Capacity Expansion Planning with Stochastic Rolling Horizon Dispatch.

• Day-ahead and balancing markets are modelled in stochastic rolling horizon dispatch • Stochastic rolling horizon dispatch is integrated with a capacity expansion problem • Benders cuts are formulated from a stochastic rolling horizon dispatch model • The impact of short-term uncertainty on investments are investigated Variable renewable energy sources introduce significant amounts of short-term uncertainty that should be considered when making investment decisions. In this work, we present a method for representing stochastic power system operation in day-ahead and balancing electricity markets within a capacity expansion model. We use Benders cuts and a stochastic rolling horizon dispatch to represent operational costs in the capacity expansion problem (CEP) and investigate different formulations for the cuts. We test the model on a two-bus case study with wind power, energy storage and a constrained transmission line. The case study shows that cuts created from the day-ahead problem gives the lowest expected total cost for the stochastic CEP. The stochastic CEP results in 3% lower expected total cost compared to the deterministic CEP capacities evaluated under uncertain operation. The number of required stochastic iterations is efficiently reduced by introducing a deterministic lower bound, while extending the horizon of the operational problem by persistence forecasting leads to reduced operational costs. [ABSTRACT FROM AUTHOR]