Optimization of Renewable Energy Businesses under Operational Level Uncertainties through Extensive Sensitivity Analysis and Stochastic Global Optimization

This work presents a decision-making framework for stochastic optimization of renewable energy businesses considering technological uncertainties and risk management. With the help of a global sensitivity analysis based on Sobol's method, significant uncertain parameters from biochemical reactions are identified. Moreover, two options for selecting sensitive parameters are explored: (1) when all parameters are simultaneously evaluated, and (2) when parameters are separately evaluated based on their kinetic pathway. For both cases, the renewable energy facility is simulated considering uncertainty. To maximize the profitability of the business, a metaheuristic stochastic global optimization technique is incorporated to the framework. This method uses a radial basis function which approximates a computationally expensive objective function and permits intelligent selection of the best operating conditions. To evaluate the efficacy of this framework, a hypothetical multiproduct lignocellulosic biorefinery modeled under operational uncertainty is optimized.