The Power Electronics Grid Interface (PEGI) Platform
PEGI is a research platform at NREL to validate inverter-based resources and power electronic-dominant energy systems.
grid-forming↗
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Results for “Power Grid”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Deep Learning-Based Data Forgery Detection in Automatic Generation Control
Automatic Generation Control (AGC) is a key control system in the power grid. It is used to calculate the Area Control Error (ACE) based on frequency and tie-line power flow between balancing areas, and then adjust power generation to maintain the power system frequency in an acceptable range. However, attackers might inject malicious frequency or tie-line power flow measurements to mislead AGC to do false generation correction which will harm the power grid operation. Such attacks are hard to be detected since they do not violate physical power system models. In this work, we propose algorithms based on Neural Network and Fourier Transform to detect data forgery attacks in AGC. Different from the few previous work that rely on accurate load prediction to detect data forgery, our solution only uses the ACE data already available in existing AGC systems. In particular, our solution learns the normal patterns of ACE time series and detects abnormal patterns caused by artificial attacks. Evaluations on the real ACE dataset show that our methods have high detection accuracy.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Design and Implementation of Real-Time Off-Grid Detection Tool Based on FNET/GridEye
Real-time situational awareness tools are of critical importance to power system operators, especially during emergencies. The availability of electric power has become a linchpin of most post disaster response efforts as it is the primary dependency for public and private sector services, as well as individuals. Knowledge of the scope and extent of facilities impacted, as well as the duration of their dependence on backup power, enables emergency response officials to plan for contingencies and provide better overall response. Based on real-time data acquired by Frequency Disturbance Recorders (FDRs) deployed in the North American power grid, a real-time detection method is proposed. This method monitors critical electrical loads and detects the transition of these loads from an on-grid state, where the loads are fed by the power grid to an off-grid state, where the loads are fed by an Uninterrupted Power Supply (UPS) or a backup generation system. The details of the proposed detection algorithm are presented, and some case studies and off-grid detection scenarios are also provided to verify the effectiveness and robustness. Meanwhile, the algorithm has already been implemented based on the Grid Solutions Framework (GSF) and has effectively detected several off-grid situations.
Guo, Jiahui↗
Permanent Magnet Synchronous Condenser for Wind Power Plant Grid Connection Support: Preprint
A synchronous condenser (SC) using a permanent magnet synchronous generator (PMSG) is proposed for providing necessary reactive power to a wind power plant to support its connection to a weak grid. A PMSG has the advantage of higher efficiency and reliability. Because of its lack of a field winding, a PMSG is typically controlled by a full-power converter, which can be costly. In the proposed system, the reactive power of the SC is controlled by a serially connected compensator operating in a closed-loop configuration. The compensator also damps the PMSG's tendency to oscillate. The compensator's VA rating is only a fraction of the rating of the SC and the PMSG. In this initial investigation, the proposed scheme is shown to be effective by computer simulations.
17 WIND ENERGY↗
The value of improved wind power forecasting: Grid flexibility quantification, ramp capability analysis, and impacts of electricity market operation timescales
The value of improving wind power forecasting accuracy at different electricity market operation timescales was analyzed by simulating the IEEE 118-bus test system as modified to emulate the generation mixes of the Midcontinent, California, and New England independent system operator balancing authority areas. The wind power forecasting improvement methodology and error analysis for the data set were elaborated. Production cost simulation was conducted on the three emulated systems with a total of 480 scenarios, considering the impacts of different generation technologies, wind penetration levels, and wind power forecasting improvement timescales. The static operational flexibility of the three systems was compared through the diversity of generation mix, the percentage of must-run baseload generators, as well as the available ramp rate and the minimum generation levels. The dynamic operational flexibility was evaluated by the real-time upward and downward ramp capacity. Simulation results show that the generation resource mix plays a crucial role in evaluating the value of improved wind power forecasting at different timescales. In addition, the changes in annual operational electricity generation costs were mostly influenced by the dominant resource in the system. Lastly, the impacts of pumped-storage resources, generation ramp rates, and system minimum generation level requirements on the value of improved wind power forecasting were also analyzed.
17 WIND ENERGY↗
Open Power System Datasets and Open Simulation Engines: A Survey Toward Machine Learning Applications
A major factor behind the success of machine learning (ML) models in multiple domains is the availability and accessibility of large, labeled, and well-organized datasets for training and benchmarking. In comparison, power grid datasets face three major challenges: (i) real-world data is often restricted by regulatory constraints, privacy reasons, or security concerns, making it difficult to obtain and work with; (ii) synthetic datasets, which are created to address these limitations, often have incomplete information and are released using specialized tools, making them inaccessible to the broader community; and, (iii) input-output datasets are difficult to generate through simulation for non-experts because open-source simulators are not known outside the power system community. This survey addresses these challenges by serving as an entry point to publicly available datasets and simulators for researchers venturing in this area. We review the current landscape of open-source power network data, machine models, consumer demand profiles, renewable generation data, and inverter models. We also examine open-source power system simulators, which are crucial for generating high-quality, high-fidelity power grid datasets. We aim to provide a foundation for overcoming data scarcity and advance towards a structured web of datasets and simulators to support the development of ML for power systems.
42 ENGINEERING↗
Simulation of hydropower at subcontinental to global scales: a state-of-the-art review
Abstract Hydroelectric power is playing a new and often expanded role in the world’s major power grids, offering low carbon generating capacity in industrializing, dam-building economies while providing reserve and flexibility to co-manage fledgling wind and solar resources in high income countries. Driven by river flows, conventional hydropower is exposed to the vagaries of weather and climate, motivating drought and climate change hydropower impact studies at large spatial scales. Here we review methods of climate-driven hydropower simulation at large spatial scales, specifically multi-basin regions to global. We identify four types of approach based on complexity of tools and richness of data applied to the problem. Since the earliest attempts to model climate-driven hydropower at continental scale almost two decades ago, the field has transitioned from one of scientific curiosity to practical application, with studies increasingly motivated by the need to inform power grid expansion planning and operation. As the hydrological and water management models used in large-scale hydropower studies become more sophisticated, new opportunities will emerge to study the impacts of changing hydropower on power system reliability and performance at large power grid scale. To grasp these opportunities, the water resources community must continue to enhance data and models for representing river flows and anthropogenic water use and management at subcontinental to global scales.
13 HYDRO ENERGY↗
Protecting Customer Privacy Through Distributed Energy Resource Anonymization
Due to their stochastic nature, the increase of Renewable Energy Resources (RERs) as a primary source of energy for power grids creates challenges regarding the reliability and resilience of the system. In order to combat these obstacles, expansion of Distributed Energy Resources (DERs) and their participation in Demand Response (DR) programs is necessary. Widespread participation requires prioritizing customer privacy and addressing concerns that may arise regarding communication between DERs and the Grid Service Provider (GSP). This paper discusses the use of flow reservation resources to split the operating cycles of DER load profiles into unique phases. The splitting of phases increases anonymization of the DERs by making it more difficult to determine the individual characteristics of the device. We discuss an example of this using simulated DER load profile data and examine the resulting effectiveness by using a machine learning algorithm for classification, called Support Vector Machine (SVM).
Distributed Energy Resource, Anonymization, Renewa↗
Electric Grid Expansion Planning with High Levels of Variable Generation
Renewables are taking a large proportion of generation capacity in U.S. power grids. As their randomness has increasing influence on power system operation, it is necessary to consider their impact on system expansion planning. To this end, this project studies the generation and transmission expansion co-optimization problem of the US Eastern Interconnection (EI) power grid with a high wind power penetration rate. In this project, the generation and transmission expansion problem for the EI system is modeled as a mixed-integer programming (MIP) problem. This study analyzed a time series creation method to capture the diversity of load and wind power across balancing regions in the EI system. The obtained time series can be easily introduced into the MIP co-optimization problem and then solved robustly through available MIP solvers. Simulation results show that the proposed time series generation method and the expansion co-optimization model and can improve the expansion result significantly after considering the diversity of wind and load across EI regions. The improved expansion plan that combines generation and transmission will aid system planners and policy makers to maximize the social welfare. This study shows that modelling load and wind variations and diversities across balancing regions will produce significantly different expansion result compared with former studies. For example, if wind is modeled in more details (by increasing the number of wind output levels) so that more wind blocks are considered in expansion planning, transmission expansion will be larger and the expansion timing will be earlier. Regarding generation expansion, more wind scenarios will slightly reduce wind generation expansion in the EI system and increase the expansion of other generation such as gas. Also, adopting detailed wind scenarios will reveal that it may be uneconomic to expand transmission networks for transmitting a large amount of wind power through a long distance in the EI system. Incorporating more details of renewables in expansion planning will inevitably increase the computational burden. Therefore, high performance computing (HPC) techniques are urgently needed for power system operation and planning optimization. As a scoping study task, this project tested some preliminary parallel computation techniques such as breaking down the simulation task into several sub-tasks based on chronology splitting or sample splitting, and then assigning these sub-tasks to different cores. Testing results show significant time reduction when a simulation task is split into several sub-tasks for parallel execution.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Large-Scale Grid Models: Synthetic Data and Co-Simulation
Power grid modernization and wide-spread distributed energy resource (DER) integration are driving an explosion of interest in advanced algorithms and new optimization schemes. However, there has historically been a lack of open-access realistically sized and detailed datasets-particularly for the distribution system-and simulation tools able to model these systems. This talk will present recent advances with very large-scale (>>1M nodes) distribution data sets and the use of co-simulation to tractably model large integrated transmission-distribution grids and their interactions with new controls/optimization.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Toward Statistical Real-Time Power Fault Detection
We propose statistical fault detection methodology based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The paper includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates.
bolted faults↗
Toward Real Time Data Analysis for Smart Grids
This paper describes the architecture and design of a novel system for supporting large-scale real-time data analysis for future power grid systems. The widespread deployment of renewable generation, smart grid controls, energy storage, plug-in hybrids, and new conducting materials will require fundamental changes in the operational concepts and principal components of the grid. As a result, the whole system becomes highly dynamic and requires constant adjusting based on real time data. Even though millions of sensors such as phase measurement units (PMU) and smart meters are being widely deployed, a data layer that can analyze this amount of data in real time is needed. Unlike the data fabric in other cloud services, the data layer for smart grids has some unique design requirements. First, this layer must provide real time guarantees. Second, this layer must be scalable to allow a large number of applications to access the data from millions of sensors in real time. Third, reliability is critical and this layer must be able to continue to provide service in face of failures. Fourth, this layer must be secure. We address these challenges though a scalable system architecture that integrates the I/O and data processing capability in a devise set of devices. Data process operations can be placed anywhere from sensors, data storage devices, to control centers. We further employ compression to improve performance. We design a lightweight compression customized for power grid data. Our system can reduce end-to-end response time by reduce I/O overhead through compression and overlap compression operations with I/O. The initial prototype of our system was demonstrated with several use cases from PNNL’s FPGI and show that our system can provide real time guarantees to a diverse set of applications.
Data Analytics, Power Grid, Big Data↗
Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems
The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.
DER integration↗
Introducing FNCS: Framework for Network Co-Simulation
This video provides a basic overview of the PNNL Future Power Grid Initiative-developed Framework for Network Co-Simulation (FNCS). It discusses the increasing amounts of data coming from the power grid, and the need for a tool like FNCS that brings together data, transmission and distribution simulators. Included is a description of the FNCS architecture, and the advantages this new open source tool can bring to grid research and development efforts.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Physics-informed heterogeneous graph neural networks for DC blocker placement
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Substation Configuration Survey for Electromagnetic Coupling Analysis
Impacts of a high-altitude electromagnetic pulse (HEMP) on the power grid are a growing concern due to the increased reliance on the power grid. A critical area of research is quantifying power system equipment response to HEMP since this is not known in general. Substation site surveys were performed at seven high voltage substations across the United States to gather substation layout and construction details pertinent to HEMP coupling calculations and component vulnerability assessments. The primary objective for the survey was to gather information on cable layouts and cable construction within substations. Additional information was also gathered on equipment present within the substations and control house layouts. This report provides information gathered from the substation surveys.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Induced Markov chain for wind farm generation forecasting
Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.
17 WIND ENERGY↗
Interdisciplinary Education through “Edutainment”: Electric Grid Resilient Control Systems Course
As energy companies and governments attempt to get more from the existing power grid and other critical infrastructures, more automatic control systems are being applied 1-2. With this greater reliance on network-based, digital automation and the stresses of pressing existing infrastructure for greater performance, the power grid and underlying systems have become more susceptible to both malicious attacks and unexpected, natural threats. Governments and other stakeholders have chosen to address infrastructure issues by the implementation of a smarter grid. In the smart grid, operators and control systems supervise power generation, distribution, transmission, and loads to utilize these assets most efficiently3. Such extensive monitoring and control over a distributed system cause complexity that challenge systems designers and human operators in new ways. In addition, cyber vulnerability of these systems has been illustrated in many recent articles on malicious attacks to electric power systems and other similar infrastructure for natural gas, water and communications4. It is therefore critical in the next generation of control systems that resilience plays a large and critical role in the grid design and development. Resilient control systems is a field of research that seeks solutions to the complexity through a holistic approach that combines cognitive science, computer security, communications, and control systems. To enable future researchers and practitioners to assist with designing more resilient systems, science, technology, engineering and mathematics education needs to incorporate interdisciplinary topics. While electrical engineering and computer science programs in the nation include a cyber-security perspective, few if any have focused on the unique control system aspects. Human cognitive aspects are most definitely not addressed in technology education discourse. To this end, a class and education tools in resilient control systems have been created.
17 WIND ENERGY↗