###### Abstract
References [21; 22; 23] proposed methods to improve system frequency stability under disturbance and methods to maximize EV prosumer revenue are discussed in [24; 25; 26; 27]. However, meta-heuristic optimization algorithms with multi-objective functions have drawback with their weight factors, as they need to vary each of the weights properly to achieve optimal solutions in all objective functions. Otherwise, the solution is trapped in a local optimal solution. A huge power system with a load flow calculation requires the consideration of variable decisions with enormous and complex computational requirements which are difficult to search in terms of global optimal solution and grid requirements/customer satisfactions could not be met [28]. The authors of [29] reviewed several techniques for multi-stage optimization dealing with real-world problems, particularly in energy management. The partial decision making of multi-stage optimization can cause complexity in problems, but this model enables decision makers to alter decision at a later stage. The results of multi-stage models have potential to find better solution than all decisions at only one stage. Although many studies have proposed smart charging with different objectives, only a few studies have focused on the benefits to both the customer and power systems. Some of these works are listed in Table 1. Coordinated smart charging using V2G to minimize charging costs and battery degradation costs was studied in [30]. Reference [31] presented an approach to coordinate V2G by focusing specifically on battery health. Customer benefits and maintaining system voltage levels using V2G technology was studied in [32]. The authors of [33] proposed an approach to minimize charging costs and voltage deviations. Reference [34] presented multi-objective optimization, including different objectives, such as charging costs, voltage drops, and voltage unbalance. Reference [35] used V2G with a reactive droop control and Reference [36] used different phase connections (phase switcher) to minimize voltage unbalance and voltage deviations. The researchers of [37] scheduled the active/reactive power of V2G to regulate the voltage and minimize losses in a system. The authors of [38] used DGs to mitigate the voltage unbalance and improve voltage magnitude in connected EV buses. It can be observed from Table 1 that most of the literature only considered customer objectives [30; 31; 32] or system objectives [35; 36; 37; 38]; there are few works in the literature which considered both [33; 34]. The researchers of [33] did not consider the reactive power exchange of V2G nor battery degradation costs, which are crucial for discharging EVs. The authors of [34] included different objectives, such as charging costs and voltage deviation, but did not consider voltage unbalance, battery degradation, or the reactive power capability of EVs. Thus, this paper tries to address these research gaps and proposes a two-stage strategy where, in the first stage, different objectives, such as customer charging costs, battery degradation costs, and voltage unbalance, are considered, and, in second stage, the voltage deviation problem is considered. Thus, this paper tries to address the research gaps outlined in the paragraphs above. It proposes a two-stage strategy to mitigate different electrical issues, such as VUF, voltage deviation, and prosumer costs, using V2G technology. The two-stage problem is formulated as an optimization problem and is solved using a meta-heuristic optimization algorithm, particle swarm optimization (PSO). The first stage targets the problem of VUF and customer costs by scheduling EV charging and the second stage targets voltage deviation with the help of reactive power injection from EVs. Thus, the major contributions of this paper are as follows:  
1. The development of a framework to fulfill grid requirements, such as voltage unbalance factors, voltage deviations, and prosumer requirements, such as charging cost and battery health, using V2G technology. 2.