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中南大学学报(自然科学版)

Journal of Central South University

第49卷    第10期    总第290期    2018年10月

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文章编号:1672-7207(2018)10-2618-07
配置储能装置的光伏预测配网优化研究
夏向阳1,易浩民1,陈向群2,陈仲伟3,熊德智2,黄瑞2,王逸超3,曾小勇1,邓丰1,胡蓉朝辉1,黄海1

(1. 长沙理工大学 电气与信息工程学院,湖南 长沙,410114;
2. 国网湖南省供电服务中心(计量中心),湖南 长沙,410004;
3. 国网湖南省电力公司 经济技术研究院,湖南 长沙,410004
)

摘 要: 在分析区域光伏并入配网时交流母线PCC点电压波动机理的前提下,提出一种配置储能装置的光伏预测配网优化运行方法。该方法分析光伏发电输出功率与多种气象因素的相关性,将多种气象因素作为多个信息源处理,运用信息融合理论将其加权为一个综合影响因子λ,建立以λ为输入的BP神经网络预测模型,将模型输出的预测值实时传送给储能装置,采用基于滤波原理的光伏发电输出功率平滑控制,实现光伏功率平滑输出,这种方法可以和分时电价有效结合,既提高配网运行经济性的同时稳定节点电压,降低节点电压越限的可能。研究结果表明:所述预测模型具有较高的预测精度,对配网的安全优化运行有一定作用。

 

关键字: 电压波动;信息融合;粒子群算法;BP神经网络;优化运行

Optimal operation of photovoltaic forecast distribution network based on information fusion theory
XIA Xiangyang1, YI Haomin1, CHEN Xiangqun2, CHEN Zhongwei3, XIONG Dezhi2, HUANG Rui2, WANG Yichao3, ZENG Xiaoyong1, DENG Fen1, HU Rongzhaohui1, HUANG Hai1

1. College of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, China;
2. Metering Center of State Grid Hunan Electric Power Company, Changsha 410004, China;
3. Hunan Electric Power Corporation Economic & Technical Research Institute, Changsha 410004, China

Abstract:An optimized operation method of PV forecasting distribution network with energy storage device was proposed based on the analysis of the voltage fluctuation mechanism of AC busbar PCC point when regional PV was incorporated into the distribution network. The method analyzed the correlation between photovoltaic power output and various meteorological factors. Treating various meteorological factors as multiple information sources, and using information fusion theory to weight it into a comprehensive impact factor λ, BP neural network prediction was established taking λ as an input. The model transmitted the predicted value of the model output to the energy storage device in real time, and adopted the smoothing control of the photovoltaic power generation output power based on the filtering principle to realize the smooth output of the photovoltaic power. The results show that the method can be effectively combined with the time-sharing electricity price, and the distribution network operation is improved. The economical stability of the PCC point voltage at the same time reduces the possibility that the node voltage exceeds the limit. The prediction model has higher prediction accuracy and has a certain effect on the safety optimization operation of the distribution network.

 

Key words: voltage fluctuation; information fusion; particle swarm optimization algorithm; BP neural network;optimized operation

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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