Energy usage optimization algorithms for cost-effective and resource-saving production are already widespread in industry. However, many optimization models disregard the uncertainty in the prediction of electricity and heat demand or in the forecast of solar energy feed-in. This paper describes an experimental optimization process for a cross-sectorial energy system with the task of taking uncertainties into account during the optimization in order to plan energy consumption from the grid. To accomplish this objective a new approach for scenario generation based on historical measurements and predictions is presented and explained in detail. At first, the optimization problem for the energy system is implemented as a deterministic problem which neglects uncertainties. Secondly, this problem is extended to a stochastic optimization problem using the aforementioned scenario generation and is solved in the extensive form. Both types of optimization problems are solved over a period of five months in a rolling process. The comparison of the experiments show that stochastic optimization is a good alternative to optimize with respect to uncertainties but comes with higher costs and longer computation time.
Schreiber et al. (Sun,) studied this question.