PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore

PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore

Author Nobre, Andre M. Google Scholar
Severiano, Carlos A., Jr. Google Scholar
Karthik, Shravan Google Scholar
Kubis, Marek Google Scholar
Zhao, Lu Google Scholar
Martins, Fernando R. Autor UNIFESP Google Scholar
Pereira, Enio B. Google Scholar
Ruther, Ricardo Google Scholar
Reindl, Thomas Google Scholar
Abstract With the substantial growth of solar photovoltaic installations worldwide, forecasting irradiance becomes a critical step in providing a reliable integration of solar electricity into electric power grids. In Singapore, the number of PV installation has increased with a growth rate of 70% over the past 6 years. Within the next decade, solar power could represent up to 20% of the instant power generation. Challenges for PV grid integration in Singapore arise from the high variability in cloud movements and irradiance patterns due to the tropical climate. For a thorough analysis and modeling of the impact of an increasing share of variable PV power on the electric power system, it is indispensable (i) to have an accurate conversion model from irradiance to solar power generation, and (ii) to carry out irradiance forecasting on various time scales. In this work, we demonstrate how common assumptions and simplifications in PV power conversion methods negatively affect the output estimates of PV systems power in a tropical and densely-built environment such as in Singapore. In the second part, we propose and test a novel hybrid model for short-term irradiance forecasting for short-term intervals. The hybrid model outperforms the persistence forecast and other common statistical methods. (C) 2016 Elsevier Ltd. All rights reserved.
Keywords PV power conversion
Solar irradiance forecasting
Short-term prediction
PV systems
Tropical regions
xmlui.dri2xhtml.METS-1.0.item-coverage Oxford
Language English
Sponsor National University of Singapore (NUS)
Singapore's National Research Foundation (NRF) through the Singapore Economic Development Board (EDB)
NRF
Tractebel Energia under the ANEEL RD program
Brazilian Scientific Research Council (CNPq)
Grant number NRF: NRF-CRP9-2011-06
Date 2016
Published in Renewable Energy. Oxford, v. 94, p. 496-509, 2016.
ISSN 0960-1481 (Sherpa/Romeo, impact factor)
Publisher Pergamon-Elsevier Science Ltd
Extent 496-509
Origin http://dx.doi.org/10.1016/j.renene.2016.03.075
Access rights Closed access
Type Article
Web of Science ID WOS:000375816700044
URI https://repositorio.unifesp.br/handle/11600/57523

Show full item record




File

File Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Search


Browse

Statistics

My Account