A K-means cluster division of regional photovoltaic power stations considering the consistency of photovoltaic output

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS
Jing Ouyang , Lidong Chu , Xiaolei Chen , Yuhang Zhao , Xuanmian Zhu , Tao Liu
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引用次数: 0

Abstract

The uncertainty and volatility of photovoltaics seriously impact the grid's power quality. Short-term photovoltaic(PV) forecasts have a positive effect on the stable operation of the power system. The accuracy of cluster division is a key factor in the output prediction of regional PV power stations. This paper proposes a cluster division method, including a novel feature selection technique and an optimized cluster algorithm based on K-means. The proposed method performs feature analysis and parameter optimization of the division of regional photovoltaic plant clusters, analyzes the clustering dimension of photovoltaic output consistency, and establishes a K-means clustering model of photovoltaic power plants that considers time, space, and inherent characteristics of power plants first. Then, a prediction model based on Long Short-Term Memory (LSTM) is established for each cluster to realize the prediction of regional cluster photovoltaic output. The simulation results demonstrate that the Mean Absolute Percentage Error (MAPE) of the proposed method is 18.27 % and Root Mean Square Error (RMSE) is 45.79 %, which verifies the superiority of the proposed method over comparison models. It shows that the proposed method can effectively solve the problem of low prediction accuracy caused by weak output consistency of power stations in regional photovoltaic clusters.
考虑光伏输出一致性的区域光伏电站集群划分的k均值
光伏发电的不确定性和波动性严重影响了电网的电能质量。光伏短期预测对电力系统的稳定运行具有积极作用。集群划分的准确性是影响区域光伏电站产量预测的关键因素。本文提出了一种聚类划分方法,包括一种新的特征选择技术和一种基于K-means的优化聚类算法。该方法对区域光伏电站集群划分进行特征分析和参数优化,分析光伏输出一致性的聚类维度,建立先考虑时间、空间和电站固有特征的光伏电站K-means聚类模型。然后,对每个集群建立基于长短期记忆(LSTM)的预测模型,实现对区域集群光伏产量的预测。仿真结果表明,该方法的平均绝对百分比误差(MAPE)为18.27 %,均方根误差(RMSE)为45.79 %,验证了该方法相对于比较模型的优越性。结果表明,该方法能有效解决区域光伏集群内电站输出一致性弱导致的预测精度低的问题。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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