## 1 Introduction
### State of the Art  
Recently, the concept of the VVC management has been explored from the technical, economic, and environmental perspectives, using either single or multi-objective optimization techniques. [PERSON] _et al._[22] have introduced a multi-objective \(\theta\)-Smart Bacterial Foraging Algorithm for daily VVC problems of distribution systems in the presence of renewable energy resources and considering environmental and economic aspects. A fair remuneration for the reactive power offered by a dispatchable DG was developed in [23]. Then, a cost-based approach has been proposed for VVC in distribution networks based on the costs of reactive power provided by the main grid, DGs, and capacitor banks [23]. A competitive market integration model for DGs located at the distribution level in a pool-based wholesale market has been proposed in [24]. [PERSON] _et al._[25] have adopted combined particle swarm optimization and a market-based optimal power flow for optimal allocation of wind turbines in a distribution market environment. A multi-period energy procurement model for a distribution company (Disco) including DGs and interruptible loads in a day-ahead electricity market has been proposed in [26]. [PERSON] _et al._ have presented a probabilistic method to assess the impact of wind turbine integration into distribution networks based on a market environment [27]. In the near future, due to high integration of resources into distribution systems, it will be necessary to schedule both active and reactive power at a distribution level. [PERSON] _et al._[28] have proposed a new multi-objective methodology to manage the optimally-coupled active and reactive power scheduling in a smart distribution system in the presence of the DGs, electric vehicles, and capacitor banks. [PERSON] _et al._[29] have presented a new economical/environmental operational scheduling method based on decoupled day-ahead active and reactive power markets at the distribution level. In the context of the smart grid, four power-demand scheduling scenarios are presented and analyzed in [30] to reduce the peak demand in a smart grid environment. [PERSON] _et al._[31] have provided a comprehensive review of different demand response (DR) schemes based on the incentives offered to the consumers in order to participate in the DR program.  
So far, many studies have focused on the reactive power market and management in power systems. A reactive power market clearing method according to a multi-objective optimization has been presented in [32]. To alleviate market power, a zonal reactive power market has been proposed in [33]. According to the zonal reactive power market, [PERSON] _et al._[34] have presented a day-ahead reactive power market clearing mechanism based on a multi-objective optimization model. The management of the generator reactive power reserve is adopted as a preventive action for ensuring the voltage stability of the power system [35]. In [36], a coupled active and reactive power market considering joint active and reactive reserve market has been cleared to minimize the costs of simultaneous active and reactive power production, and the costs of energy not supplied. [PERSON] _et al._[37] have implemented a particle swarm optimization algorithm combined with a feasible solutions search for optimizing the reactive power dispatch in wind farms.