Preliminary Study on Spatial Complementarity Characteristics of Short-term Power Prediction for Distributed Photovoltaic Clusters

RUAN Chenglong; LI Kangping; LI Zhenghui; HUANG Chunyi

doi:10.7500/AEPS20230518009

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Preliminary Study on Spatial Complementarity Characteristics of Short-term Power Prediction for Distributed Photovoltaic Clusters

Basic Research | 更新时间：2025-06-16

- Preliminary Study on Spatial Complementarity Characteristics of Short-term Power Prediction for Distributed Photovoltaic Clusters
- Automation of Electric Power Systems Vol. 48, Issue 3, Pages: 42-50(2024)
- 作者机构：
  
  1.上海交通大学智慧能源创新学院，上海市 200240
  2.上海非碳基能源转换与利用研究院，上海市 200240
  3.清华大学电机工程与应用电子技术系，北京市 100084
  4.电力传输与功率变换控制教育部重点实验室（上海交通大学），上海市 200240
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52107103);the State Key Laboratory of Power System Operation and Control(SKLD22KM13)
- DOI：10.7500/AEPS20230518009
  CLC：
- Received：18 May 2023，
  
  Revised：2023-08-11，
  
  Published：10 February 2024
- 稿件说明：
移动端阅览
阮呈隆,李康平,李正辉等.分布式光伏集群功率短期预测的空间互补特性初探[J].电力系统自动化,2024,48(3):42-50.

RUAN Chenglong,LI Kangping,LI Zhenghui,et al.Preliminary Study on Spatial Complementarity Characteristics of Short-term Power Prediction for Distributed Photovoltaic Clusters[J].Automation of Electric Power Systems,2024,48(3):42-50.
阮呈隆,李康平,李正辉等.分布式光伏集群功率短期预测的空间互补特性初探[J].电力系统自动化,2024,48(3):42-50. DOI： 10.7500/AEPS20230518009.

RUAN Chenglong,LI Kangping,LI Zhenghui,et al.Preliminary Study on Spatial Complementarity Characteristics of Short-term Power Prediction for Distributed Photovoltaic Clusters[J].Automation of Electric Power Systems,2024,48(3):42-50. DOI： 10.7500/AEPS20230518009.

摘要

随着分布式光伏装机容量的快速增长，分布式光伏集群功率预测对电网调度的影响日益增强。分布式光伏集群功率预测存在“先累加后预测”“先预测后累加”以及“先聚类再预测”3种技术路线。通过在含600多个分布式光伏站点实测功率数据集上的实验发现，分布式光伏集群功率预测存在“空间互补”现象，即3类集群预测路线得到的预测误差相比于单个场站的平均预测误差都有所降低。为了探究空间互补现象的产生机理及影响因素，首先从机理分析的角度将其归纳为“出力曲线互补”和“预测误差互补”两类空间互补特性。然后，提出了“空间互补系数”这一概念，对互补效果进行量化评估。最后，探究了集群规模、分布范围、天气类型和聚类簇数4种因素对空间互补特性的影响。结果表明，两类空间互补特性对分布式光伏集群短期功率预测精度提升效果显著，且预测误差互补在精度上优于出力曲线互补。研究结果可为分布式光伏集群划分提供依据，有助于实现更加高效且准确的功率预测。

Abstract

With the rapid growth of distributed photovoltaic (PV) installed capacity

the impact of power prediction for distributed PV clusters on grid dispatch is becoming increasingly significant. There are three technical routes for power prediction of distributed PV clusters

i.e.

accumulation before prediction

prediction before accumulation and clustering before prediction. Through experiments on a dataset of over 600 distributed PV sites

the spatial complementarity is found in the power prediction for distributed PV clusters

whereby the prediction errors of the three cluster prediction routes are lower than the average prediction error of a single site. To explore the generation mechanism and influencing factors of the spatial complementarity

this paper first categorizes it into two types of spatial complementarity characteristics

namely power curve complementarity and prediction error complementarity

based on the mechanism analysis. Secondly

the concept of spatial complementarity coefficient is proposed to quantitatively evaluate the complementary effect. Finally

the effects of cluster scale

distribution range

weather type and number of clusters on the spatial complementarity characteristics are explored. The results show that the two types of spatial complementarity characteristics have significant effects on improving the short-term power prediction accuracy of distributed PV clusters

with prediction error complementarity superior to power curve complementarity. The research results can provide a basis for the division of distributed PV clusters and contribute to achieving more efficient and accurate power prediction.

关键词

Keywords

references

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