| dc.description.abstract | The management of present-day power systems has become increasingly complex due to the
integration of distributed and renewable energy resources. This added complexity makes the
power system optimization problem challenging to solve with conventional methods because of
its increased dimensionality. Reinforcement learning offers a model-free approach to optimal
decision-making. However, popular algorithms such as Deep Q-Networks (DQN) struggle to
produce feasible solutions due to their inability to strictly enforce operational constraints. To
address this issue, Hou Shengren et al. (International Journal of Electrical Power & Energy
Systems 152, 2023) proposed a variation of the DQN algorithm called “MIP-DQN”. Building
on the work of Matteo Fischetti and Jason Jo (Constraints 23.3: 296-309, 2018), this algorithm
formulates the trained critic neural network as a mixed-integer programming problem, enabling
the algorithm to identify the action that maximises the action-value function while satisfying
operational constraints.
With this thesis, we aim to provide an accessible study of the use of reinforcement learning,
particularly the MIP-DQN algorithm, for the optimal management of hybrid power systems from
both theoretical and applied perspectives. Theoretically, we present the necessary mathematical
foundations to understand reinforcement learning in the context of power system optimization.
This includes a detailed mathematical formulation of the power system optimization problem
and an explanation of the novel MIP-DQN algorithm, touching on various mathematical topics
such as optimization, Markov Decision Processes, optimal control, and reinforcement learning.
Our main contribution is the implementation of the MIP-DQN algorithm and the analysis of
its performance in optimising the management of a specified power system. | |
