  With the escalating frequency of extreme grid disturbances, such as natural
disasters, comes an increasing need for efficient recovery plans. Algorithms
for optimal power restoration play an important role in developing such plans,
but also give rise to challenging mixed-integer nonlinear optimization
problems, where tractable solution methods are not yet available. To assist in
research on such solution methods, this work proposes PowerModelsRestoration, a
flexible, open-source software framework for rapidly designing and testing
power restoration algorithms. PowerModelsRestoration constructs a mathematical
modeling layer for formalizing core restoration tasks that can be combined to
develop complex workflows and high performance heuristics. The efficacy of the
proposed framework is demonstrated by proof-of-concept studies on three
established cases from the literature, focusing on single-phase positive
sequence network models. The results demonstrate that PowerModelsRestoration
reproduces the established literature, and for the first time provide an
analysis of restoration with nonlinear power flow models, which have not been
previously considered.
