摘要
基于极限学习机算法预测风光储功率以及柔性负荷功率,从时间角度出发,先制定日前优化调度,根据实时预测结果对日前优化调度结果采用粒子群算法进行滚动优化,其优化调度的目标为成本最低、网损最小。仿真结果表明:考虑柔性负荷的调峰能力、多目标函数、多时间尺度的优化调度方法能够有效提升含风光储综合能源的经济效益、社会效益,增加了可再生新能源发电的消纳能力,为源荷共同参与削峰填谷协调调度提供了参考。
In this paper,the IES and flexible load power are predicted based on the extreme learning machine algorithm.From the perspective of time,the day-ahead optimal scheduling is developed first,and then the particle swarm optimization algorithm is used to roll the day-ahead dispatching results according to the real-time prediction results.The goal of optimal dispatching is the lowest cost and minimum network loss.The simulation results show that the optimal dispatching method considering the peak regulation capacity,multi-objective function and multi-time scale of flexible load can effectively improve the economic and social benefits of the comprehensive energy with wind storage.This optimal dispatching strategy also increases the absorption capacity of renewable and new energy power generation,and provides a reference for the source load to participate in the coordinated scheduling of peak cutting and valley filling.
作者
徐阳旭
彭学林
张梦
XU Yangxu;PENG Xuelin;ZHANG Meng(State Grid Hubei Wuhan Power Supply Company,Wuhan 430050,China)
出处
《电工材料》
CAS
2023年第4期67-70,76,共5页
Electrical Engineering Materials
关键词
风光储综合能源
柔性负荷
多时间尺度
调度
integrated energy system with solar,wind and energy storage
flexible load
multi-time scale
dispatching strategy
