Financing Solar PV Projects: Energy Production Risk Reduction and Debt Capacity Improvement

Romeo Pacudan

Chapter 10 in Financing Renewable Energy Development in East Asia Summit Countries A Primer of Effective Policy Instruments, pp 297-320 from Economic Research Institute for ASEAN and East Asia (ERIA)

Abstract: Various risks influence the decision in obtaining financing and determining the cost of financing for utility-scale solar photovoltaic (PV) projects in many developing countries. One of the risk areas is in the estimation of solar PV energy production, which is significantly derived from the uncertainty in solar resource data and measurement. Due to the lack of ground-measured data sets, the solar PV industry mainly relies on satellite-derived irradiation data to estimate on-site solar energy resource, but modelled data often lacked the accuracy to mitigate energy production risks. The use of multiple data sources has been increasingly employed and emerging to be the best practice in the solar industry. One of the methodologies that combine various sources of data is the measure-correlate-predict (MCP) approach, which correlates short-term measured data with long-term reference data sets. The study, using the proposed 27 megawatt peak (MWp) solar PV project in Brunei Darussalam, evaluates the impact of using correlated irradiation data sets on energy production and capital structuring of utility-scale solar PV projects. The study results confirm the outcome of other studies—that correlated solar irradiation data sets generate superior, high-confidence energy estimates (probability of exceedance at P90 and P99 levels) than those using satellite-derived data sets. With assumed financial parameters, the high-confidence energy estimates from MCP-derived data comfortably satisfy the debtservice coverage ratios (DSCRs) set by lending institutions and credit rating agencies, as well as generate lower levelised production cost of electricity. Also, the study shows that to achieve the minimum target DSCR of 1.3x and 1.2x for P90 and P99 energy production levels, the share of debt on the overall project capital structure could be further increased by around 7% for both cases from a reference debt share of 70%. The use of high-quality data sets therefore reduce project risks, increase project financial leverage, add enhance financial competitiveness. The government’s support measures that address the issue on resource data uncertainty and establishing best practice in data measurement and use in project analysis would be crucial in developing solar PV industry in developing countries.

Keywords: Solar irradiation data sets; measure-correlate-predict; probability of exceedance; capital structuring (search for similar items in EconPapers)
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