Financial risk reduction in photovoltaic projects through ocean-atmospheric oscillations modeling

- First to reduce financial risk on a photovoltaic plant by climate information.
- Financial Risk was reduced 60-81% in Chile compared to traditional methodology.
- 10 areas of Chile were studied, Latin America's largest & fastest growing market.
- New profit model was developed for PV investment and risk assessment.
- Model for 3 ocean-atmospheric oscillations: ENSO (El Niño), SAM & IOD.

The impact of climate change on society has increased the interest to deploy renewable energies and to understand climate. Climate variability is partly predictable and is a fundamental factor in explaining financial risk in renewable energy projects. Current methodologies used for risk assessment do not appropriately account for climate predictability. We found limited literature on risk reduction on PV projects through the modeling of predictable components of solar radiation and ocean-atmospheric oscillations, allowing us to present original proposals to fill these voids. A new profit model for PV plants was developed, capturing this predictable climate information. The proposed methodology is potentially applicable to hydro, wind and other renewable resources, and allows leaving aside predictable climate components from the project's risk calculation. The model was tailored for the risk assessment of PV investments and is applied over 10 geographical areas across Chile, the largest PV market in Latin America, where climate is strongly affected by 3 ocean-atmospheric oscillations (El Niño Southern Oscillation, Southern Annular Mode, and Indian Ocean Dipole). Using the model in these regions allows reducing the monthly financial the risk to reduce by 60-81% compared to traditional methodology. For a 100 MW PV project located in those areas, this means reducing annualized risk from 4.93 to 7.88 MM $USD/year (traditional model) to 1.11-2.38 MM USD/year (proposed model). Modeling of ocean-atmospheric oscillations allows achieving the greatest risk reduction between the cities of Copiapó and Coquimbo (north-central regions), decreasing their influence towards the extreme latitudes. Their risk reduction will depend on the quality of the model, and may have strong implications for both investors and financial institutions. It could also impact competition in the energy sector due to possible asymmetries of information. To facilitate extending the use of the model elsewhere, the incorporation of subsidies is discussed.

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