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This paper presents a fully distributed algorithm for the stochastic Volt/VAr control (VVC) problem in active distribution networks. Exact convexification of the VVC problem is achieved through the use of second-order cones on the continuous relaxation of the original optimization structure. The global optimum solution of the relaxed problem is obtained through the application of the alternating direction method of multipliers (ADMM). In order to minimize the effect of rounding off on the final solution, an adaptive threshold discretization technique is used. A two-stage control strategy is adopted where the discrete controllers, like load tap changers and switched capacitors, are dispatched at the beginning of the optimization interval and the continuous controllers, like distributed generation (DG) inverters, are adjusted in real time according to an optimized decision rule. The superiority of the proposed algorithm is demonstrated through numerical simulations on the UKGDS-95 and the IEEE-123 bus systems. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage three-phase solar photovoltaic (PV) system, which is controlled through a novel enhanced second order generalized integrator (ESOGI) based control technique. The proposed ESOGI is used for fundamental component extraction from nonlinear load current and distorted grid voltages. This integrator effectively and simultaneously manages to address the DC offset, inter-harmonic and integrator delay problems of the traditional SOGI. In addition, this control technique provides power factor correction, harmonic elimination, and load balancing functionalities. The ESOGI controller is used to generate reference grid currents for controlling the voltage source converter (VSC), interfacing the PV panel with the grid. Extensive experimental results on a developed prototype in the laboratory, depict that the total harmonic distortion (THD) of the grid injected currents and voltages are found well under IEEE-519 standard. [ABSTRACT FROM AUTHOR]

Volt/var control (VVC) is one of the primary functions of the distribution management system aiming at optimum operation of power distribution networks while respecting all of their operational and security constraints. However, the recent huge integration of highly stochastic distributed generation (DG) sources with the grid presents a significant challenge to the traditional VVC schemes, which assume the future to be perfectly known. This paper presents a two-stage chance constrained optimization scheme to handle these nodal power uncertainties and guarantees that the operational and security constraints are respected for almost all realizations of the uncertainty. The chance constrained model is solved by collecting enough randomly chosen samples from the probability spaces of the uncertain parameters so that the class of the problem, a mixed integer second-order cone program, is not elevated. The algorithm not only dispatches the optimum schedule for discrete controlling devices like transformers and shunt capacitors but also optimizes the predefined decision rules for reactive power control of DG sources, thus falling in line with the requirement laid down in the revised IEEE 1547 Standard. Numerical simulations on three different test systems show the superiority of the proposed algorithm over the traditional deterministic methods. [ABSTRACT FROM AUTHOR]