A novel frequency response analysis model applicable to high-penetration wind power grid

The decoupling relationship and low inertia characteristics of wind turbine generator system (WTGS) make the system frequency response more complex. In order to evaluate the frequency stability of high-penetration wind power grid, it is significant to establish an exactly frequency response analysis model. Due to the features of wide spatial distribution and large number of WTGS in the power grid, the wind speed and actual operating state faced by WTGS in different regions have obvious discrepancy, which makes the traditional frequency response models that merely considers the single operating state of WTGS have evident limitations. Therefore, in order to consider the discrepancy of frequency regulation characteristics caused by the spatial dispersion of WTGS, an improved frequency response modeling method which can take into account the frequency regulation characteristics of multiple wind speeds is proposed. This method uses small signal analysis theory and fuzzy control method to construct an improved equivalent aggregation model of WTGS. Combining this model with the typical system frequency response (SFR) model, a novel frequency response model considering different wind conditions and different frequency regulation characteristics of WTGSs is established. Finally, an improved IEEE-118 bus system is used for case analysis to verify the correctness and superiority of the proposed method for high-penetration wind power grid. (C) 2022 The Authors. Published by Elsevier Ltd

Demand side control for power system frequency regulation

The increasing penetration of renewable energy resources brings a number of uncertainties to modern power system operation. In particular, the frequent variation of wind or solar power output causes a short-term mismatch between generation and demand and system frequency fluctuation. The traditional approach to dealing with this problem is to increase the amount of system spinning reserve, which increases costs. In recent years, researchers have been actively exploring the utilization of residential and commercial loads in frequency regulation without affecting customers’ comfort level. This is called dynamic demand control (DDC). This dissertation describes an in-depth study of DDC for bulk power system frequency regulation, from both a technical and economic perspective

Integrating physical and data-driven system frequency response modelling for wind-PV-thermal power systems

This paper presents an integrated system frequency response (SFR) modelling method for wind-PV-thermal power systems (WPTPSs) by combining both physical model-based and data-driven modelling methods. The SFR physical model is built and simplified by the balanced truncation (BT) method. Based on the physical model, an improved radial basis function neural networks (RBFNNs) is then employed to establish an off-line SFR model using source data. Following the transfer learning principle, the transferred data from the source data set is determined by the maximum mean discrepancy (MMD) criterion. The RBFNN-based SFR model is then fine-tuned using both the transferred source data and target data. Finally, the fine-tuned RBFNNs is applied to investigate real-time SFR of WPTPSs. Simulation results confirm the effectiveness of the proposed SFR modelling strategy with an illustrative WPTPS

Compliance verification methodology for renewable generation integration. Application to island power grids

261 p.This thesis proposes a new methodology to validate the integration of renewable generation to install in island power grids. In weak power grids, the penetration of non-synchronous power generation can be challenging. Furthermore, system operators often impose strict technical requirements. In order to streamline grid code compliance verification, this thesis presents a simulation based procedure focused on most critical rules in isolated power grids: Frequency Ride-Through, Low Voltage Ride-Through and voltage and current unbalance. The methodology presented in this thesis proposes a generic and reduced grid model as equivalent system suitable for both simulating the static and dynamic performance of a selected power system for interconnection and design purposes, and for verifying the compliance of aforementioned technical requirements. Depending on the disturbance to be represented and on sensitivity studies of the model parameters, the generic grid model must be then particularised, in order to obtain a particular grid model. Finally, the grid model has to be parameterised based on grid characteristics and grid code limits, resulting into a parameterised grid model. In the present thesis, the methodology is applied to three study cases, where the installation of a renewable power plant is under study: a medium size island grid, Terceira island in the Açores and Fuerteventura-Lanzarote system. The numerical application to these three study cases backs the validity of the methodology proposed in the present thesis

Quantification and mitigation of the impacts of extreme weather on power system resilience and reliability

Modelling the impact of extreme weather on power systems is a computationally expensive, challenging area of study due to the diversity of threats, complicatedness of modelling, and data and simulation requirements to perform the relevant studies. The impacts of extreme weather – specifically wind – are considered. Factors such as the distribution of outage probability on lines and the potential correlation with wind power generation during storms are investigated; so too is sensitivity of security assessments involving extreme wind to the relationships used between failures and the natural hazard being studied, specifically wind speed. A large scale simulation ensemble is developed and demonstrated to investigate what are deemed the most significant features of power system simulation during extreme weather events. The challenges associated with modelling high impact low probability (HILP) events are studied and demonstrate that the results of security assessments are significantly affected by the granularity of incident weather data being used and the corrections or interpolation being applied to the source data. A generalizable simulation framework is formulated and deployed to investigate the significance of the relationship between incident natural hazards, in this case wind, and its corresponding impact on system resilience. Based on this, a large-scale simulation model is developed and demonstrated to take consideration of a wide variety of factors which can affect power systems during extreme weather events including, but not limited to, under frequency load shedding, line overloads, and high wind speed shutdown and its impact on wind generation. A methodology for quantifying and visualising distributed overhead line failure risk is also demonstrated in tandem with straightforward methods for making wind power projections over transmission systems for security studies. The potential correlation between overhead line risk and wind power generation risk is illustrated visually on representations of GB power networks based on real world data.Open Acces

Modern Power System Dynamic Performance Improvement through Big Data Analysis

Higher penetration of Renewable Energy (RE) is causing generation uncertainty and reduction of system inertia for the modern power system. This phenomenon brings more challenges on the power system dynamic behavior, especially the frequency oscillation and excursion, voltage and transient stability problems. This dissertation work extracts the most useful information from the power system features and improves the system dynamic behavior by big data analysis through three aspects: inertia distribution estimation, actuator placement, and operational studies.First of all, a pioneer work for finding the physical location of COI in the system and creating accurate and useful inertia distribution map is presented. Theoretical proof and dynamic simulation validation have been provided to support the proposed method for inertia distribution estimation based on measurement PMU data. Estimation results are obtained for a radial system, a meshed system, IEEE 39 bus-test system, the Chilean system, and a real utility system in the US. Then, this work provided two control actuator placement strategy using measurement data samples and machine learning algorithms. The first strategy is for the system with single oscillation mode. Control actuators should be placed at the bus that are far away from the COI bus. This rule increased damping ratio of eamples systems up to 14\% and hugely reduced the computational complexity from the simulation results of the Chilean system. The second rule is created for system with multiple dynamic problems. General and effective guidance for planners is obtained for IEEE 39-bus system and IEEE 118-bus system using machine learning algorithms by finding the relationship between system most significant features and system dynamic performance. Lastly, it studied the real-time voltage security assessment and key link identification in cascading failure analysis. A proposed deep-learning framework has Achieved the highest accuracy and lower computational time for real-time security analysis. In addition, key links are identified through distance matrix calculation and probability tree generation using 400,000 data samples from the Western Electricity Coordinating Council (WECC) system

Frequency regulation for power systems with renewable energy sources

Both the increasing penetration of renewable sources and their participation in the production of power in the electrical system require a more comprehensive analysis of the dynamic behavior of the grid frequency regulation structure. In this sense, this work presents the use of Control Sensitivity Functions to describe the dynamical characteristics of both primary and secondary control loops in frequency regulation. Bode plots are employed as a visualization and analysis tool. These sensitivity functions are applied to study the behavior of the power system with the contribution of wind turbines through the inertia emulation techniques. In this regard, the effects of inertia variations in frequency control are addressed for power systems under the integration of wind units. The transfer functions of the system are obtained starting from a linearized wind turbine model. The mathematical relationships are formulated to analyze the sensitivity and stability regarding inertia coefficient H. These expressions are then verified through simulation of several cases under different stability conditions and disturbances in wind speed and loadResumen: Tanto la creciente penetración de fuentes renovables de energía como su participación en el despacho de suministro energético en el sistema de potencia requiere un análisis completo del comportamiento dinámico de la estructura de regulación de frecuencia. En este sentido, esta tesis presenta el uso de las Funciones de Sensibilidad de Control para describir las características dinámicas de los lazos primario y secundario de regulación de frecuencia en sistemas de potencia, utilizando diagramas de Bode como herramienta de visualización y análisis. Estas funciones de sensibilidad se aplican en el estudio del comportamiento dinámico de la regulación en frecuencia con contribuciones de turbinas eólicas a través de las técnicas de emulación inercial. Bajo este escenario, los efectos de las incertidumbres o variaciones en la inercia son estudiados desde la integración de las turbinas eólicas en la estructura de control. Partiendo de una representación lineal del sistema, se proponen las formulaciones matemáticas necesarias para analizar la sensibilidad y la estabilidad del sistema con respecto a los cambios en la inercia. Estas expresiones se verifican a través de simulación de varios casos bajo diferentes condiciones de estabilidad y perturbaciones en la velocidad del viento y en la carga del sistemaDoctorad