Redispatch is essential in power system operation to maintain security and resolve congestions. However, often the redispatch models do not fully capture the operational constraints of diverse generation resources and operator preferences. This paper proposes a multi-period and multi-energy source preventive N-1 redispatch model. The model adjusts a day-ahead market-cleared dispatch to account for the N-1 security while accounting for generator (thermal, hydro, battery storage, wind, and solar) and grid constraints. To reflect practical operator preferences, the model minimises both redispatch costs and the number of redispatch actions. An adaptive constraint relaxation mechanism is included to address infeasibility under stressed conditions while preserving system security. The proposed model captures key operational features of modern power systems and supports decision-making in both cost-focused and operator-aligned scenarios. An extended IEEE 118-bus system is used to demonstrate the model’s effectiveness in enhancing system security, reducing redispatch costs, and utilising flexibility resources under realistic conditions.

The electrification strategies that are being designed to meet sustainability objectives and rising energy demands pose significant challenges for power systems worldwide and require Transmission Expansion Planning (TEP). This study adopts a risk-informed approach to TEP, formulated as a multi-objective optimization problem that concurrently minimizes systemic risks and expansion costs. Given the intractability of this problem with conventional solvers, we turn to artificial intelligence techniques. In particular, we conceptualize power grids as graphs and introduce a goal-oriented graph generation methodology using deep reinforcement learning. We extend welfare-Q learning, a modified variant of Q-learning tailored to yield high rewards across multiple dimensions, by incorporating geometric deep learning for function approximation. This allows us to account for system security while minimizing grid expansion costs. Notably, system risk is evaluated by incorporating a Graph Neural Network (GNN) cascading failure meta-model into the proposed approach. The TEP method is applied to the IEEE 118-bus system, and the efficacy of this novel technique is compared against the state of the art. We conclude that the deep reinforcement learning method can compete with established methods for multi-objective optimization, identifying expansion strategies that improve system security at reduced costs. Furthermore, we test the robustness of the meta-model against topology changes in the transmission network, demonstrating its applicability to novel grid configurations.

Power grids are critical infrastructures of paramount importance to modern society and, therefore, engineered to operate under diverse conditions and failures. The ongoing energy transition poses new challenges for the decision-makers and system operators. Therefore, we must develop grid analysis algorithms to ensure reliable operations. These key tools include power flow analysis and system security analysis, both needed for effective operational and strategic planning. The literature review shows a growing trend of machine learning (ML) models that perform these analyses effectively. In particular, Graph Neural Networks (GNNs) stand out in such applications because of the graph-based structure of power grids. However, there is a lack of publicly available graph datasets for training and benchmarking ML models in electrical power grid applications. First, we present PowerGraph, which comprises GNN-tailored datasets for i) power flows, ii) optimal power flows, and iii) cascading failure analyses of power grids. Second, we provide ground-truth explanations for the cascading failure analysis. Finally, we perform a complete benchmarking of GNN methods for node-level and graph-level tasks and explainability. Overall, PowerGraph is a multifaceted GNN dataset for diverse tasks that includes power flow and fault scenarios with real-world explanations, providing a valuable resource for developing improved GNN models for node-level, graph-level tasks and explainability methods in power system modeling. The dataset is available at https://figshare.com/articles/dataset/PowerGraph/22820534 and the code at https://github.com/PowerGraph-Datasets.

This paper presents a fault tree analysis (FTA) method for the unavailability of supply of gas networks. The method is based on the automatic generation of fault trees, which estimate the probability of disruption of the gas delivery from terminals/storages to the demand nodes. Moreover, it allows probabilistic analyses of the availability of gas supply to individual demand nodes and to the overall gas network. To assess the importance of each network component, the risk achievement worth and the risk reduction worth importance measures are utilized. The developed method can identify weakness in the gas network and can guide expansion planning and maintenance scheduling activities. Furthermore, the developed framework is enriched by steady-state analysis of the gas network operations performed using a physical flow/pressure model. The impact of a component failure on the gas supply interruption at different demand nodes is assessed by the physical model in retrospective to the FTA results. The framework is exemplified with reference to the reduced gas network of Great Britain. The results provide insights to support a robust reliability assessment of the gas network. Moreover, the probability mapping of the most important components in the gas network informs optimal strategies for maintenance scheduling as well for prioritizing improvements of the gas network aimed at significant risk reduction. Remarkably, the failure of components identified as most important according to the FTA importance measures may not have significant effects on the gas supply. A discussion on the range of applicability and limitations of the two methodologies is provided.

Cyber-attacks in cyber-physical systems (CPS) are receiving much attention due to the pervasive use of communication in essential services. If cyber components in CPS are compromised by attackers, the ability to maintain stability of the physical system is lost, and performance disruptions may occur. Vulnerability analysis allows quantifying the impact of attacks based on the damage cost model. Yet, existing works partially account for uncertainties in cyber-attacks and may provide inadequate support to decision making. This paper proposes a framework for optimal defense resource allocation for minimizing unsupplied demand of CPS under uncertain cyber-attacks. The vulnerability model of cyber components is described by an attacker-defender two-stage min-max game. The unavailability of cyber components causes the loss of performance of the monitored physical components. Uncertainties in the most probable attack time and in the accuracy of its estimate by the defender are considered. Numerical studies identify the optimum strategies in terms of protection and redundancy allocation of cyber components, and demonstrate that the contest intensity largely affects the two-stage game. Furthermore, if uncertainties increase, the system damage costs also increase and the defensive resource allocation strategies converge to a constant one due to the defender's lack of information about the attack.

This paper looks into the vulnerabilities of the electric power grid and associated communication network, in the face of intermittent power generation and uncertain demand within a complex network framework of analysis of smart grids. The perspective is typical for the system of systems analysis of interdependencies in a critical infrastructure (CI), i.e., the smart grid for electricity distribution. We assess how the integration of the two systems copes with requests to increase power generation due to enhanced power consumption at a load bus. We define adequate measures of vulnerability to identify the most limiting communication time delays. We quantify the probability that a reduction in the functionality of the communication system yields a faulty condition in the electric power grid, and find that a factual indicator to quantify the coupling strength between the two networks is the frequency of load-shedding actions due to excessive communication time delay. We evaluate safety margins with respect to communication specifications, i.e., the data rate of the network, to comply with the safety requirements in the electric power grid. Finally, we find a catastrophic phase transition with respect to this parameter, which affects the safe operation of the CI.

Infrastructure interdependency stems from the functional and logical relations among individual components in different distributed systems. To characterize the extent to which a contingency affecting an infrastructure is going to weaken, and possibly disrupt, the safe operation of an interconnected system, it is necessary to model the relations established through the connections linking the multiple components of the involved infrastructures. In this work, the modeling of interdependencies among network systems and of their effects on failure propagation is carried out within the simulation framework of a failure cascade process. The sensitivity of the critical loading value (the lower bound of the cascading failure region) and of the average cascade size with respect to the coupling parameters defining the interdependency strength is investigated as a means to arrive at the definition and prescription of cascade-safe operating margins.