  Digital twin (DT) is one of the most promising enabling technologies for
realizing smart grids. Characterized by seamless and active---data-driven,
real-time, and closed-loop---integration between digital and physical spaces, a
DT is much more than a blueprint, simulation tool, or cyber-physical system
(CPS). Numerous state-of-the-art technologies such as internet of things (IoT),
5G, big data, and artificial intelligence (AI) serve as a basis for DT. DT for
power systems aims at situation awareness and virtual test to assist the
decision-making on power grid operation and management under normal or urgent
conditions. This paper, from both science paradigms and engineering practice,
outlines the backgrounds, challenges, framework, tools, and possible directions
of DT as a preliminary exploration. To our best knowledge, it is also the first
exploration on DT in the context of power systems. Starting from the
fundamental and most frequently used power flow (PF) analysis, some typical
application scenarios are presented. Our work is expected to contribute some
novel discoveries, as well as some high-dimensional analytics, to the
engineering community. Besides, the connection of DT with big data analytics
and AI may has deep impact on data science.
