  Power grid partitioning is an important requirement for resilient
distribution grids. Since electricity production is progressively shifted to
the distribution side, dynamic identification of self-reliant grid subsets
becomes crucial for operation. This problem can be represented as a
modification to the well-known NP-hard Community Detection (CD) problem. We
formulate it as a Quadratic Unconstrained Binary Optimization (QUBO) problem
suitable for solving using quantum computation{\color{blue}, which is expected
to find better-quality partitions faster. The formulation aims to find
communities with maximal self-sufficiency and minimal power flowing between
them}. To assess quantum optimization for sizeable problems, we develop a
hierarchical divisive method that solves sub-problem QUBOs to perform grid
bisections. Furthermore, we propose a customization of the Louvain heuristic
that includes self-reliance. In the evaluation, we first demonstrate that this
problem examines exponential runtime scaling classically. Then, using different
IEEE power system test cases, we benchmark the solution quality for multiple
approaches: D-Wave's hybrid quantum-classical solvers, classical heuristics,
and a branch-and-bound solver. As a result, we observe that the hybrid solvers
provide very promising results, both with and without the divisive algorithm,
regarding solution quality achieved within a given time frame. Directly
utilizing D-Wave's Quantum Annealing (QA) hardware shows inferior partitioning.
