PROJECT TITLE :

Distributed Stochastic Reserve Scheduling in AC Power Systems With Uncertain Generation

ABSTRACT:

Distributed consensus and the alternating direction method of multipliers are used to implement multi-area stochastic reserve scheduling (RS) using an ac optimum power flow (OPF) model with a large penetration of wind power (ADMM). The OPF-RS issue is first formulated using semidefinite programming (SDP) in infinite-dimensional spaces, which is computationally intractable in most cases. A novel affine strategy is used to produce an approximation of the infinite-dimensional SDP as a tractable finite-dimensional SDP, and the approximation is clearly quantified. These new advancements in randomised optimization can be used to best schedule power generating units while also identifying the geographic distribution of the required reserve. A consensus ADMM algorithm is then used to identify a plausible solution for both the local and the overall power network, which is based on the power system's geographical pattern. We developed a distributed stochastic architecture that allows each area to use its own wind information to get local feasibility certifications, while ensuring the overall feasibility of the multi-area power network under moderate conditions With Monte Carlo simulations for two IEEE case studies, we compare a new benchmark formulation, the so-called converted dc (CDC) power flow model (PFC).