Machine Learning Approaches for Optimising Battery Operations in Smart Grids

The project will investigate various machine learning approaches to optimise battery operations in the smart grid. Battery storage plays a vital role in smoothing out the fluctuations in energy demand and generation from distributed renewable sources. Machine learning can help predict the energy demand, and solar and wind power generation. Using reinforced learning, it is possible to make optimal battery charging and discharging decisions to flatten the demand curve. The project will involve extensive simulations and building machine learning models. The prospective students will need to have a strong background in programming, simulation and machine learning. The knowledge of renewable energy generation and battery storage is also essential.