  In light of a reliable and resilient power system under extreme weather and
natural disasters, networked microgrids integrating local renewable resources
have been adopted extensively to supply demands when the main utility
experiences blackouts. However, the stochastic nature of renewables and
unpredictable contingencies are difficult to address with the deterministic
energy management framework. The paper proposes a comprehensive
distributionally robust joint chance-constrained (DR-JCC) framework that
incorporates microgrid island, power flow, distributed batteries and voltage
control constraints. All chance constraints are solved jointly and each one is
assigned to an optimized violation rate. To highlight, the JCC problem with the
optimized violation rates has been recognized to be NP-hard and challenging to
be solved. This paper proposes a novel evolutionary algorithm that successfully
tackles the problem and reduces the solution conservativeness (i.e. operation
cost) by around 50% comparing with the baseline Bonferroni Approximation.
Considering the imperfect solar power forecast, we construct three data-driven
ambiguity sets to model uncertain forecast error distributions. The solution is
thus robust for any distribution in sets with the shared moment and shape
assumptions. The proposed method is validated by robustness tests based on
those sets and firmly secures the solution robustness.
