This research explores the feasibility of building a large-scale setpoint tracking controller for the co-regulation of Electric Vehicle (EV) charging stations, aiming to coordinate charging with energy market dynamics and minimize the error between a power setpoint and the aggregated consumption of charging stations while capitalizing on developments in the imbalance market. The study examines the roles of actors in the energy market, the characteristics of the EV charging infrastructure, and the information provided by TenneT regarding the imbalance market. Using historical charging data and information provided by TenneT regarding the imbalance market, an optimization problem is formulated and a method for coordinating EV charging is proposed. Our sensitivity analysis of the weight parameters and reduction factor shows their significant impact on the performance of the controller. In this study, we evaluate the performance of the proposed co-regulation controller by tuning the weight parameters to find the optimal balance between financial benefits and customer satisfaction. Our sensitivity analysis of the weight parameters demonstrates that changing them can have a significant impact on the performance of the controller. We also consider the impact of the reduction factor on the performance of the controller and find that increasing it enhances financial benefits but reduces customer satisfaction. Our simulation results indicate that the proposed co-regulation controller can effectively balance financial benefits and customer satisfaction by using appropriate weight parameters. We estimate a yearly profit of e266.45 per EV user, which is equivalent to 13.2% reduction in cost. In conclusion, our research demonstrates the feasibility and effectiveness of using co-regulation to manage the charging demand of electric vehicles in a cost-effective and sustainable way. Our findings provide valuable insight for the development of smart charging strategies that balance the needs of the EV driver, the grid, and other stakeholders, and have important implications for the energy market. Further research is needed to evaluate the effectiveness and robustness of the proposed solution under varying degrees of uncertainty in the input data. Our proposed solution provides a practical and scalable method for managing the charging demand of electric vehicles and has the potential to contribute significantly to global efforts to reduce carbon emissions.