Combinatorial chance-constrained economic optimization of distributed energy resources

The transformation of the energy system towards sustainable energy sources is characterized by an increase in weather dependent distributed energy resources (DER). This adds a layer of uncertainty in energy generation on top of already uncertain load distribution. At the same time, many households are fitted with renewable generation units and storage systems. The increased intermittent generation in the distribution grid leads to new challenges for the commitment and economic dispatch of DER. The main challenge addressed in this work is to decide which available resources to select for a given task. To solve this, we introduce Stochastic Resource Optimization (SRO), a general purpose, combinatorial, chance-constrained optimization model for the short-term economic selection of stochastic DER. It incorporates correlations between stochastic resources are using copula theory. The contributions of this paper are twofold: First, we validate the applicability of the SRO formulation on a simplified congestion management use-case in a small neighbourhood grid comprised of prosumer households. Second, we provide an analysis of the performance of different solving algorithms for SRO problems and their run-times. Our results show that a fast metaheuristic algorithm can provide high quality solutions in acceptable time on the evaluated problem sets.