North American Electric Reliability Corporation Research Articles

Enhanced solar and wind potential during widespread temperature extremes across the U.S. interconnected energy grids

Several recent widespread temperature extremes across the United States (U.S.) have been associated with power outages, disrupting access to electricity at times that are critical for the health and well-being of communities. Building resilience to such extremes in our energy infrastructure needs a comprehensive understanding of their spatial and temporal characteristics. In this study, we systematically quantify the frequency, extent, duration, and intensity of widespread temperature extremes and their associated energy demand in the six North American Electric Reliability Corporation regions using ERA5 reanalysis data. We show that every region has experienced hot or cold extremes that affected nearly their entire extent and such events were associated with substantially higher energy demand, resulting in simultaneous stress across the entire electric gird. The western U.S. experienced significant increases in the frequency (123%), extent (32%), duration (55%) and intensity (29%) of hot extremes and Texas experienced significant increases in the frequency (132%) of hot extremes. The frequency of cold extremes has decreased across most regions without substantial changes in other characteristics. Using power outage data, we show that recent widespread extremes in nearly every region have coincided with power outages, and such outages account for between 12%–52% of all weather-related outages in the past decade depending on the region. Importantly, we find that solar potential is significantly higher during widespread hot extremes in all six regions and during widespread cold extremes in five of the six regions. Further, wind potential is significantly higher during widespread hot or cold extremes in at least three regions. Our findings indicate that increased solar and wind capacity could be leveraged to meet the higher demand for energy during such widespread extremes, improving the resilience and reliability of our energy systems in addition to limiting carbon emissions.

Cybersecurity in Smart Grids: Protecting Critical Infrastructure from Cyber Attacks

The increasing digitalization of smart grids has brought numerous benefits in terms of efficiency, sustainability, and reliability, but it has also exposed these critical infrastructures to significant cybersecurity risks. This study investigates the cybersecurity challenges faced by smart grids, focusing on the vulnerabilities present in communication systems, control networks, and endpoint devices. The problem statement revolves around the growing complexity of smart grid infrastructures, which have become prime targets for cyberattacks such as denial-of-service (DoS), malware, and data breaches, threatening grid stability, data integrity, and national security. The primary objective of this study is to evaluate the current cybersecurity methods used in smart grids, such as encryption, firewalls, intrusion detection systems (IDS), and emerging technologies like artificial intelligence (AI) and blockchain. The study also aims to assess regulatory frameworks, such as the National Institute of Standards and Technology (NIST) and North American Electric Reliability Corporation Critical Infrastructure Protection (NERC CIP) standards and identify gaps in policy that need addressing. The results of the study demonstrate that AI-based anomaly detection systems significantly reduce attack detection times, averaging 2.5 seconds, with a low false positive rate of 7%. Blockchain technology, while increasing energy overhead by 15%, provides enhanced security for decentralized energy transactions. Multi-factor authentication (MFA) proved effective, blocking 98% of unauthorized access attempts. However, regulatory gaps were identified, especially in real-time monitoring and incident response. The study concludes that by adopting a layered security approach, integrating AI and blockchain technologies, and strengthening regulatory frameworks, smart grids can achieve a 75% improvement in cybersecurity efficacy. Continued efforts to address challenges such as legacy system integration, resource limitations, and evolving cyber threats are essential to securing the future of energy infrastructure.

Enhanced probabilistic contingency analysis considering fast ramping events of renewable generation

One of the major challenges in transmission planning is accounting for the impacts of renewable generation. Contingency Analysis (CA) is a technique used by utilities to meet transmission planning criteria mandated by the North American Electric Reliability Corporation (NERC). In existing CA practices, renewable generation is treated like conventional generation and does not consider the intermittency and fluctuations inherent to renewables. As renewable generation continues to grow, considering the impacts of renewable intermittency is becoming increasingly important. In this paper, an innovative approach is proposed to model the renewable intermittency as generator outages by, for the first time, introducing the concept of intermittency-induced outages (IIOs) for a single renewable plant and common mode outages (CMOs) for multiple renewable plants. A probabilistic outage model features different outage modes and the associated parameters that can be derived based on historical renewable generation data. The IIOs and CMOs can be seamlessly integrated into the existing probabilistic contingency analysis (PCA) framework. This paper presents the details for modeling and parameterization of the renewable outage modes. Such probabilistic outage models have been implemented using a Python-based enhanced PCA (ePCA) tool that drives and enhances the capability of the built-in PCA module of the PSS/E software platform, a widely used tool by the industry. Using the Western Electricity Coordinating Council (WECC) system, a proof-of-concept case study is presented and discussed to illustrate the unique impacts of increased penetration levels of renewables on transmission planning studies.

The cause of sub-cycle overvoltage: Capacitive characteristics of solar PVs

In 2017 and 2018, large-scale solar photovoltaic (PV) tripping events occurred after transmission grid disturbances. Sub-cycle overvoltage is identified as the main cause that the PV inverters’ protection systems initiated tripping actions. According to the reports from North American Electric Reliability Corporation (NERC), overvoltage is more severe at the PV inverter buses compared to the point of measurement (POM). Mechanism analysis of such a feature is an open research problem in the NERC community. This paper fills the gap. The goal of this paper is to explain why inverters may experience more severe overvoltage compared to the POM bus, which usually has shunt compensation installed. Through circuit construction with inverter control included, this paper will analyze and demonstrate the role of inverter control in aggravating overvoltage due to its capacitive nature in the subcycle dynamic range. While this nature is well known to the power electronics community, the current paper connects the dots to demonstrate how the capacitive nature of inverters can influence solar PV operation in a grid. A quantitative analysis is presented to explain the overvoltage mechanism.

Regional analysis of aluminum and steel flows into the American automotive industry

AbstractAluminum and steel represent the two most dominant metals in light‐duty vehicles, yet the flows of these materials into the American automotive industry have not been well characterized. This study proposes and implements a method for analyzing the flow of these metals into the automotive industry. We create a framework for performing regionally linked, sector‐specific material flow analyses and use this framework to trace flows of aluminum and steel entering the American automotive industry, focusing on flows downstream from raw material production. We show that automotive aluminum sheet and extrusions are sourced primarily from the NPCC (23%), SERC (20%), MRO (18%), and RFC (13%) North American Electric Reliability Corporation (NERC) regions, and a spatially unresolved local region within the United States and Canada (18%). We determine that primary aluminum is largely from Canada (70%), nearly all from Quebec (69%). Further upstream, alumina and bauxite originate mostly from Brazil, Australia, and Jamaica. We also show that finished automotive steel is sourced primarily from the RFC (63%) and SERC (20%) regions. The crude steel supply similarly originates mainly from the RFC (69%) and SERC (7%) regions. Upstream raw materials including coke, coking coal, iron ore, lime, and steel scrap are primarily sourced from the United States with only direct reduced iron and pig iron used in electric arc furnace steel production coming mostly from outside the United States. The framework developed here allows for increased spatial resolution of material flows, which can be used to develop more specific life cycle impact factors for life cycle assessments.

Protecting the Texas electric grid: A cybersecurity strategy for the Electric Reliability Council of Texas (ERCOT) and the Public Utility Commission of Texas (PUCT)

AbstractThe electrical system serves as the fundamental base of a country's economic activity, and is a likely target for cyberattacks. As the modern economy continues its evolution towards greater digitization and interconnectedness, policymakers must outline and enforce regulations protecting those critical assets, without which the economy would suffer. The Texas Interconnection, due to being independently operated, and to a large‐extent legislated by the state of Texas, enjoys a simpler regulatory environment than its counterparts in the rest of the contiguous 48 states. We exploit this relative independence to offer policy solutions to better protect Texas against a cyberattack on its electric grid. Specifically, ERCOT and the PUCT have the authority and ability to streamline and simplify the potentially confusing protocols enforced by the North American Electric Reliability Corporation (NERC) to make adoption more likely. We also discuss grant programs for smaller utilities and the role of cyber‐insurance in helping utilities navigate the difficulties in understanding the protocols so that compliance can occur.

PMU Based Problematic Parameter Identification Approach for Calibrating Generating Unit Models

Accurate model of generating unit plays a key role in power system modeling and simulation. To ensure the safe and stable operation of power grids, North American Electric Reliability Corporation requires that all generators larger than 10 MVA have to perform the model verification in every five years. However, the traditional key parameter identification based model calibration method cannot guarantee the accurate correction of the generator's problematic parameters, which have errors with their real values. The effectiveness of this kind model calibration method is still in doubt. To address the issues, this article developed a problematic-parameter-identification based generating unit model calibration approach. Trajectory sensitivity based sensitivity analysis is applied to determine the problematic parameter candidates (PPCs), which determine the dynamic responses of the system. Then, the Hilbert spectrum of PPCs and model output error curves are compared in time-frequency domain to screen out the problematic parameters. Also, the selected problematic parameters are calibrated with the measurements based identification technique. The effectiveness of the proposed method has been validated throughout a series testing cases from an actual hydropower plant integrated power system.

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

Quantifying the challenge of reaching a 100% renewable energy power system for the United States

An RTDS-Based Testbed for Investigating the Impacts of Transmission-Level Disturbances on Solar PV Operation

The increasing penetration of renewable energy resources such as solar and wind via power electronic inverters is challenging grid dynamics, as well as grid planning, operation, and protection. Recently, the North American Electric Reliability Corporation (NERC) has reported a series of similar events of the unintended loss of solar generation in Southern California over a large geographic area following the transmission-level disturbances. These events highlight the importance of understanding the characteristics of the transmission-side disturbances propagating into the distribution systems and their impacts on the operation of inverter-based resources. In this paper, a real-time electromagnetic simulation testbed is constructed for real-time electromagnetic simulations to generate realistic transmission-level disturbances and investigate their impacts on the solar PV operation under different fault types and locations, solar penetration levels, and loading levels. Through the simulation analysis and grid strength assessment, it is found that the grid strength at points of integration (POIs) of solar PVs significantly affects the transient stability of solar generators. Particularly, undesirable transient stability events are more likely to occur at the weak POIs following the transmission-level disturbances. Moreover, undesirable transient stability events become severer when the transmission-level disturbance is closer to the weak POIs or the disturbances become more serious. Additionally, the impact of the transmission-level disturbances on the solar PVs at the weak POIs exacerbate with the increasing solar penetration levels and loading levels. Thus, it is important to study and develop new technologies for grid planning, operation, and protection in weak grid conditions to address the emerging issues of integrating the high penetration of solar PVs and other IBRs.

Understanding key factors affecting power systems resilience

In this paper, we study the key factors that impact on power systems resilience under severe weather-induced disruptions from three dimensions: the extrinsic disruptions, the intrinsic capacities of a system and the effectiveness of recovery. Using 12 years of historical blackout data from 2007 to 2018 in the U.S., we apply various group selection and bi-level selection methods to identify the key predictor groups as well as factors within-group that affect power system resilience. After deleting the predictors which are fully or highly correlated with others, we split the remaining 39 candidate predictors into 8 natural groups and consider the number of customers affected and the recovery time as response variables. To ensure stability of the selection process, we adopt the random subsampling method to rank the importance of the groups and key predictors. It is found that the disruption types from the extrinsic disruptions dimension have a significant impact on the resilience of power systems, especially for the hurricanes with high scales. From the intrinsic capabilities dimension, the demographic group has a large impact on the number of customers affected. The number of customers affected tends to be large in highly urbanized areas with large population. From the effectiveness of recovery dimension, the group of economics is top selected for the recovery time. It is found that the power system tends to be more resilient with a better economic health. Feature selection under quantile regression is also conducted as the histograms show that the distributions of the responses are skewed and heavy-tailed. It is found that the recovery time is also greatly affected by the investment on the compliance and enforcement program from the North American Electric Reliability Corporation. In summary, our analysis provides interesting insights for understanding power system resilience and developing strategies to enhance the resilience.

Construction of a Frequency Compliant Unit Commitment Framework Using an Ensemble Learning Technique

Frequency control is essential to ensure reliability and quality of power systems. North American Electric Reliability Corporation’s (NERC) Control Performance Standard 1 (CPS1) is widely adopted by many operating authorities to examine the quality of the frequency control. The operating authority would have a strong interest in knowing how the frequency-sensitive features affect the CPS1 score and finding out more effective unit-dispatch schedules for reaching the CPS1 goal. As frequency-sensitive features usually possess multi-variable and high-correlated characteristics, this paper employed an ensemble learning technique (the Gradient Boosting Decision Tree algorithm, GBDT) to construct Frequency Response Model (FRM) of the Taipower system in Taiwan to evaluate by CPS1 score. The proposed CPS1 model was then integrated with Unit Commitment (UC) program to determine the unit-dispatch that achieves the targeted CPS1 score. The feasibility and effectiveness of the proposed CPS1-UC platform were validated and compared with the other benchmark model-based UC methods by two operating cases. The proposed model shows promising results: the system frequency could be maintained well, especially in the periods of the early morning or the high renewable penetration.

What causes natural gas fuel shortages at U.S. power plants?

Using 2012–2018 power plant failure data from the North American Electric Reliability Corporation, we examine how many fuel shortage failures at gas power plants were caused by physical interruptions of gas flow as opposed to operational procedures on the pipeline network, such as gas curtailment priority. We find that physical disruptions of the pipeline network account for no more than 5% of the MWh lost to fuel shortages over the six years we examined. Gas shortages at generators have caused correlated failures of power plants with both firm and non-firm fuel arrangements. Unsurprisingly, plants using the spot market or interruptible pipeline contracts for their fuel were somewhat more likely to experience fuel shortages than those with firm contracts. We identify regions of the Midwest and Mid-Atlantic where power plants with non-firm fuel arrangements may have avoided fuel shortage outages if they had obtained firm pipeline contracts. The volume of gas needed by power plants to fuel the lost MWh in those regions was only a small fraction of the total volume delivered to potentially non-essential commercial and industrial pipeline customers in those regions and modest prices there at the times when power plants failed indicate gas was available.

Transmission Line Protection for Systems With Inverter-Based Resources – Part I: Problems

Inverter-based resources respond differently than synchronous generation sources during power system faults, challenging line protection reliability. To understand the reliability challenges, Sandia National Laboratories and North American Electric Reliability Corporation (NERC) led a collaborative study with four inverter manufacturers (mix of Type 3 Wind, Type 4 Wind, and PV Solar) and two North American protective relay manufacturers. As part of this study, an electromagnetic transients program (EMTP) with “real-code” models representing inverter firmware was used to investigate the protective relay response to faults on the modeled power system with the different inverter types. The currents and voltages obtained from the study were then played back through line relay hardware with settings typical of systems with conventional generation. The protective relays exhibited the expected reliability challenges but provided insight into the behavior of inverter-based resources during power system faults. In this paper, we share some of the challenging cases with the responses of the different line protection schemes. Using insight from the various event records, Part II of this paper shows how relay settings can be modified to obtain a substantial improvement in reliability.

Evaluation of Simulation Methods for Analysis of Geomagnetic Disturbance System Impacts

Geomagnetic Disturbance (GMD) impacts a power system by causing the circulation of quasi-dc Geomagnetically-induced Currents (GICs) in transmission lines and high-voltage transformer windings leading to transformer saturation, increased reactive power losses, and voltage regulation problems. Utility planners use various analysis methods and simulation tools to analyze GMD system impacts. These techniques can be broadly categorized into load-flow-based (LF), transient stability type (TS), and electromagnetic transient type (EMT) methods. These methods are based on different modeling assumptions and solution techniques. There is a need for evaluation of these assumptions and cross-examination of results to ensure their accuracy. Such an evaluation is essential to ensure that planners performing required GMD vulnerability assessments have confidence in the results. This paper evaluates the LF, TS, and EMT methods through cross-examination of their results. The objective is to identify their limitations, assess the consistency of their results, and provide assumptions on their use for analysis of GMD system impacts. The study is conducted in consistence with the North American Electric Reliability Corporation (NERC) GMD guidelines and standards.

A Risk-Based Approach to Assess the Operational Resilience of Transmission Grids

Modern risk analysis studies of the power system increasingly rely on big datasets, either synthesized, simulated, or real utility data. Particularly in the transmission system, outage events have a strong influence on the reliability, resilience, and security of the overall energy delivery infrastructure. In this paper we analyze historical outage data for transmission system components and discuss the implications of nearby overlapping outages with respect to resilience of the power system. We carry out a risk-based assessment using North American Electric Reliability Corporation (NERC) Transmission Availability Data System (TADS) for the North American bulk power system (BPS). We found that the quantification of nearby unscheduled outage clusters would improve the response times for operators to readjust the system and provide better resilience still under the standard definition of N-1 security. Finally, we propose future steps to investigate the relationship between clusters of outages and their electrical proximity, in order to improve operator actions in the operation horizon.

A suggested approach to site storage resources in transmission planning models

Abstract

Assessment of Impact of AC Circuit Attributes to Outage Frequency in the WECC System

This paper develops a statistics-based approach for analysis of transmission outage data. The method is applied to the Western Electricity Coordinating Council (WECC) transmission inventory and outage data collected and submitted by member utilities to the North American Electric Reliability Corporation's (NERC) Transmission Availability Data System (TADS). The developed model considers the random nature of Alternating Current (AC) circuit outages and introduces fourteen inventory attributes of the AC circuits as explanatory variables for the outage frequency. The main goal of the paper is to investigate statistical relationships between the number of automatic outages on AC circuits and AC circuit inventory attributes based on the TADS data from 2013–2018, which has proven to be sufficient in obtaining statistically valid results. Concepts associated with the statistical validation of performance indices are discussed with emphasis placed on analysis of distinct types of explanatory variables such as voltage class, mileage, number of terminals, circuits per structure, terrain, elevation, etc.

Verification of Utility-Scale Solar Photovoltaic Plant Models for Dynamic Studies of Transmission Networks

In recent years, there has been a growing need for accurate models that describe the dynamics of renewable energy sources, especially photovoltaic sources and wind turbines. In light of this gap, this work focuses on the validation of standard dynamic models developed by the Western Electricity Coordinating Council (WECC), using actual measurements from the Western Texas and Southern California transmission networks. The tests are based on the North American Electric Reliability Corporation compliance standards and include dynamic stability tests for volt-varcontrol and primary frequency response. Through an extensive set of field tests, we show that the WECC generic models can be used to simulate real dynamic phenomena in large-scale solar photovoltaic power plants, and we propose guidelines for correct usage of these models. The results show that the WECC models are especially accurate when the photovoltaic system is connected with a low impedance to the main network. We also show that the tested WECC models successfully predict the frequency response of an actual grid event that occurred in the Electric Reliability Council of Texas and which resulted in a loss of nearly 1.365 GW. This result supports the use of these models in the study of large-scale dynamic phenomena that include renewable energy sources.

Power System NERC Reliability Compliance Study and Violation Mitigation

Power system contingency analysis plays a pivotal role in identifying potential weak spots and ensuring reliable operation of power systems. Voltage stability is a critical aspect of performance evaluation of power system reliability. The North American Electric Reliability Corporation demands utilities to perform compliance studies to make sure that their systems meet the requirements of adequacy and security aspects of reliability. This paper presents contingency studies performed for a real utility system. The voltage stability criteria are defined, and a general software package is used to perform analysis including regular ACCC analysis, PV analysis, QV analysis, voltage sensitivity analysis, and dynamic studies. The analysis can be carried out manually. In order to facilitate analysis and to avoid tedious steps of running the simulation, recording results, and processing and reporting results, software tools are developed to automate all sorts of tasks and steps based on Python language. The studies have demonstrated that the software tools are efficient for carrying out contingency studies. Example results are reported in the paper in order to illustrate typical study results for a real power utility system.

Advanced techniques of power system restoration and practical applications in transmission grids

Optimal restoration of a real-time power system following a disruption is a complex process. In view of that and with increase in frequency and severity of power system outages across the US and their impact on consumers and utilities, North American Electric Reliability Corporation elevated the standard of compliance for power system restoration. While several utilities have proposed solutions addressing the elevated standards based on dynamic programming, they could not address the issues for large-scale power systems and real-time operations including those using steady-state and transient analysis due to the curses of dimensionality. In this paper, we introduce a restoration process based on approximate dynamic programming integrated with solution space reduction methodology. The main theoretical foundations of the methodology and key algorithms used in this restoration process include the knowledge of power system engineering, complex network science, concepts of graph theory, and dynamic programming in operation research. We first use the proposed method to demonstrate an optimal restoration process on the IEEE 118-bus test system and then on a 2000-bus synthetic test system. This method addresses the concerns related to the curses of dimensionality and simplifies the solution space and thus can be applied to various complex real-time operation settings.