The growing adoption of Electric Vehicles (EVs) presents challenges for the power grid, especially in meeting the peak demand without overloading the power system. Conventional grid reinforcement strategies are often costly and time-consuming, making them insufficient to address increased energy demand from simultaneous EV charging. However, when effectively managed, EV charging can be a flexible resource supporting grid stability and balance. To efficiently use this flexibility, business models play a crucial role in organising and incentivising market participation, yet the interaction between market players and grid integration remains underexplored. This article presents an extended and systematic review of over 100 state-of-the-art studies on business models for EV charging under grid limitations, presenting the most comprehensive analysis to date. Unlike the previous studies that primarily focus on technical EV-grid integration, this study combines technical and market-based solutions, focusing on the European electricity market and stakeholder perspectives. Moreover, the study identifies research gaps and proposes recommendations to improve or develop new business models for more efficient use of EV flexibility. The findings offer valuable insights for researchers, industry players, policymakers, and other actors aiming to improve the efficient usage of EV charging flexibility.

The growing adoption of electric vehicles (EVs) presents challenges for power systems, particularly due to uncontrolled charging. Such charging can lead to grid overload that requires immediate grid reinforcement. This paper proposes a planning model for an EV aggregator participating in ancillary service markets while considering the distribution grid limitations. Monte Carlo simulations capture uncertainties in mobility patterns and activations of the ancillary services. We compare uncontrolled charging with a bidirectional smart charging algorithm, which is formulated as a mixed-integer linear program. A case study focusing on the Swedish market, specifically regarding participation in the frequency containment reserve, demonstrates that smart charging benefits the EV aggregator, EV owners, and the power system. The results highlight that the flexibility of the EV can optimize the existing utilization of the grid and delay the reinforcement of the grid. The proposed planning model supports decision-making in uncertain markets, ensuring the feasibility of the EV aggregator business model.

The growing adoption of electric vehicles (EVs) has introduced substantial challenges to the grid. Uncontrolled EV charging may lead to grid overloading, voltage instability, increased power losses, accelerated aging of distribution transformers, and risk of outages. Therefore, a strategic approach is required to tackle the adverse impacts of uncontrolled EV charging to the grid. A promising approach is using EV batteries collectively as a flexible load. Residential areas have the most pronounced EV flexibility potential due to the significant length of uninterrupted parking. In this paper, models of EV charging in residential areas are formulated, followed by Monte Carlo simulations. Three charging models are developed: uncontrolled charging, controlled charging without considering grid limitation and controlled charging considering grid limitation. An optimization problem based on quadratic programming is used in the controlled charging. A residential area based on the IEEE European LV test feeder adopting the deregulated Swedish electricity market is taken as a case study for the simulation. The case study findings indicate that incorporating grid limitation into controlled charging strategies can prevent grid overload and significantly reduce charging and battery degradation costs. In this case study, controlled charging can reduce the charging costs to approximately 42% compared to uncontrolled charging. Considering the battery degradation costs, controlled charging costs are 24% lower than uncontrolled charging. It is possible to postpone the costly grid reinforcement by applying strategic EV charging scheduling. The methods and outcomes pave the way for developing, testing, and implementing business models to manage the grid impacts of growing EV charging.