Engineering topics

Liu, Yilu

Publications and source records attributed to Liu, Yilu.

27 records · Page 2

DOE OSTI2024

Mitigating Voltage Instability in the Saudi Grid for a Decarbonized, Fully Solar Power System

Lately, solar photovoltaic (PV) has received significant interest due to its economic and environmental benefits. As the integration of renewable energy sources (RES) increases into existing power grids, challenges such as the decrease in short circuit ratio (SCR) are introduced. Here, this paper investigates a case study of the Saudi power grid, examining the voltage stability as the grid transitions from traditional power generation to 100% penetration of solar PV gradually. Moreover, the paper explores the relationship between the increase in solar penetration and the potential effects on the short circuit MVA (SCMVA), which could significantly impact the overall SCR of the system. To meet the North American Electric Reliability Corporation (NERC) recommendation of maintaining SCR at a particular level, synchronous condensers (SC)s were integrated into the grid. The effectiveness of utilizing SCs to maintain the system voltage at optimal levels in a fully solar grid is also considered. In addition, this paper covers the weak grid analysis via utilizing the Newton-Raphson load flow method, along with PV and QV curve analyses. The purpose behind that is to determine weak bus locations in need of voltage improvements, and to meet the amount of reactive power to be injected. Finally, the amount of power being delivered by SCs will be added progressively in three scenarios to show the enhancements on SCR more precisely.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

random forest↗

DOE OSTI2024

Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) (Summary Report)

The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a comprehensive analysis based on extensive stakeholder input of possible pathways for Puerto Rico to achieve its goal of 100% renewable energy by 2050. In this summary report of the PR100 Final Report, we describe the background and motivation behind the study, provide an overview, summarize results, highlight key findings, and outline implementation actions for stakeholders to take in the immediate-, near-, mid-, and long-term to achieve Puerto Rico’s energy system goals.

100% renewable energy target↗

DOE OSTI2024

Estudio de Resiliencia de la Red Electrica de Puerto Rico y Transiciones a Energia 100% Renovable (PR100): Informe resumido [Spanish Translation of Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) (Summary Report)]

100% renewable energy target↗

DOE OSTI2024

Presentacion del Webinar Publico sobre Resultados Finales del Estudio PR100

Esta presentacion incluye un resumen de los resultados finales del Estudio de Resiliencia de la Red Electrica de Puerto Rico y Transiciones a Energia 100% Renovable (PR100). Dirigido por el Departamento de Energia y el Laboratorio Nacional de Energias Renovables, y financiado por la Agencia Federal para el Manejo de Emergencias, el estudio PR100 fue un esfuerzo de dos anos que ha resultado en caminos impulsados por las partes interesadas para que Puerto Rico alcance sus metas de energia limpia. For the English version of this presentation, see NREL/PR-6A20-88773 (https://www.nrel.gov/docs/fy24osti/88773.pdf).

100% renewable energy target↗

DOE OSTI2024

Fault-tolerant grid frequency measurement algorithm during transients

A system determines the frequency of grid signals corresponding to an electrical grid in real time. The system includes a transient detector that monitors a grid signal from a voltage meter or a current meter connected to the electrical grid. The system produces, in real time and at a sampling rate, a deviation signal indicative of a periodicity of the monitored grid signal. The system determines, over one or more cycles of the monitored grid signal, a measurement signal corresponding to the deviation signal. The system determines a frequency signal that corresponds a frequency estimation of the monitored signal by applying a frequency estimation when values of the measurement signal are less than a deviation threshold and maintaining the frequency signal at a constant value when values of the measured signal equal or exceeds the deviation threshold.

Zhan, Lingwei↗

DOE OSTI2024

Real-Time Inertia Estimation Tool Implementation Based on Probing Signals

As renewable energy penetration increases and the traditional generators retire in power grids, system inertia decreases and exhibits significant daily fluctuations. Furthermore, fast frequency responses (FFRs) provided by the inverter-based resources (IBRs) begin to playa very critical role and bring new challenges to real-time system inertia monitoring due to the difficulties of quantifying its artificial inertia contribution. Thus, necessitating an accurate real-time inertia estimation tool will not only benefit the secure power grid operations, but also provide insights on assessing the artificial inertia contribution from the IBRs. This paper presents a probing-based real-time inertia estimation tool that has been validated through a power-hardware-in-the-Ioop (PHIL) test system using identical hardware battery energy storage system (BESS) and control in an actual power grid. Preliminary results indicate high estimation accuracy of the developed tool and pave the way for the field test and deployment.

inverter-based resources↗

DOE OSTI2024

Mitigating commutation failures in HVDC systems with SFCL deployment

Commutation failures represent a prevalent issue encountered in line-commutated-converter high voltage direct current (LCC-HVDC) systems. As the widespread deployment of HVDC systems continues, the risk associated with commutation failures increases, posing a growing threat to power grids due to their potential to trigger severe consequences, including cascading failures and widespread blackouts. This research paper aims to address the significant issue of commutation failure within direct current (DC) systems through advocating for the use of resistive-type Superconducting Fault Current Limiters (R-SFCLs). To substantiate the efficacy of this proposed strategy, an array of simulations are executed using the PSCAD/EMTDC software. This comprehensive study investigates the performance characteristics of R-SFCLs configured with varying resistance values, scrutinizing their response under diverse fault resistance scenarios and distinct fault initiation times within the LCC-HVDC system. The outcomes of these simulations are that SFCLs confer significant advantages for mitigating commutation failures, surpassing traditional mitigation methods in terms of effectiveness. Consequently, SFCLs emerge as an optimal solution to prevent commutation failures in the HVDC systems.

Commutation failure↗

DOE OSTI2023

ML-Based Power System Stability Assessment Considering Network Topology Changes: WECC 20,000+ Bus System Case Study

Modern power grids are fast-changing and thus require real-time monitoring and online stability assessment. With the rapid development of machine learning (ML) techniques, using data-driven models to provide fast and accurate estimations of power system stability marginal information, such as frequency nadir for frequency stability and critical clearing time (CCT) for transient stability, have become possible. However, despite the numerous research on ML-based methods for frequency nadir and CCT prediction, there is limited work on the impact of different network topology changes. Furthermore, most previous studies only focused on small or synthetic systems, and there is a lack of research on actual large power system models. In this paper, the above issues are addressed by studying the actual U.S. Western Electricity Coordinating Council (WECC) system model with more than 20,000 buses. Massive simulations are conducted in PowerWorld Simulator to study the impact of various topology change scenarios on both frequency stability and transient stability. System operating information is extracted from the success dispatch cases of various network topologies to generate a comprehensive dataset for ML-based models. Two ML methods, random forest (RF) and multilayer perceptron (MLP) neural network, are trained and tested for both frequency nadir prediction and CCT prediction. Test results have proven the models are capable of online stability assessment for large power networks such as the WECC system with sufficient accuracy.

critical clearing time↗