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In view of the increasing levels of renewable generation across the world, the operational dynamics of power systems are changing. One of the dimensions of this transformational change is the nature of fault ride through behavior of renewable technologies such as wind and the associated differences compared to conventional generation. This paper investigates the impacts of fault ride through behavior of wind farms on the system stability in detail, using full dynamic model of a low inertia island power system. Recorded PMU measurements are used to establish the variations in wind farm behavior during and after a fault. A detailed sensitivity analysis on the active and reactive power injection behavior of wind farms is carried out to establish the component of fault ride through behavior that is most critical to system stability, using the full model of the Ireland and Northern Ireland Power system (All island power system). Detailed investigation of the mechanics of a fault in a low inertia, wind generation dominated system is carried out to reveal potential technical scarcities. Finally, mitigations for identified scarcities arising from non-ideal fault ride through behavior of wind farms are presented in terms of Transmission System Operator led novel system services. [ABSTRACT FROM AUTHOR]

This paper presents a methodology for the design of over-frequency containment schemes in low inertia, high renewable systems. This methodology is demonstrated through the comprehensive redesign of the containment scheme for significant over-frequency events (e.g., the loss of an exporting HVDC interconnector) in the Ireland and Northern Ireland power system (All-Island System). The new scheme allows an existing synchronous generator runback system integrity protection scheme to be replaced with fixed wind tripping and dynamic response from wind. This exploits the capabilities of renewable generation to create effective protection and control resources. The methodology provides the steps required to determine the key parameters of the scheme (e.g., maximum allowable frequency, required volume of tripping, lowest trip frequency). A key consideration of the methodology is the volume of dynamic response from wind that can be deployed before frequency limits are violated (e.g., inertia and wind penetration). Dynamic simulations are performed in DSE Tools for the All-Island system considering a 2020 operational scenario. These simulations support the application of the methodology and demonstrate the effectiveness of the scheme designed for the All-Island system. [ABSTRACT FROM AUTHOR]

This paper describes a power system inertia estimation method that utilizes power perturbations caused by the flow switch on an HVDC interconnector. The analysis is performed using real power system measurements from Ireland's all-island power system. The method employs an energy based estimation technique which permits a non-step power change to be used for inertia calculation and encapsulates the challenges associated with the use of real data. While it is shown that synchronous generators account for most of the power system inertia, other contributors are also investigated, namely power station auxiliary load, distributed generation and power system demand. Time-of-day and wind related effects on Ireland's power system inertia are explored. Recommendations are made for the practical implementation of the method. [ABSTRACT FROM AUTHOR]

Accurate and rapid load forecasting for industrial customers has been playing a crucial role in modern power systems. Due to the variability of industrial customers’ activities, individual industrial loads are usually too volatile to forecast accurately. In this paper, a short-term load forecasting model for industrial customers based on the Temporal Convolutional Network (TCN) and Light Gradient Boosting Machine (LightGBM) is proposed. Firstly, a fixed-length sliding time window method is adopted to reconstruct the electrical features. Next, the TCN is utilized to extract the hidden information and long-term temporal relationships in the input features including electrical features, a meteorological feature and date features. Further, a state-of-the-art LightGBM capable of forecasting industrial customers’ loads is adopted. The effectiveness of the proposed model is demonstrated by using datasets from different industries in China, Australia and Ireland. Multiple experiments and comparisons with existing models show that the proposed model provides accurate load forecasting results. [ABSTRACT FROM AUTHOR]