Assessing a regional building applied PV potential – Spatial and dynamic analysis of supply and load matching
Andreas Molin,
Simon Schneider,
Patrik Rohdin and
Bahram Moshfegh
Renewable Energy, 2016, vol. 91, issue C, 261-274
Abstract:
Electricity production by PV is growing world-wide, and grid parity of PV-electricity can be found in many countries, even in low sunlight countries, such as Sweden (at latitude 58°). High installation-rate of PV-systems poses a challenge to the grid-operator. Building-integrated PV-supply potential analysis was performed for Linköping municipality in Sweden based on GIS-data for all the buildings in the municipality. The Linköping model provides a high spatial resolution (>180 000 buildings). The data are sorted based on azimuth and tilt, categorized in steps of 10°, and then used to construct hourly power supply data. The supply data are fed into the existing electricity load-profile of Linköping municipality. The strength and novelty of the method is that it provides the possibility of varying the installation-rate in different spatial directions to better match the load-profile.
Keywords: PV system; Dynamic load matching; Load profile; Self-consumption; Spatial-time distribution; Regional PV potential (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148116300842
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:91:y:2016:i:c:p:261-274
DOI: 10.1016/j.renene.2016.01.084
Access Statistics for this article
Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides
More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().