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Implications of applying solar industry best practice resource estimation on project financing

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  • Pacudan, Romeo

Abstract

Solar resource estimation risk is one of the main solar PV project risks that influences lender’s decision in providing financing and in determining the cost of capital. More recently, a number of measures have emerged to mitigate this risk. The study focuses on solar industry’s best practice energy resource estimation and assesses its financing implications to the 27MWp solar PV project study in Brunei Darussalam. The best practice in resource estimation uses multiple data sources through the measure-correlate-predict (MCP) technique as compared with the standard practice that rely solely on modelled data source. The best practice case generates resource data with lower uncertainty and yields superior high-confidence energy production estimate than the standard practice case. Using project financial parameters in Brunei Darussalam for project financing and adopting the international debt-service coverage ratio (DSCR) benchmark rates, the best practice case yields DSCRs that surpass the target rates while those of standard practice case stay below the reference rates. The best practice case could also accommodate higher debt share and have lower levelized cost of electricity (LCOE) while the standard practice case would require a lower debt share but having a higher LCOE.

Suggested Citation

  • Pacudan, Romeo, 2016. "Implications of applying solar industry best practice resource estimation on project financing," Energy Policy, Elsevier, vol. 95(C), pages 489-497.
    • Handle: RePEc:eee:enepol:v:95:y:2016:i:c:p:489-497
    DOI: 10.1016/j.enpol.2016.02.021
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    Citations

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    Cited by:

    1. Pacudan, Romeo, 2018. "Feed-in tariff vs incentivized self-consumption: Options for residential solar PV policy in Brunei Darussalam," Renewable Energy, Elsevier, vol. 122(C), pages 362-374.
    2. Shih‐Chieh Liao & Shih‐Chieh Chang & Tsung‐Chi Cheng, 2022. "Index‐based renewable energy insurance for Taiwan Solar Photovoltaic Power Plants," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 25(2), pages 145-172, June.
    3. Pradhan, Priyabrata & Gadkari, Prabodh & Mahajani, Sanjay M. & Arora, Amit, 2019. "A conceptual framework and techno-economic analysis of a pelletization-gasification based bioenergy system," Applied Energy, Elsevier, vol. 249(C), pages 1-13.
    4. Stetter, Chris & Piel, Jan-Hendrik & Hamann, Julian F.H. & Breitner, Michael H., 2020. "Competitive and risk-adequate auction bids for onshore wind projects in Germany," Energy Economics, Elsevier, vol. 90(C).
    5. Bossink, Bart A.G., 2017. "Demonstrating sustainable energy: A review based model of sustainable energy demonstration projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1349-1362.
    6. Firouzi, Afshin & Meshkani, Ali, 2021. "Risk-based optimization of the debt service schedule in renewable energy project finance," Utilities Policy, Elsevier, vol. 70(C).
    7. Feldman, David & Jones-Albertus, Rebecca & Margolis, Robert, 2020. "Quantifying the impact of R&D on PV project financing costs," Energy Policy, Elsevier, vol. 142(C).
    8. Pengran Zhou & Pengfei Zhou & Serhat Yüksel & Hasan Dinçer & Gülsüm Sena Uluer, 2019. "Balanced Scorecard-Based Evaluation of Sustainable Energy Investment Projects with IT2 Fuzzy Hybrid Decision Making Approach," Energies, MDPI, vol. 13(1), pages 1-20, December.
    9. Pacudan, Romeo & Hamdan, Mahani, 2019. "Electricity tariff reforms, welfare impacts, and energy poverty implications," Energy Policy, Elsevier, vol. 132(C), pages 332-343.

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