Review of Different Optimization Techniques for Charging and Market Participation of Electric Vehicles

The transition towards carbon neutral power system comes with an increasing level of uncertainty, both on the production side from renewable energy sources (RES) but also from the consumption side such as electric vehicles (EV s), Broad penetration of EV s in the power system with uncontrolled charging will lead to undesired voltage and current limits violations, security issues and the unnecessary high cost of network reinforcement. To prevent voltage unbalance, harmonics, congestion and to reduce network losses, smart charging of EV s is necessary. Controllable charging can help in mitigating network issues and in the same time increase the quality of supply through ancillary service provision, such as voltage and frequency control. Different optimization techniques are used in the literature (direct optimization, heuristics, game theory, and machine learning) based on the type and complexity of the problem, desired computational time and the need for the global optimal solution. In the line with this, the contribution of this paper is to provide insight in different optimization techniques used for aggregation and smart EV charging with a focus on the different group types of optimization algorithms and benefits from the controlled operation of EV in the power system and different types of markets (energy market, reserve market, balancing market).