基于残差卷积自编码的风光荷场景生成方法

doi:10.12204/j.issn.1000-7229.2021.08.002

摘要/Abstract

摘要：

随着风、光等新能源的发展,对含大量风、光电网的调控和运行提出了更高的要求。典型场景是处理该问题的主要方式之一。针对传统聚类生成典型场景的方法易产生数据信息损失、特征提取不够精确等问题,提出了一种基于残差卷积自编码聚类的风光荷不确定性源场景生成方法。首先,利用残差卷积自编码器网络提取风光荷数据的特征,在减少数据信息损失并考虑风光荷耦合性的前提下,降低数据维度;然后,为了减少噪声数据对实验结果产生的影响,利用k-medoids进行聚类从而生成典型场景。最后,以西北某地区电网实际采集数据为研究对象进行算例分析, 与传统聚类方法进行戴维斯堡丁指数(Davies-Bouldin index,DBI)、Calinski-Harabaz指数(Calinski-Harabasz index,CHI)等指标对比,验证了所提方法的可行性。

关键词: 残差神经网络, 多通道卷积自编码, 风光荷特征提取, 场景生成

Abstract:

With the development of new energy sources, higher requirements are put forward for the regulation and operation of grids with PV and wind power. The typical scenario is one of the main ways to deal with this problem. The traditional method for generating typical scenarios is prone to data information loss and feature extraction inaccuracy. This paper proposes an uncertain wind-PV-load typical scenario generation method based on residual convolution auto-encoders. First, the residual convolution auto-encoders network is used to extract the characteristics of wind-PV-load data to reduce the data dimension while reducing the loss of data information and taking into account the coupling of wind and solar power. Then, reducing the influence of noise data on the experimental results, k-medoids is used for clustering to generate typical scenarios. The actual data collected from a power grid in northwest China is taken as the research object. Comparison with traditional clustering methods such as DBI (Davies-Bouldin Index), CHI (Calinski-Harabasz Index) and other indicators, the feasibility of the proposed method is verified.

Key words: residual neural network, multi-channel convolutional auto-encoder, wind-PV-load feature extraction, scenario generation

中图分类号:

TM715

彭雨筝, 李晓露, 李聪利, 丁一. 基于残差卷积自编码的风光荷场景生成方法[J]. 电力建设, 2021, 42(8): 10-17.

PENG Yuzheng, LI Xiaolu, LI Congli, DING Yi. Typical Wind-PV-Load Scenario Generation Based on Residual Convolutional Auto-encoders[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(8): 10-17.

图/表 15

图1 卷积自编码模型结构

Fig.1 Structure of CAE model

图2 残差模块结构

Fig.2 Structure of residual module

图3 残差网络架构示例

Fig.3 Example of residual network architecture

图4 残差块的2种结构形式

Fig.4 Two structural forms of residuals blocks

图5 利用残差网络改进的多通道卷积自编码器

Fig.5 Improved multi-channel convolutional encoder using residual network

图6 基于RESCAE-kmedoids风光荷场景生成框架

Fig.6 The framework structure of wind-PV-load scenario generation based on RESCAE-kmedoids

图7 轮廓系数评价指标变化

Fig.7 Change of SIL evaluation index

图8 风光荷年典型场景

Fig.8 Typical annual wind-PV-load scenarios

图9 风光荷季典型场景

Fig.9 Typical seasonal wind-PV-load scenarios

表1 风光荷年典型场景概率

Table 1 Typical annual wind-PV-load scenario probability

表2 风光荷季典型场景概率

Table 2 Typical seasonal wind-PV-load scenario probability

图10 CHI计算指标结果

Fig.10 The computation result of CHI index

图11 DBI计算指标结果

Fig.11 The computation result of DBI index

图12 自编码聚类的 CHI计算指标结果

Fig.12 CHI calculation index result of auto-encoded clustering

图13 自编码聚类的DBI计算指标结果

Fig.13 DBI calculation index result of auto-encoded clustering

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