The growing penetration of renewable energy sources (RES) in power networks introduces significant challenges in load frequency control (LFC). Uncertainties in power generation make load balancing difficult, leading to frequency fluctuations that can cause equipment damage and blackouts. Additionally, the large-scale, spatially distributed nature of modern power systems necessitates a multi-agent control approach. Traditional PID-based controllers are ill-equipped to handle the uncertainties introduced by RES, while stochastic and robust model predictive control (MPC) methods, though capable of addressing small uncertainties, are often overly conservative. Similarly, reinforcement learning (RL) offers adaptability but lacks interpretability and explicit constraint handling. This thesis presents a distributed control framework that integrates model predictive control and reinforcement learning to address these challenges. Parametric uncertainties are incorporated into the system dynamics to account for stochasticities introduced by RES. At the core of the approach is a parameterized MPC scheme that approximates the RL value function, enabling the system to learn to avoid constraint violations while optimizing performance by driving state deviations from nominal operating conditions to zero. A distributed Q-learning scheme is used to learn the parametrization, which reduces the need for extensive information sharing, enhancing cybersecurity, and enables learning even with imperfect initial knowledge of system dynamics. The proposed framework is applied in simulations of a three-area power network to evaluate its potential and is compared against stochastic MPC and a deep deterministic policy gradient learning method. Results show that the proposed approach offers a balance between adaptability, performance and interpretability, and successfully handles constraints. It outperforms sample-based stochastic MPC in terms of cost, computation time and constraint handling, and outperforms deep deterministic policy gradient RL in performance, constraint handling and sample efficiency.

Autonomous vehicles offer significant potential for improving traffic efficiency and reducing
fuel consumption, with Model Predictive Control (MPC) being widely used due to its ability
to guarantee constraint satisfaction and safety while providing optimal control performance.
However, car models traditionally used in MPC approaches for vehicle control often overlooks
discrete dynamics like gear changes, which are critical for optimizing vehicle fuel consumption. Advancements have incorporated these discrete dynamics into MPC, resulting in a hybrid model that considers both continuous and discrete dynamics. The incorporation of
the fuel model, along with these discrete dynamics, significantly increases the computational
complexity of the MPC problem, making real-time implementation challenging. To address
this issue, Reinforcement Learning (RL) can be leveraged to simplify the optimization problem by learning policies that determine key discrete components, such as gear selection. This
allows the MPC controller to handle a simpler optimization problem, thereby reducing the
computational burden and enabling real-time control. This research aims to propose a new
approach to integrate RL and MPC for vehicle control, where RL is used to manage gear
transitions and MPC controls the overall vehicle dynamics, offering a computationally efficient solution, while achieving near optimal performance comparable to the conventional
MPC approach.