Building America Case Study: Photovoltaic Systems with Module-Level Power Electronics
Abstract
Direct current (DC) power optimizers and microinverters (together known as module-level power electronics, or MLPE) are one of the fastest growing market segments in the solar industry. According to GTM Research in The Global PV Inverter Landscape 2015, over 55% of all residential photovoltaic (PV) installations in the United States used some form of MLPE in 2014.
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
- OSTI Identifier:
- 1225312
- Report Number(s):
- NREL/FS-5500-64876; DOE/GO-102015-4755
- DOE Contract Number:
- AC36-08GO28308
- Resource Type:
- Program Document
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 14 SOLAR ENERGY; 24 POWER TRANSMISSION AND DISTRIBUTION; Photovoltaic; Power electronics; Microinverter; DC power optimizer
Citation Formats
. Building America Case Study: Photovoltaic Systems with Module-Level Power Electronics. United States: N. p., 2015.
Web.
. Building America Case Study: Photovoltaic Systems with Module-Level Power Electronics. United States.
. 2015.
"Building America Case Study: Photovoltaic Systems with Module-Level Power Electronics". United States. https://www.osti.gov/servlets/purl/1225312.
@article{osti_1225312,
title = {Building America Case Study: Photovoltaic Systems with Module-Level Power Electronics},
author = {},
abstractNote = {Direct current (DC) power optimizers and microinverters (together known as module-level power electronics, or MLPE) are one of the fastest growing market segments in the solar industry. According to GTM Research in The Global PV Inverter Landscape 2015, over 55% of all residential photovoltaic (PV) installations in the United States used some form of MLPE in 2014.},
doi = {},
url = {https://www.osti.gov/biblio/1225312},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Sep 01 00:00:00 EDT 2015},
month = {Tue Sep 01 00:00:00 EDT 2015}
}
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