基于光伏发电量预测的含氢储能微网分段优化调度  

Sectional Optimal Dispatching of Hydrogen Storage Microgrid Based on Photovoltaic Generation Prediction

作　　者：王小昔 雷勇[1] 张汀 WANG Xiao-xi;LEI Yong;ZHANG Ting(College of Electrical Engineering,Sichuan University,Chengdu 610000,China)

机构地区：[1]四川大学电气工程学院,成都610000

出　　处：《科学技术与工程》2023年第19期8218-8226,共9页Science Technology and Engineering

基　　金：四川省科技计划(2021YFG0254)。

摘　　要：针对多储能微网如何高效、经济运行,搭建了基于光伏发电的含氢储能、蓄电池储能的微网系统,采用一种日前预测调度与日内实时调度相结合的分段调度策略。在日前预测调度阶段,采用基于麻雀搜索算法优化支持向量机模型提高对日前的光伏发电量和负荷预测的精度,以微网最小使用成本为目标,考虑系统运行的可靠性,采用改进粒子群算法制定微网的日前最优调度策略。在日内调度阶段,考虑氢储能系统的响应延迟特性,以蓄电池为灵活补充元件,制定实时调整微网运行策略,消除预测误差带来的影响。最后,结合实际算例分析,验证了分段优化调度的可行性。结果表明,提出的方法能够有效预测数据,减少微网调度的响应时间,提高系统运行的经济性和稳定性。For the efficient and economic operation of multiple energy storage microgrids,a microgrid system with hydrogen storage and battery storage based on photovoltaic power generation was built,and a segmented scheduling strategy combining day ahead predictive scheduling and day in real time scheduling was adopted.In the day ahead forecasting and dispatching stage,the sparrow search algorithm was used to optimize the support vector machine model to improve the accuracy of the day ahead photovoltaic power generation and load forecasting.With the minimum use cost of the microgrid as the goal,considering the reliability of the system operation,the improved particle swarm optimization algorithm was used to formulate the day ahead optimal dispatching strategy of the microgrid.In the intra day scheduling phase,considering the response delay characteristics of the hydrogen energy storage system,the battery was used as a flexible supplementary element to develop a real-time adjustment strategy for the microgrid operation to eliminate the impact of prediction errors.Finally,the feasibility of subsection optimal scheduling was verified by analyzing an actual example.The results show that the proposed method can effectively predict data,reduce the response time of microgrid scheduling,and improve the economy and stability of system operation.

关 键 词：日前预测调度 日内实时调度 改进支持向量机算法 改进粒子群算法 

分 类 号：TM73[电气工程—电力系统及自动化]