1 research outputs found
Risk-Based Distributionally Robust Optimal Power Flow With Dynamic Line Rating
In this paper, we propose a risk-based data-driven approach to optimal power
flow (DROPF) with dynamic line rating. The risk terms, including penalties for
load shedding, wind generation curtailment and line overload, are embedded into
the objective function. To hedge against the uncertainties on wind generation
data and line rating data, we consider a distributionally robust approach. The
ambiguity set is based on second-order moment and Wasserstein distance, which
captures the correlations between wind generation outputs and line ratings, and
is robust to data perturbation. We show that the proposed DROPF model can be
reformulated as a conic program. Considering the relatively large number of
constraints involved, an approximation of the proposed DROPF model is
suggested, which significantly reduces the computational costs. A Wasserstein
distance constrained DROPF and its tractable reformulation are also provided
for practical large-scale test systems. Simulation results on the 5-bus, the
IEEE 118-bus and the Polish 2736-bus test systems validate the effectiveness of
the proposed models