  The increase in renewable energy sources (RESs), like wind or solar power,
results in growing uncertainty also in transmission grids. This affects grid
stability through fluctuating energy supply and an increased probability of
overloaded lines. One key strategy to cope with this uncertainty is the use of
distributed energy storage systems (ESSs). In order to securely operate power
systems containing renewables and use storage, optimization models are needed
that both handle uncertainty and apply ESSs. This paper introduces a compact
dynamic stochastic chance-constrained optimal power flow (CC-OPF) model, that
minimizes generation costs and includes distributed ESSs. Assuming Gaussian
uncertainty, we use affine policies to obtain a tractable, analytically exact
reformulation as a second-order cone problem (SOCP). We test the new model on
five different IEEE networks with varying sizes of 5, 39, 57, 118 and 300 nodes
and include complexity analysis. The results show that the model is
computationally efficient and robust with respect to constraint violation risk.
The distributed energy storage system leads to more stable operation with
flattened generation profiles. Storage absorbed RES uncertainty, and reduced
generation cost.
