Extreme Photovoltaic Power Analytics for Electric Utilities

被引：7

作者：

Tang, Zefan ^{[1
]}

Zhang, Peng ^{[1
]}

Muto, Kunihiro ^{[1
]}

Sawasawa, Martial ^{[1
]}

Simonelli, Marissa ^{[1
]}

Gutierrez, Christopher ^{[1
]}

Yang, Jaemo ^{[2
]}

Astitha, Marina ^{[2
]}

Ferrante, David A. ^{[3
]}

Debs, Joseph N. ^{[3
]}

Manning, Robert ^{[4
]}

Mader, James ^{[4
]}

机构：

[1] Univ Connecticut, Dept Elect & Comp Engn, Storrs, CT 06269 USA

[2] Univ Connecticut, Dept Civil & Environm Engn, Storrs, CT 06269 USA

[3] Eversource Energy, Berlin, CT 06037 USA

[4] United Illuminating Co, Orange, CT 06477 USA

来源：

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY | 2020年 / 11卷 / 01期

基金：

美国国家科学基金会;

关键词：

Extreme photovoltaic power analytics; electric utility; k-means clustering; extreme value analysis; GENERALIZED PARETO DISTRIBUTION; REGIONAL FREQUENCY-ANALYSIS; RIVER-BASIN; PV POWER; STORAGE; DISTRIBUTIONS; GENERATION; EFFICIENCY; POLICIES; IMPACTS;

D O I：

10.1109/TSTE.2018.2884500

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Obtaining high-fidelity information on extreme photovoltaic (PV) power is critical for electric utility system planning and operations. However, a scarcity of extreme data has previously made achieving an accurate estimate of extreme PV power an intractable challenge. In response to this challenge, this paper presents Extreme PV Power Analytics (EPVA). It utilizes k-means clustering to determine which PV systems have similar behaviors in their extreme capacity factors (ECFs) in order to incorporate more extreme data in an extreme value analysis. This extreme value analysis is subsequently applied to obtain the distribution of ECFs. Zone partitioning results and ECF distribution results for The United Illuminating Company service territory are presented to validate the effectiveness and efficacy of EPVA.

引用

页码：93 / 106

页数：14

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