The transition to green energy is reshaping the energy landscape, marked by increased integration of renewable energy sources, distributed resources, and the electrification of other energy sectors. These changes challenge grid security, particularly regarding the N-1 security criterion, a crucial factor in preventing blackouts. Furthermore, climate change is contributing to the growing frequency of extreme weather events, which constitute the second major cause of blackouts. As grid complexity keeps on increasing, the need for N-k security, where k lines fail simultaneously, and increased resilience against extreme weather events is becoming increasingly evident. This necessitates studying the security constrained optimal power flow (SCOPF) problem considering multiple line outages (N-k). Current methods exhibit poor scalability as k increases. In response to the challenge of limited scalability, this thesis proposes a constraint-driven machine learning approach to approximate N-k SCOPFs. The proposed approach relies on the linearized direct current optimal power flow. The approach utilizes a neural network to map power system loads to generator setpoints. A feasibility restoration layer is employed to restore base case infeasible predictions. By incorporating line outage distribution factors (LODFs), all post-contingency flows are computed. The loss function utilized to train the neural network draws inspiration from the penalty function method. Lastly, a copula analysis computes joint outage probabilities for k extgreater 1 enabling a probabilistic security assessment. The first academic contribution of this thesis is the development of a constraint-driven approach to approximate N-k SCOPFs considering all contingencies using LODFs. The second academic contribution is the formulation of a N-k risk based security criterion, providing an alternative to the current deterministic N-1 security criterion. The approach shows promise in its ability to scale effectively to N-k contingencies. Using LODFs, the approach effectively computes all post-contingency flows for up to k = 3. Moreover, case studies show the constraint-driven approach's effectiveness in identifying violating post-contingency cases, with up to 173$ imes$ speedups and close to optimal dispatch costs. However, the consideration of N-k contingencies holds combinatorial complexity, and more efficient methods need to be developed for the computation and storage of all LODFs, and for the computation of all post-contingency flows. Additionally, the proposed constraint-driven approach can not enforce any post-contingency constraints, necessitating post-contingency feasibility checks when security against specific contingencies is required. Next, by incorporating probabilities, the approach shows promise in improving power systems security and resilience, but further research is necessary. In this thesis, only line outages are considered. In the future, the approach could be modified to additionally account for other equipment outages (e.g. generator outages). Furthermore, future research could investigate the adoption of this approach in corrective control settings, where it is employed in the restorative phase of a contingency event. Another suggestion is centered around the incorporation of graph neural networks in the proposed approach, which could provide a more scalable alternative to fully connected linear neural networks. Furthermore, more scalable methodologies could be explored to construct the matrix containing all LODFs, and a more scalable methodology for computing all post-contingency flows could be developed. Finally, future work could investigate how to utilize the proposed approach under varying conditions like network topology changes or changing outage probabilities.

The growth of renewable energy technologies is leading to energy systems that are more reliant than ever on renewables such as Wind and Photovoltaic (PV) power. Despite their benefits in terms of sustainability, their ubiquity poses challenges in maintaining grid stability given their intermittency, emphasising the prediction of power fluctuations. Physical models and statistical approaches, especially for nowcasting (forecasting for 0-6 hours in the future), have been superseded by Machine Learning (ML) methods in terms of forecast accuracy (below 3% Root Mean Squared Error (RMSE)). Within ML, Artificial Neural Network (ANN) methods seem to perform particularly well for nowcasting. This project focuses on predicting solar and wind meteorology with that level of accuracy, and on how to best use the prediction to minimize the cost of maintaining a balanced energy system, i.e. one where power consumption matches production at any moment. Producing accurate power predictions based on Multi-Modal (MM) data and the extent to which prediction accuracy reduces system cost are challenges to be addressed in this thesis. MM and End-to-End (E2E) training (with the system cost as the task of an ANN based algorithm) are investigated to this end. MM learning involves handling information from multiple types of input (audio and visual, for example) for performing a ML task such as regression or classification. It is of interest for this project because it has been shown to outperform other NN approaches in predicting sudden changes in solar irradiance. E2E learning entails an algorithm design which predicts the end goal of a ML process directly from the inputs. This is pursued because it addresses the true task (cost minimization) of system operators as the focus of the ML algorithm. The proposed method consists of a NN architecture that learns to fuse features from MM data (sky imagery and meteorological sensor data) at intermediate layers of the network in order to predict PV or Wind generation. This prediction is then used as an input to an Optimal Power Flow (OPF) problem (which seeks to minimize generation costs in a power system, considering power balance and transmission network constraints to ensure the twin goals of economic and secure system operation). The proposed model is trained E2E, therefore it is informed by the minimized cost solved by the optimization, rather than the intermediate power prediction (as conventional approaches would involve). In an IEEE 6-bus system with PV generation, a sequential training baseline results in costs 10% higher than a perfect forecast, while our proposed MM4-E2E approach achieves costs only 7% higher, a significant improvement. The intermediate prediction of PV power by MM4-E2E is also improved, with 18% lower RMSE by the proposed model compared to the baseline, explained by the enhancement of one modality by the other through MM learning. In a power system with two renewable sources, costs are reduced through the proposed model compared to a conventional approach (4% excess cost compared to 7%, measured against a perfect forecast), but power prediction accuracy is worse, sue to convergence to local minima.

The growing demand for electricity, driven by widespread adoption of heat pumps, electric vehicles, and industrial electrification, strains power grids and introduces challenges for a reliable and secure supply amidst intermittent renewable energy integration. Network topology control offers flexibility, altering connections to redirect power flows and mitigate transmission line overloads. This thesis aims to investigate an ML and AI approach to overcome the computational complexity. The proposed approach merges a curriculum-trained machine learning agent with a Monte Carlo Tree Search (MCTS) to enhance power network action security. The MCTS guides the simulation of potential actions, considering future outcomes for improved long-term performance identification. A curriculum-based ML approach is used to pre-train an agent to propose grid actions. MCTS is then used to secure these actions, leveraging outcomes in the training algorithm for enhanced sample efficiency and reduced training times. The approach uses MCTS-verified, simulation-tested actions for immediate training feedback, eliminating the need to wait for scenario completion, enhancing sample efficiency. An electrically distance-guided search in the MCTS improves convergence by prioritising actions closer to overflows, often found to be most influential in reducing violations.