This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal powerflow problems, by adding dependence structure (correlation) andsupport information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncer-tainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational results achieved and the computational time.