## 1 Introduction  
The conventional Volt/Var control aims to find appropriate coordination between the on-load tap changer (OLTC) and all of the switched shunt capacitors (Sh.Cs) in the distribution networks. The main goal of a VVC system is to achieve an optimum voltage profile over the distribution feeders and optimum reactive power flows in the system [1; 2; 3]. Recently, because of the integration of various types of Distributed Generations (DGs), some challenging issues have emerged in distribution system operation. Generally, DGs are expected to increase the number of switching operations of conventional voltage control devices, such as OLTCs and Sh.Cs [4]. At present, inverters coupled with DG units can provide both active and reactive power based on the DSO request. Hence, by growing DG penetration into distribution systems, DG units could incorporate daily VVC. Nowadays, all studies of VVC can be classified into two main frameworks: _centralized offline control_ and _real-time control_. Studies on centralized offline control aim to determine dispatching schedules of VVC devices according to the day-ahead load forecast. In these studies, various objective functions such as total energy cost offered by generation units, electrical energy losses, voltage deviations, and total emission of generation units have been adopted as the methodologies for managing VVC. In [5] an Ant Colony Optimization (ACO) algorithm has been adopted to optimize the total cost of electrical energy generated by Distribution Companies (Discos) and DGs in the daily VVC problem. In [6] a fuzzy price-based compensation methodology has been proposed to solve the daily VVC problem in distribution systems in the presence of DGs. In [7] a new optimization algorithm based on a Chaotic Improved Honey Bee Mating Optimization (CIHBMO) has been implemented to determine control variables including the active and reactive power of DG units, reactive power values of capacitors, and tap positions of transformers for the next day. Also, in [8] a Fuzzy Adaptive Chaotic Particle Swarm Optimization (FACPSO) has been introduced to solve the multi-objective optimal operation management of distribution networks including fuel-cell power plants. In [9], minimization of active power losses and micro-generation shedding have been suggested as a methodology for optimized and coordinated voltage support in distribution networks with large integration of DGs and micro-grids. In [10], an analytic hierarchy process (AHP) strategy and Binary Ant Colony Optimization (BACO) algorithm have been employed to solve the multi-objective daily VVC in distribution systems. In [11] a multi-objective \(\theta\)-Smart Bacterial Foraging Algorithm (M0-SBFA) has been used for daily VVC, considering environmental and economic aspects as well as technical issues of distribution networks. According to progress in the Wind Turbine (WT) technology, Discos have paid attention to WTs more than any other Renewable Energy Sources (RESs). The stochastic nature of the wind speed may cause a fluctuation of electrical power in the distribution systems. Thus, a probabilistic analysis of distribution systems is required to cope with all uncertainties caused by the wind speed variations and load fluctuations [12; 13]. The ability of doubly fed induction generator (DFIG)-based wind farms to deliver multiple reactive power objectives considering variable wind conditions is examined in [14]. On the other hand, studies on real-time control methods have helped to control the VVC equipment based on real-time and local measurements and experiences. The second framework of VVC requires a higher level of distribution system automation and more hardware and software supports [15]. This control methodology generally provides no coordination between devices, and is often limited to a unidirectional power flow. Furthermore, it is very difficult for a real-time controller to take intoaccount the overall load change as well as the constraints of the maximum allowable daily operating times of switchable equipment.