EirGrid

European power systems are transitioning towards low-inertia, converter-dominated operation, introducing new challenges for oscillatory stability. Reported oscillation events are typically presented in isolation, limiting broader understanding of trends. This paper presents a cross-event synthesis of oscillatory incidents across Europe, bringing together experiences of Very Low Frequency Oscillations (VLFOs), inter-area modes, and Sub-Synchronous Oscillations (SSOs). Recurring operating conditions associated with oscillatory events are identified, including reduced system strength, lightly loaded systems and high penetrations of Inverter Based Resources (IBRs). Additionally, this paper presents, for the first time in the literature, three Sub-Synchronous Oscillation events experienced on the island of Ireland. These events provide new insight into oscillatory behaviour in high Inverter Based Resources systems and reinforces the trends observed across Europe. The findings demonstrate that oscillatory stability is becoming more complex, operating condition dependent and heavily influenced by control interactions, highlighting the need for enhanced monitoring, modelling and mitigation strategies for next-generation power systems.

This paper presents the harmonic distortion allocation methodology used in the Irish transmission system to ensure compliance with power quality limits in accordance with IEC 61000-3-6 planning levels. Increasing penetration of inverter-based resources (IBR) and long high-voltage underground cables can lead to amplification of harmonic distortion at the Point of Connection (POC). To address these challenges, the Transmission System Operator (TSO) has implemented a structured allocation framework that quantifies headroom, assigns limits to new Users and ensures compliance through post-connection monitoring. The methodology incorporates background distortion measurement, uncertainty margins, allocation rules and network harmonic impedance considerations. This paper summarizes the allocation process, provides definitions and discusses its practical application in Ireland.

ABSTRACT Increasing global energy demand and renewable energy expansion have heightened the importance of accurate solar irradiance forecasting for effective grid management and capacity planning. Atmospheric pollution significantly affects solar irradiance measurements, requiring air quality integration for precise forecasting in polluted urban environments. This study develops a comprehensive multi‐city data set spanning eight geographically diverse locations with systematically categorized pollution levels, from pristine environments (Copenhagen, Sydney) to heavily polluted urban centers (Beijing, New Delhi, Lahore). A pollution‐aware neural network training methodology is introduced, representing the first systematic investigation of ensemble model performance across explicitly categorized atmospheric quality levels. The study presents a novel ensemble architecture integrating multi‐layer perceptrons, recurrent neural networks, and nonlinear autoregressive with exogenous inputs, specifically designed for forecasting under varying atmospheric pollution conditions. The proposed ensemble model achieves superior performance with R ² of 0.8702, RMSE of 1.0809, and MAE of 0.8137, consistently outperforming individual models across all pollution categories and geographical locations. Validation using the HI‐SEAS data set confirms superiority over three contemporary state‐of‐the‐art methodologies. The framework incorporates SHapley Additive exPlanations (SHAP) analysis for model interpretability and comprehensive cross‐validation procedures. This study establishes a foundational framework for pollution‐aware solar forecasting, addressing critical gaps regarding atmospheric variability's impact on prediction accuracy.

Frequency control in power systems is implemented in a hierarchical structure traditionally known as primary frequency control (PFC), secondary frequency control (SFC) and tertiary control reserve (TCR) and, some jurisdictions, include time error control (TEC) as well. This hierarchical structure has been designed around a century ago based on timescales separation, that is, approximately an order of magnitude difference between each control structure. This paper argues, based on real-world observations as well as detailed dynamic simulations on a model of the All-Island power system (AIPS) of Ireland, that this frequency control structure is not necessary in current and future converter-dominated power grids. The paper proposes to redesign this structure by removing the SFC and TCR and rely on PFC and a real-time energy market. The PFC is responsible for addressing fast power imbalances in timescales of tens of ms to few minutes (e.g., 100 ms to 5 minutes) while the real-time energy market is responsible for addressing longer imbalances in timescales of minutes to hours (e.g., 5 minutes to 1 hour). TEC, on the other hand, is considered as optional.

Electric power systems are entering a period of demand growth worldwide. The growth includes many types of loads that are interconnecting and anticipated to connect in the coming years. In many regions, demand growth is no longer driven primarily by broad population or economic growth. Instead, a meaningful share of new demand comes from individual facilities whose size, concentration, and operating characteristics can materially affect the bulk power system and the markets that operate those systems. These facilities include AI data centers, traditional hyperscale and multi-tenant data centers, cryptocurrency mining operations, semiconductor and battery manufacturing plants, hydrogen electrolysis facilities, and other large electrified industrial processes. A single project may consume tens or hundreds of megawatts at their maximum consumption all at one point of interconnection, or even gigawatts, that can pose reliability or economic consequences to the larger power grid. That scale is not unprecedented in an absolute sense—heavy industry has long included large individual customers—but the pace of development, the clustering of facilities, and the diversity of operating profiles create a different planning and operational environment than the one for which many existing rules were designed. Large loads matter to wholesale electricity markets because organized markets are built around economic scheduling of supply and demand, along with efficient price formation to align incentives with what the grid needs in both the short- and long-term timeframes. In many energy systems, day-ahead, intra-day and real-time markets are used to schedule energy; ancillary service markets are used to procure operating reserves and other grid services; and capacity markets support long-term resource adequacy and recovery of capital costs that are not recoverable from those other market products. These frameworks depend on load forecasts to determine required volumes, technology participation models that represent accurate capabilities of the market participants supporting the markets and understanding of eligible product providers for each service. Large loads can affect all three areas. The markets require accurate forecasts that ensure sufficient supply is available. The market prices can guide flexible responses and obligations for large loads through potentially new participation models while providing multiple products and services.