Robust Optimization in the Scheduling of Concentrated Solar Power Plants

The scheduling of renewable energy systems, such as in Concentrated Solar Power (CSP) plants, proves to be a difficult task with active research and innovation in progress. The inherent uncertainty of some model parameters that are used to generate the schedule may result in less than favorable objective function values and sometimes infeasibility when the actual realization of such parameter values are different from the ones used in the model. Robust optimization techniques can be used to make scheduling decisions while accounting for uncertainty. In this work we propose a methodology for generating the optimal schedule of a CSP plant under uncertainty. Starting from a given forecast of the uncertain parameters (such as heat from the sun, or the price at which power is sold), we artificially generate a range of realization scenarios by using a deviation array, which then act as input to a CSP scheduling model in order to obtain a recommendation of the power sold. The forecasts/scenarios are defined by probability distributions. These recommendations are then input into another CSP scheduling model in which the forecasts act as realizations. The result is a scenario matrix used to make scheduling decisions by calculating the expected benefit of choosing a certain schedule. We illustrate this framework in a particular CSP plant. For this case study our results indicate that, for uncertain heat from the sun, the most beneficial schedule is based upon a medium sun heat forecast, while our second case where the pricing of power sold is uncertain, the most beneficial schedule is a low pricing forecast. This method can be extended to real forecast sets over procedurally generated ones, providing a schedule without much computational effort. We suggest that future work explores broader forecast probability distributions as well as introducing the possibility of infeasible scenarios.Chemical Engineerin