Under-frequency load shedding (UFLS) is a classic and a commonly accepted measure used to mitigate the frequency disturbances in case of loss-of-generation incidents in AC power grids. Triggering of UFLS is classically done at frequency thresholds when system frequency collapse is already close to happening. The renewed interest for synchronous condensers due to the global trends on massive commissioning of non-synchronous renewable power generation leading to reduction of system inertia gives an opportunity to rethink the approach used to trigger load-shedding activation. This question is especially relevant for the Baltic states facing a desynchronization from Russian power grid and a necessity to operate in an isolated island mode. The main goal of this paper is to introduce a predictive load shedding (LS) method without usage of either frequency or ROCOF measurements based on the monitoring of active power injections of synchronous condensers and to prove the efficiency of the concept through several sets of case study simulations. The paper shows that the proposed approach can provide a greatly improved frequency stability of the power system. The results are analyzed and discussed, the way forward for the practical implementation of the concept is sketched.

The transition from traditional synchronous generators to intermittent renewable sources, combined with increasingly variable and difficult-to-control energy demand, is creating a growing need for large-scale reserves and energy storage. At the same time, reduced system inertia and evolving electricity market regimes are emerging as important challenges that may affect grid stability, reliability, and economic performance. Advanced storage technologies, particularly those with fast ramping and high-response capabilities, offer a potential means of providing near-instantaneous support in response to unexpected system disturbances or market signals, thereby helping to mitigate inertia-related risks. This paper investigates four technologies: pumped hydroelectric storage, battery energy storage systems, synchronous condensers, and special protection schemes, with a focus on their capability to deliver rapid responses to large-scale disturbances. The analysis is conducted using a deliberately simplified power system model to provide qualitative insights into system behavior and control interactions. The results indicate that automation-enabled responses to system imbalances, including support from synchronous condensers and the rapid activation of additional generation, can enhance system performance under disturbance conditions within the considered framework. These findings demonstrate the feasibility and potential value of such approaches; however, further validation using higher-fidelity models and system-specific data is required to quantify their operational and economic impacts.

Transforming energy supply systems is essential for achieving a sustainable, carbon-neutral future. However, this transition substantially increases the complexity of long-term planning due to the large-scale integration of carbon-free energy sources, strong interactions among technical and economic subsystems, multiple stakeholders, and significant uncertainty. Comprehensive evaluation of alternative development pathways requires detailed power system and electricity market simulations across numerous scenarios, resulting in a major computational bottleneck. The objective of this paper is to develop and demonstrate an artificial intelligence-based surrogate modelling framework that enables the rapid evaluation of large numbers of long-term power system development scenarios while preserving the accuracy of detailed simulation models. The proposed framework combines stochastic scenario generation, detailed power system and electricity market simulations, and artificial neural network (ANN)-based surrogate models to approximate scenario-dependent performance indicators. By replacing repeated high-fidelity simulations with ANN surrogates, the methodology substantially improves computational efficiency while supporting uncertainty analysis, multi-scenario assessment, and multi-stakeholder decision-making. A case study demonstrates the effectiveness of the proposed framework for scalable, high-resolution strategic power system planning.

Transforming energy supply systems is vital for achieving a sustainable, carbon-neutral future. However, this transformation entails significant complexity, driven by the integration of carbon-free energy sources, tightly coupled technical and economic subsystems, and the need to ensure reliability, resilience, and affordability. Addressing these challenges requires advanced planning approaches capable of capturing uncertainty, multiple stakeholder perspectives, and system dynamics across temporal and spatial scales. In response, this article presents an integrated planning and operational framework that combines multi-actor, multi-criteria, and multi-stage stochastic optimization with surrogate modelling and artificial intelligence. The proposed approach enhances scalability, computational efficiency, and precision in the planning and operation of future energy systems. A case study illustrates its effectiveness in supporting high-resolution, uncertainty-aware decision-making.