摘要
随着需求响应机制在电力系统中的推广应用,负荷预测技术面临新的难点与挑战。对考虑需求响应的负荷预测技术发展脉络进行了系统梳理。全面评估了现有技术的优缺点。创新性地从数据质量到模型优化的全链条角度揭示了技术演进,并分析了引入需求响应机制后负荷预测技术面临的关键瓶颈与解决方案。首先,从时间尺度角度对负荷预测技术进行分类,明确了研究范畴。其次,深入剖析了输入数据质量、特征表征、参数优化等技术挑战。针对上述挑战,归纳了数据清洗、特征构建、模型融合等创新性解决方案。最后,结合新型电力系统发展趋势,提出了源荷功率协同预测、多类型需求响应融合建模、面向实时电力市场的动态负荷预测等未来研究方向。该研究为构建智能电网环境下完整的负荷预测理论与应用体系提供了参考。
With the popularization and application of demand response mechanism in power system,load forecasting technologies faces new difficulties and challenges.The development lineage of load forecasting technologies considering demand response is systematically sorted out.The advantages and disadvantages of existing technologies are comprehensively evaluated.The technical evolution is innovatively revealed from the perspective of the whole chain from data quality to model optimization,and the key bottlenecks and solutions faced by the load forecasting technologies after the introduction of the demand response mechanism are analyzed.Firstly,load forecasting technologies are categorized from the perspective of time scale,and the research scope is clarified.Secondly,technological challenges such as input data quality,feature characterization,and parameter optimization,etc.,are deeply analyzed.To address these challenges,innovative solutions such as data cleaning,feature construction,and model fusion,etc.,are summarized.Finally,combining with the development trend of new power system,future research directions such as source-load-power cooperative prediction,multi-type demand response fusion modeling,and dynamic load forecasting for real-time power market,etc.,are proposed.This research provides reference for constructing a complete load forecasting theory and application system in smart grid environment.
作者
田发扬
赵倩宇
王守相
刘文彬
刘洋
李立生
武颖
TIAN Fayang;ZHAO Qianyu;WANG Shouxiang;LIU Wenbin;LIU Yang;LI Lisheng;WU Ying(State Key Laboratory of Intelligent Power Distribution Equipment and System(Tianjin University),Tianjin 300072,China;Key Laboratory of the Ministry of Education on Smart Power Grids(Tianjin University),Tianjin 300072,China;State Grid Shandong Electric Power Research Institute,Jinan 250003,China;Shandong Smart Grid Technology Innovation Center,Jinan 250003,China)
出处
《自动化仪表》
2025年第7期1-8,14,共9页
Process Automation Instrumentation
基金
国家重点研发计划基金资助项目(2022YFB243900)、国网山东省电力公司科技基金资助项目(520626230030)、第二十七届科协年会学术论文“配微储协同的低碳高品质新型配电系统”专题项目。
关键词
智能电网
负荷预测
需求响应
特征工程
深度学习
不确定性建模
Smart grid
Load forecasting
Demand response
Feature engineering
Deep learning
Uncertainty modeling
