Solar energy production has enormous potential in southeastern Ontario

[kristopher]

Solar energy production has enormous potential in southeastern Ontario

2010-04-14
Joshua Pearce.

Solar power in southeastern Ontario has the potential to produce almost the same amount of power as all the nuclear reactors in the United States, according to two studies conducted by the Queen’s University Applied Sustainability Research Group.
These studies, led by Queen’s mechanical engineering professor Joshua Pearce, are the first to explore the region’s solar energy potential. Professor Pearce was surprised by how many gigawatts could be produced.

“We came up with enormous numbers and we were being conservative. There about 95 gigawatts of potential power just in southeastern Ontario – that shows there is massive potential,” says Professor Pearce, who specializes in solar photovoltaic materials and applied sustainability.

One study, accepted for publication in the journal Computers, Environment and Urban Systems, discovered that if choice roof tops in southeastern Ontario were covered with solar panels, they could produce five gigawatts, or about five per cent of all of Ontario’s energy. The study took into account roof orientation and shading.

“To put this in perspective, all the coal plants in all of Ontario produce just over six gigawatts. The sun doesn’t always shine, so if you couple solar power with other renewable energy sources such as wind, hydro and biomass, southeastern Ontario could easily cover its own energy needs,” Professor Pearce says.

A second study, published in May issue of the journal Solar Energy, looked at land in southeastern Ontario that could be used for solar farms. The study considered land with little economic value – barren, rocky, non-farmable areas near electrical grids – and concluded it has the potential to produce 90 gigawatts.

“Nuclear power for all of the United States is about 100 gigawatts. We can produce 90 on barren land with just solar in this tiny region, so we are not talking about small potatoes,” Professor Pearce says.

The professor conducted the studies to provide policy makers solid numbers on solar energy potential, as well as find possible solar farm locations for developers.

Also contributing to the studies were Queen’s civil engineering student Lindsay Wiginton and mechanical engineering student Ha Nguyen.

To read the study in Computers, Environment and Urban Systems, go to http://dx.doi.org/10.1016/j.compenvurbsys.2010.01.001 (There is a firewall)

"Quantifying rooftop solar photovoltaic potential for regional renewable energy policy"
L.K. Wiginton, H.T. Nguyen, J.M. Pearce *

Abstract

Solar photovoltaic (PV) technology has matured to become a technically viable large-scale source of sustainable energy. Understanding the rooftop PV potential is critical for utility planning, accommodating grid capacity, deploying financing schemes and formulating future adaptive energy policies. This paper demonstrates techniques to merge the capabilities of geographic information systems and object-specific image recognition to determine the available rooftop area for PV deployment in an example large-scale region in south eastern Ontario. A five-step procedure has been developed for estimating total rooftop PV potential which involves geographical division of the region; sampling using the Feature Analyst extraction software; extrapolation using roof area-population relationships; reduction for shading, other uses and orientation; and conversion to power and energy outputs. Limitations faced in terms of the capabilities of the software and determining the appropriate fraction of roof area available are discussed. Because this aspect of the analysis uses an integral approach, PV potential will not be georeferenced, but rather presented as an agglomerate value for use in regional policy making. A relationship across the region was found between total roof area and population of 70.0 m2/capita ± 6.2%. With appropriate roof tops covered with commercial solar cells, the potential PV peak power output from the region considered is 5.74 GW (157% of the region’s peak power demands) and the potential annual energy production is 6909 GWh (5% of Ontario’s total annual demand). This suggests that 30% of Ontario’s energy demand can be met with province-wide rooftop PV deployment. This new understanding of roof area distribution and potential PV outputs will guide energy policy formulation in Ontario and will inform future research in solar PV deployment and its geographical potential.





To read the Solar Energy study, go to http://dx.doi.org/10.1016/j.solener.2010.02.009

"Estimating potential photovoltaic yield with r.sun and the open source Geographical Resources Analysis Support System"

H.T. Nguyena and J.M. PearceCorresponding Author Contact Information, a, E-mail The Corresponding Author

a Department of Mechanical and Materials Engineering, Queen’s University, 60 Union Street, Kingston, Ontario, Canada K7L 3N6
Received 16 November 2009;
revised 11 February 2010;
accepted 23 February 2010.
Communicated by: Associate Editor Frank Vignola.
Available online 17 March 2010.

Abstract

The package r.sun within the open source Geographical Resources Analysis Support System (GRASS) can be used to compute insolation including temporal and spatial variation of albedo and solar photovoltaic yield. A complete algorithm is presented covering the steps of data acquisition and preprocessing to post-simulation whereby candidate lands for incoming solar farms projects are identified. The optimal resolution to acquire reliable solar energy outputs to be integrated into PV system design software was determined to be 1 square km. A case study using the algorithm developed here was performed on a North American region encompassing fourteen counties in South-eastern Ontario. It was confirmed for the case study that Ontario has a large potential for solar electricity. This region is found to possess over 935,000 acres appropriate for solar farm development, which could provide 90 GW of PV. This is nearly 60% of Ontario’s projected peak electricity demand in 2025. The algorithm developed and tested in this paper can be generalized to any region in the world in order to foster the most environmentally-responsible development of large-scale solar farms.