In the evolving landscape of energy systems, microgrids have emerged as a key solution for enhancing energy efficiency and sustainability. Capable of operating independently or alongside the main power grid, microgrids integrate renewable energy sources and ensure local energy distribution. This makes them instrumental in reducing dependencies on centralised power supplies and improving resilience against disruptions. This research addresses the unit commitment problem, a mathematical optimisation challenge where the objective is to coordinate a group of energy production units to meet demand at minimal cost. We model the microgrid as a mixed logical dynamical (MLD) system, incorporating both the continuous and discrete variables involved in the microgrid. Model predictive control (MPC) is selected as the control strategy due to its suitability for controlling hybrid systems and its ability to handle complex constraints.

However, the application of MPC is challenged by the need to solve computationally demanding mixed-integer linear programming (MILP) problems at each control iteration, which are combinatorial. To address this challenge, this research proposes integrating a learning-based method to enhance MPC in microgrids. We propose using transformers to learn and predict the binary decisions in MILP problems, thereby reducing the problem to a more tractable linear programming problem. Transformers are chosen for their ability to recognise patterns in sequential data, a key aspect of the decision-making process in MPC. Furthermore, their capability for parallel processing allows for more efficient training and scalability to larger problems, making them highly suitable for handling the dynamic and complex optimisation tasks found in microgrid control. Simulation experiments show that integrating transformers in the decision of the discrete variables reduces the overall computation with only a slight loss of optimality and, therefore, improves the online applicability of MPC in microgrid control.