摘要
为保证电网供需平衡,准确预测电力系统负荷与合理制订调度方案至关重要.文章通过分析统计模型、机器学习和深度学习在负荷预测中的应用,以及数学规划、模拟退火和遗传算法在负荷调度优化中的应用,深入研究了负荷预测与调度优化的关联性,提出了基于目标匹配、约束协调和算法集成的协同优化策略.案例分析结果表明,相比于独立的预测与调度方法,协同优化可显著降低预测误差和调度成本.因此,构建跨层次的协同控制,是实现电网高效智能运营的有效途径.
Abstract
To ensure the balance of supply and demand in the power grid,it is crucial to accurately predict the load of the power system and formulate a reasonable scheduling plan.The article analyzes the applications of statistical models,machine learning,and deep learning in load forecasting,as well as the applications of mathematical programming,simulated annealing,and genetic algorithms in load scheduling optimization.It deeply studies the correlation between load forecasting and scheduling optimization,and proposes a collaborative optimization strategy based on goal matching,constraint coordination,and algorithm integration.The case analysis results indicate that compared to independent prediction and scheduling methods,collaborative optimization can significantly reduce prediction errors and scheduling costs.Therefore,building cross level collaborative control is an effective way to achieve efficient and intelligent operation of the power grid.
关键词
电力系统/负荷预测/调度优化/协同控制Key words
power system/load forecasting/scheduling optimization/collaborative control引用本文复制引用
出版年
2024
NETL
NSTL
万方数据