The current power grid is heavily congested, as a result of more itermittent power loads and less controllable sources and drains of power. This issue is exacerbated by the planned introduction of large quantities of solar and wind capacity, introducing more uncertainty into the grid operation problem. Current expansion plans are not able to cope with the expected loads, nor can they keep the delivery-guarantees currently kept while also decarbonizing the grid. This is a problem for the green transition, and more efficient grid expansion can help mitigating this problem.

The proposed solution leverages extra information found using the scenario approach to grid operation, instead of the Monte-Carlo simulations currently used in grid expansion studies. Doing so exploits the extra information the scenario approach yields with regard to reliability, and operational performance. By optimizing robustly against all samples we find more useful improvements that actually push the boundary of operation, where the Monte-Carlo approach might be less likely to find those same improvements given that is overly focuses on samples of little consequence.

To test this hypothesis, a simple graph representation of a power grid is constructed, and a grid expansion program either using the scenario approach or the Monte-Carlo approach is applied to this graph, resulting in two sets of candidate modifications that either program found to be most promising. To validate the results, operational performance using both the scenario approach and the Monte-Carlo approach are computed, the empirical violation probabilty is computed and computation time is compared.

Deep Reinforcement Learning (DRL) is a promising approach to Traffic Signal Control (TSC). However, significant challenges remain in translating this potential into real-world traffic management solutions. This thesis investigates obstacles hindering the application of DRL in real-world TSC, focusing on low sampling frequencies and the complexities of multi-modal traffic scenarios.

We developed a high-frequency sampling Proximal Policy Optimization (PPO) approach for TSC at a four-legged intersection, integrating both vehicle and pedestrian traffic in a multimodal setting. By employing Invalid Action Masking (IAM), we effectively handle signal timing constraints across these modalities. The framework was evaluated through traffic volume sensitivity analyses, assessments of generalization capabilities, disturbance rejection tests, and comparisons of methods for handling invalid actions.

The results indicate that short sampling intervals, such as 1 second, do not improve performance in terms of time-loss, with 4 to 6 seconds identified as the optimal range for PPO in TSC of a four-legged intersection. The findings also demonstrate that IAM can effectively be incorporated without compromising performance. When evaluating the ability to handle sudden spikes in traffic volume, PPO demonstrated superior performance, outperforming baseline methods such as max-pressure and fixed-time strategies in terms of both overshoot and settling time. Also, the results show that PPO can effectively prioritize vehicle and pedestrian modalities, displaying a clear proportional decrease in time-loss for the prioritized modality.