Risk-based optimization of the debt service schedule in renewable energy project finance

Project finance is used in capital intensive renewable energy projects worldwide. Financial entities such as large banks and institutional investors are active in providing syndicated loans for infrastructure projects and compete to offer better terms to the sponsors of these projects. The literature is full of research on capital structure optimization. We propose a novel stochastic framework for optimizing the debt service schedule with due regard to the probability of default of the project company. The applicability of the proposed method is demonstrated for a 10 MW solar photovoltaic project employing a genetic algorithm (GA) as the optimization tool. The NASA SSE dataset is used to collect irradiation data, and PVsyst software is employed to simulate the performance of the project. The uncertainties are accounted for using Monte Carlo simulation, and the revenue generated, its corresponding free cash flow and the debt service coverage ratio are simulated as random variables. The proposed optimization framework enables lenders to offer an optimized debt service that maximizes the shareholder's profitability index. A particle swarm optimization is also employed to validate the stability and usefulness of GA optimization.