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Grid electricity for Fiji islands: Future supply options and assessment of demand trends

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  • Prasad, Ravita D.
  • Raturi, Atul

Abstract

Electricity is a secondary energy source and one of the main drivers for economic development of a nation. Long-term planning for electricity demand is essential for strategic expansion of supply options which would require significant investment in terms of human resources and capital. This paper is focused on the past trends in annual grid-electricity demand for Fiji, from which forecast is done using statistically significant linear regression models. The regression models reveal that domestic grid-electricity demand variance is explained by population, GDP and electricity price. However, for non-domestic demand, the variance is explained by changes in population and GDP with electricity tariffs playing a small role. The absolute deviation of forecasts for total demand from 5 different regression models ranges from 1.2 to 32%. For domestic demand it ranges from 3.0 to 5.0% while non-domestic deviation ranges from 1.7 to 19%. Analytic hierarchy process was employed to choose the best model for demand forecast which then led the discussion on future supply options for grid-electricity expansion in Fiji. Biomass power plants, hydro and GCPV are seen to be the most promising supply.

Suggested Citation

  • Prasad, Ravita D. & Raturi, Atul, 2017. "Grid electricity for Fiji islands: Future supply options and assessment of demand trends," Energy, Elsevier, vol. 119(C), pages 860-871.
  • Handle: RePEc:eee:energy:v:119:y:2017:i:c:p:860-871
    DOI: 10.1016/j.energy.2016.11.054
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    1. Sumer, Kutluk Kagan & Goktas, Ozlem & Hepsag, Aycan, 2009. "The application of seasonal latent variable in forecasting electricity demand as an alternative method," Energy Policy, Elsevier, vol. 37(4), pages 1317-1322, April.
    2. Wang, Yuanyuan & Wang, Jianzhou & Zhao, Ge & Dong, Yao, 2012. "Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China," Energy Policy, Elsevier, vol. 48(C), pages 284-294.
    3. Amarawickrama, Himanshu A. & Hunt, Lester C., 2008. "Electricity demand for Sri Lanka: A time series analysis," Energy, Elsevier, vol. 33(5), pages 724-739.
    4. Bueno, C. & Carta, J.A., 2006. "Wind powered pumped hydro storage systems, a means of increasing the penetration of renewable energy in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(4), pages 312-340, August.
    5. Dilaver, Zafer & Hunt, Lester C, 2011. "Modelling and forecasting Turkish residential electricity demand," Energy Policy, Elsevier, vol. 39(6), pages 3117-3127, June.
    6. Sharma, Kaushik & Ahmed, M. Rafiuddin, 2016. "Wind energy resource assessment for the Fiji Islands: Kadavu Island and Suva Peninsula," Renewable Energy, Elsevier, vol. 89(C), pages 168-180.
    7. Azadeh, A. & Saberi, M. & Seraj, O., 2010. "An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran," Energy, Elsevier, vol. 35(6), pages 2351-2366.
    8. Erdogdu, Erkan, 2007. "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, Elsevier, vol. 35(2), pages 1129-1146, February.
    9. Brown, Richard E. & Koomey, Jonathan G., 2003. "Electricity use in California: past trends and present usage patterns," Energy Policy, Elsevier, vol. 31(9), pages 849-864, July.
    10. Ilkan, M. & Erdil, E. & Egelioglu, F., 2005. "Renewable energy resources as an alternative to modify the load curve in Northern Cyprus," Energy, Elsevier, vol. 30(5), pages 555-572.
    11. Narayan, Paresh Kumar & Smyth, Russell, 2005. "The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration," Energy Policy, Elsevier, vol. 33(4), pages 467-474, March.
    12. Kumar, Ajal & Prasad, Shivneel, 2010. "Examining wind quality and wind power prospects on Fiji Islands," Renewable Energy, Elsevier, vol. 35(2), pages 536-540.
    13. Reuter, Wolf Heinrich & Fuss, Sabine & Szolgayová, Jana & Obersteiner, Michael, 2012. "Investment in wind power and pumped storage in a real options model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2242-2248.
    14. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    15. Rallapalli, Srinivasa Rao & Ghosh, Sajal, 2012. "Forecasting monthly peak demand of electricity in India—A critique," Energy Policy, Elsevier, vol. 45(C), pages 516-520.
    16. Bianco, Vincenzo & Manca, Oronzio & Nardini, Sergio, 2009. "Electricity consumption forecasting in Italy using linear regression models," Energy, Elsevier, vol. 34(9), pages 1413-1421.
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    2. Aneesh A. Chand & Kushal A. Prasad & Kabir A. Mamun & Krishneel R. Sharma & Kritish K. Chand, 2019. "Adoption of Grid-Tie Solar System at Residential Scale," Clean Technol., MDPI, vol. 1(1), pages 1-8, August.

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