Machine learning based electric vehicle charging management system for smart grid applications

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Demand for electric vehicles (EVs) is growing rapidly, with EVs forecast to account for as much as 60% of new car sales by 2030. Growth is being driven by government policy, in line with net zero targets, increasing consumer choice and availability of supporting infrastructure. Large scale charging infrastructure scale up is required however, globally, to support EV demand growth and is forecast to require up to $1 trillion of investment by 2040. With net zero targets also driving increased renewable baseload power adoption, grids have the challenge of managing the intermittency that comes with renewable energy supply. Electric vehicle fleets provide the potential to assist grid balancing as sources of both demand (grid to vehicle (G2V)) and supply (vehicle to grid (V2G)), potentially reducing the need for grid scale energy storage. In order to manage grid balancing in this context, the ability to accurately forecast demand and supply will be crucial. AI and machine learning techniques can assist in this area to derive patterns from large scale datasets with complex relationships between variables. Also, human behavioural models are increasingly being integrated, to assist the understanding and prediction of EV owner behaviours. This project will seek to prove the hypothesis that EVs have the potential to meaningfully assist grid balancing if owners are appropriately incentivised. A key objective of the research will be to produce an AI based model that simulates EV charging and discharging activity in the context of overall electricity grid supply, demand and grid balancing strategies - the model will allow users to simulate different scenarios and gauge EV owner responses to changing conditions, including the integration of suitable human behavioural models. The project will aim to build on research to date and to understand any barriers to the large-scale involvement of EVs in grid balancing and how these may be overcome. The research has the potential to be of interest to a range of stakeholders, including grid operators, energy companies, EV companies and charging station operators. There is also the potential for the work to both learn from and be of interest to those in other fields studying the combination of AI techniques and human behavioural modelling. Research to date shows that reinforcement learning (RL) based methods are increasingly being applied to EV charging scenarios. RL has a number of benefits over conventional model-based optimisation, being more suited to the complexity, dynamism and randomness of power network modelling. The proposed approach is to obtain grid supply and demand data, along with EV usage and charging data and to develop a multi-vehicle, deep reinforcement learning model, where EV owners seek to maximise their utility within their environment, with cognitive models (such as expected utility theory and prospect theory) built into the way rewards (such as utility relating to range, cost and time) are perceived in the RL reward function. With increasing numbers of EVs on the road and increasing availability of data, there is the potential to complete a real-world study into charging behaviours, integrating the results into the workings of the RL reward function, to add realism. If the model can be developed with some realism, this will allow users to test responses to differing scenarios, with the ability to adjust a range of variables. Research to date shows that there can be novelty in advancing the level of behavioural modelling integrated into RL methods, for example by integrating cumulative prospect theory and more advanced cognitive models into the RL reward function. Also, the future grid is expected to include aerial vehicles and other forms of transport, as well as ground-based electric vehicles; research into the behavioural aspects of aerial vehicle charging has so far been limited.