Modern society is facing great challenges due to pollution and increased carbon dioxide (CO₂) emissions. As part of solving these challenges, the use of renewable energy sources and electric vehicles (EVs) is rapidly increasing. However, increased dynamics have triggered problems in balancing energy supply and consumption demand in the power systems. The resulting uncertainty and unpredictability of energy production, consumption, and management of peak loads has caused an increase in costs for energy market actors. Therefore, the means for studying the balancing of local smart grids with EVs is a starting point for this paper. The main contribution is a simulation-based approach which was developed to enable the study of the balancing of local distribution grids with EV batteries in a cost-efficient manner. The simulation-based approach is applied to enable the execution of a distributed system with the simulation of a local distribution grid, including a number of charging stations and EVs. A simulation system has been constructed to support the simulation-based approach. The evaluation has been carried out by executing the scenario related to balancing local distribution grids with EV batteries in a step-by-step manner. The evaluation results indicate that the simulation-based approach is able to facilitate the evaluation of smart grid– and EV-related communication protocols, control algorithms for charging, and functionalities of local distribution grids as part of a complex, critical cyber-physical system. In addition, the simulation system is able to incorporate advanced methods for monitoring, controlling, tracking, and modeling behavior. The simulation model of the local distribution grid can be executed with the smart control of charging and discharging powers of the EVs according to the load situation in the local distribution grid. The resulting simulation system can be applied to the study of balancing local smart grids with EV batteries. Based on the evaluation results, it is estimated that the simulation-based approach can provide an essential, safe, and cost-efficient method for the evaluation of complex, critical cyber-physical systems, such as smart grids.