Direct-Current Power Flow Solvers and Energy Storage Sizing

In the modern power grid, the increasing penetration of intermittent energy sources like solar and wind into the comes with unsought challenges. With increasing smart grid directcurrent (DC) deployments in distribution feeders, microgrids, smart buildings, and highvoltage transmission, there is a need for better understanding the landscape of power flow (PF) solutions as well as for efficient PF solvers with performance guarantees. This thesis puts forth three approaches with complementary strengths towards coping with the PF task, consisting of solving a system on non-linear equations, in DC power systems. We consider a possibly meshed network hosting ZIP loads and constant-voltage/power generators. Uncertainty is another inevitable side-effect of a modern power grid with vast deployments of renewable generation. Since energy storage systems (ESS) can be employed to mitigate the effect of uncertainties, their energy and power ratings along with their charging control strategies become of vital importance for renewable energy producers. This thesis also deals with the task of sizing ESS under a model predictive control (MPC) operation for a single ESS used to smoothen out a random energy signal. To account for correlations in the energy signal and enable charging adjustments in response to real-time fluctuations, we adopt a linear charging policy, designed by minimizing the initial ESS investment plus the average operational cost. Since charging decisions become random, the energy and power limits are posed as chance constraints. The chance constraints are enforced in a distributionally robust fashion. The proposed scheme is contrasted to a charging policy under Gaussian uncertainties and a deterministic formulation.