The increasing penetration of stochastic renewable distributed generation, energy storage systems and novel loads (e.g. electric-vehicles (EVs)) in active-distribution-networks (ADNs) or microgrids has triggered the need to develop real-time (e.g. minutes to sub-second) control frameworks to avoid grid-operational problems. These can be split into two main categories of active constraints: static and power-quality. Static constraints refer to branches' ampacity limitations, nodal voltage magnitudes' security-bounds as well as resources' limitations (e.g. MV-LV substation transformer apparent power limitations, power converters' capability curves and general constraints of the internal states of energy storage systems). Power-quality constraints refer to the quality-of-service for the end-users that must be guaranteed by the power distribution utility. Within this context, this thesis focuses on the development of real-time ADN controls, in the form of frameworks or control-enabling methodologies, that take into account the above-mentioned power-grid operational constraints while considering grid uncertainties and unbalances. In its first part, the thesis focuses on a general, i.e. resource-agnostic, methodology to linearize the power-flow equations and showcases its real-time control-enabling advantages through two sub-second-scale control-application-examples. Then, in the second part, a deeper focus is given to the operational challenges raised by the large presence of electric-vehicles charging-stations in distribution grids.