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Electronic vehicle (EV) adoption is an important part of the transition to a low carbon energy future, but rapid EV uptake will create drastic changes in electricity demand, potentially resulting in voltage imbalance and the need for network reinforcement. In this podcast, we take a look at how advancements in vehicle-to-grid (V2G) technology and the application of artificial intelligence, machine learning and reinforcement learning may enable the EV itself to provide part of the solution.

With V2G technology, EVs' batteries become a storage device when parked, and the energy stored in a charged EV battery can be used to balance the grid, storing energy when there is a surplus, and selling energy back into the grid when there is a wider demand.

In this episode of our Energy Tech podcast series, Richard Power and Nigel Brook take a look at how artificial intelligence, machine learning and reinforcement learning algorithms will be utilised to study an EV’s demand characteristics and provide information on how much energy can be stored and sold back into the grid without affecting daily needs. AI can also analyse electricity market trends to predict future market loads and schedule charging cycles to minimise possible peaks, allowing better EV integration into the grid. It may also use price signal algorithms to avoid charging at peak hours, creating a dynamic charging rate at any given time depending on the available data and demand.

Listen to find how these exciting new technologies will be applied and to understand the key legal opportunities and risks associated with them.