What can V2G actually do in a grid that's already stretched?
Vehicle-to-grid (V2G) lets electric vehicles send power back to the grid during peak demand, acting like mobile batteries. In a real-world simulation of a Brazilian urban power grid, researchers tested adding up to 118 battery electric buses with V2G capability. They found that V2G successfully shaved peak demand — meaning it reduced the highest stress on the grid — but only up to a point: when more than 33 buses tried to send power back at the same time, the grid experienced overvoltages (dangerous voltage spikes) [1]. This tells you that in a typical developing-world distribution feeder, V2G can help, but it requires smart controls to coordinate how many vehicles discharge simultaneously. The same study showed that adding solar panels could supply up to 64% of the buses' daily energy needs, but also caused reverse power flows and overvoltages, reinforcing that dynamic control is essential [1].
What technical and policy conditions make V2G viable in constrained regions?
The technical side is solvable but not trivial. Researchers have developed robust controllers (like super-twisting sliding mode controllers) that can manage the bidirectional power flow in EV chargers for both charging and discharging, reducing the 'chattering' (unwanted oscillations) that can damage equipment [4][5]. These controllers have been verified in hardware-in-the-loop tests, meaning they are ready for real-world deployment [4][5]. However, the Brazilian study shows that even with V2G, a grid may need conductor upgrades or load redistribution if too many vehicles connect at once — beyond 56 buses, thermal overload (overheating of wires) occurred [1]. On the policy side, integrated energy system modeling, as explored for India, can help plan where to put charging stations, manage peak electricity demand, and maximize use of renewable energy — all of which are critical for making V2G work in infrastructure-constrained settings [3]. The key takeaway: V2G is not a plug-and-play solution; it requires smart grid features, targeted grid reinforcement, and policies that prevent cost shifting onto the poor.
About These Sources
This answer is built on 5 peer-reviewed studies — published from 2022 to 2025, 2 from 2024 or later, 3 in Q1 journals, collectively cited 100 times — selected as the most relevant from 6 studies that passed quality screening, drawn from 54 papers retrieved from a database of over 500 million.
Sources used in this answer
Impact Assessment of Electric Bus Charging on a Real-Life Distribution Feeder Using GIS-Integrated Power Utility Data: A Case Study in Brazil
In a simulation of a Brazilian urban power feeder, V2G operation with electric buses enabled peak shaving but caused overvoltages when more than 33 buses injected power simultaneously; the grid also faced thermal overload beyond 56 buses, indicating the need for smart controls and reinforcement [1].
How grid reinforcement costs differ by the income of electric vehicle users
Using real driving profiles and power flow simulations, grid reinforcement costs for EVs were up to 33 times higher in higher-income neighborhoods than lower-income ones, and if costs are spread evenly, low-income households could face energy poverty [2].
Assessing the Sustainability of Transportation Electrification in India
Integrated energy system modeling for India shows that V2G can help balance the grid and integrate renewables, but success depends on strategic planning for charging stations and peak demand management [4].
Supertwisting sliding mode controller for grid-to-vehicle and vehicle-to-grid battery electric vehicle charger
A super-twisting sliding mode controller was designed and hardware-verified for a BEV charger in both G2V and V2G modes, reducing chattering and ensuring stable power flow [5].
Robust nonlinear control of battery electric vehicle charger in grid to vehicle and vehicle to grid applications
A similar robust nonlinear controller for BEV chargers was developed and tested via hardware-in-the-loop, showing better dynamic performance than an integral backstepping controller for V2G and G2V operations [6].
