Francisco Sousa Fernandes

Abstract
This work proposes an innovative methodology for the optimal placement of grid-forming converters (GFM) in converter-dominated grids while accounting for multiple stability classes. A heuristic-based methodology is proposed to solve an optimisation problem whose objective function encompasses up to 4 stability indices obtained through the simulation of a shortlist of disturbances. The proposed methodology was employed in a modified version of the 39-bus test system, using DigSILENT Power Factory as the simulation engine. First, the GFM placement problem is solved individually for the different stability classes to highlight the underlying physical phenomena that explain the optimality of the solutions and evidence the need for a multi-class approach. Second, a multi-class approach that combines the different stability indices through linear scalarisation (weights), using the normalised distance of each index to its limit as a way to define its importance, is adopted. For all the proposed fitness function formulations, the method successfully converged to a balanced solution among the various stability classes, thereby enhancing overall system stability.

Abstract
This work proposes a robust methodology for the location and sizing of grid forming (GFM) converters that simultaneously considers the solution costs and the security gains while accounting for the TSO nonlinear cost-security sensitivity. Such methodology, which includes a collection of techniques to reduce the problem dimensionality, formulates the placement problem as a non-linear multi-criteria decision support problem and uses a solution-seeking algorithm based on Bayesian Optimisation to determine the solution. To ease comprehension, a modified version of the IEEE 39 Test System is used as a case study throughout the method's detailed explanation and application example. A sensitivity analysis of the GFM converter's over-current capacity in the solution of the formulated placement problem is also performed. The results show that the proposed method is successful in finding solutions with physical meaning and that respect the decision agent preferences.

Abstract
The increasing penetration of renewable energy sources in power systems has heightened the importance of grid-forming (GFM) converters, which emulate the dynamic behavior of synchronous machines and are crucial for ensuring stability in converter-dominated grids. However, the complexity of modern grids calls for innovative control mechanisms to unlock the full potential of GFM technology. This work presents a novel automated framework for control design in power systems. Simulated annealing is used to evolve the structural design of control systems represented as graph-based models. The method achieves greater flexibility by using control graphs instead of traditional tree-based representations, supporting complex feedback loop configurations. A simplification process is also included to reduce complexity and improve interpretability, ensuring practical applicability. Validation on a two-generator power system with one GFM converter demonstrates the method's ability to design robust controllers that enhance system stability, achieving better performance metrics, such as smoother frequency responses with significantly reduced frequency deviations compared to benchmark configurations. The improved frequency response arises from differing terminal angle profiles, enabling faster, stronger power responses that quickly arrest frequency deviations during disturbances.

Abstract
In a high-risk sector, such as power system, transparency and interpretability are key principles for effectively deploying artificial intelligence (AI) in control rooms. Therefore, this paper proposes a novel methodology, the evolving symbolic model (ESM), which is dedicated to generating highly interpretable data-driven models for dynamic security assessment (DSA), namely in system security classification (SC) and the definition of preventive control actions. The ESM uses simulated annealing for a data-driven evolution of a symbolic model template, enabling different cooperative learning schemes between humans and AI. The Madeira Island power system is used to validate the application of the ESM for DSA. The results show that the ESM has a classification accuracy comparable to pruned decision trees (DTs) while boasting higher global inter-pretability. Moreover, the ESM outperforms an operator-defined expert system and an artificial neural network in defining preventive control actions.

Abstract
Hybrid storage systems that combine high energy density and high power density technologies can enhance the flexibility and stability of microgrids and local energy communities under high renewable energy shares. This work introduces a novel approach integrating rule-based (RB) methods with evolutionary strategies (ES)-based reinforcement learning. Unlike conventional RB methods, this approach involves encoding rules in a domain-specific language and leveraging ES to evolve the symbolic model via data-driven interactions between the control agent and the environment. The results of a case study with Liion and redox flow batteries show that the method effectively extracted rules that minimize the energy exchanged between the community and the grid.