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Solar PV electricity and market characteristics: two Canadian case-studies

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  • Rowlands, Ian H.

Abstract

To determine whether solar electricity (that is, electricity generated by photovoltaics) is, on an average, more valuable—in market terms—than the electricity generated in power systems as a whole, this article investigates the extent to which solar resource availability in two Canadian locations is associated with peak electricity market demand and peak electricity market price. More specifically, solar radiation and electricity market data for the period 1 May 2002 to 30 April 2004 are examined for Calgary, Alta. and Guelph, Ont. A variety of visual and statistical investigations reveal that solar radiation values coincide closely with peak electricity market demand and, though to a somewhat lesser extent, peak electricity market prices during the summertime in each location. While more detailed investigation is needed in order to determine the specific impact of different levels of PV penetration upon provincial electricity markets, the article provides ample encouragement for further research. The article also shows how different techniques can be used—in any location—to investigate the relationship among solar electricity potential, system-wide demand and market prices. With electricity industries being restructured around the world, it continues to be important for solar energy proponents to participate in discussions regarding economic costs and benefits. Techniques used in this article can help them advance the solar electricity case more effectively and thus catalyse the deployment of photovoltaics in markets around the world.

Suggested Citation

  • Rowlands, Ian H., 2005. "Solar PV electricity and market characteristics: two Canadian case-studies," Renewable Energy, Elsevier, vol. 30(6), pages 815-834.
    • Handle: RePEc:eee:renene:v:30:y:2005:i:6:p:815-834
    DOI: 10.1016/j.renene.2004.08.001
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    Cited by:

    1. Lee, Donghun & Kim, Kwanho, 2021. "PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information," Renewable Energy, Elsevier, vol. 173(C), pages 1098-1110.
    2. Richardson, David B. & Harvey, L.D.D., 2015. "Strategies for correlating solar PV array production with electricity demand," Renewable Energy, Elsevier, vol. 76(C), pages 432-440.
    3. Rezaei, M. & Anisur, M.R. & Mahfuz, M.H. & Kibria, M.A. & Saidur, R. & Metselaar, I.H.S.C., 2013. "Performance and cost analysis of phase change materials with different melting temperatures in heating systems," Energy, Elsevier, vol. 53(C), pages 173-178.
    4. Tveten, Åsa Grytli & Bolkesjø, Torjus Folsland & Martinsen, Thomas & Hvarnes, Håvard, 2013. "Solar feed-in tariffs and the merit order effect: A study of the German electricity market," Energy Policy, Elsevier, vol. 61(C), pages 761-770.
    5. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    6. Rowlands, Ian H. & Kemery, Briana Paige & Beausoleil-Morrison, Ian, 2011. "Optimal solar-PV tilt angle and azimuth: An Ontario (Canada) case-study," Energy Policy, Elsevier, vol. 39(3), pages 1397-1409, March.
    7. Mills, Andrew & Wiser, Ryan & Barbose, Galen & Golove, William, 2008. "The impact of retail rate structures on the economics of commercial photovoltaic systems in California," Energy Policy, Elsevier, vol. 36(9), pages 3266-3277, September.

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