Massively Digitized Power Grid: Opportunities and Challenges of Use-Inspired AI
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14 SOLAR ENERGY↗
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GMLC Southeast Regional Consortium: Technology Driven Improvements to Power Grid Resiliency
Recognizing that electric grid challenges can differ by geographical regions across the US, the Department of Energy (DOE) created a pioneer regional partnership to provide technical assistance to stakeholders facing grid modernization challenges. The southeast regional teams will transfer technologies developed under DOE auspices to stakeholders for testing, evaluation, and market validation. Regional members will help define critical challenges and pave the way for transition of technologies to the commercial sector. Using the depth and expertise at Oak Ridge National Laboratory, Savannah River National Laboratory, University of Tennessee’s CURENT, CAPER and the Clemson 20 MVA Electrical Grid Laboratory—along with critical industry partners, such as Duke Energy, TVA, Southern Company, EPB, and Santee Cooper—the Southeast Regional Consortium team will conduct collaborative research to advance the state of the grid in the Southeast region and improve power system resiliency to enhance responsiveness, enable faster restoration of power, and increase distributed energy resource (DER) concentrations. This report provides details of the technical research and demonstration projects accomplished by Oak Ridge National Laboratory (ORNL) as part of the Southeast Regional Consortium. Specifically, ORNL analyzed the application of optical current and voltage measurements impedance relays which would enable step distance protection to be deployed on distribution systems. This would improve the precision of determining the geographic location of faults, increase network topology flexibility, and allow bi-directional power flow. Next, ORNL deployed time sensitive networking technology on the EPB fiberoptic data network to study the impact of increased sensor traffic on existing supervisory control and data acquisition data. The deterministic latency and high bandwidth were demonstrated by remotely inspecting a substation using a drone with full HD streaming video operated at the EPB Control Center. Finally, a new application-based communication and control system developed at ORNL call CSEISMIC was tested for future deployment on the EPB Control Center microgrid using a hardware-in-the-loop testbed that was also developed at ORNL. The controller was able to successfully optimize ancillary service when the microgrid is connected to the distribution network. The seamless islanding, resynchronization, and reconnection of the microgrid was also demonstrated through the testing.
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Inertia and the Power Grid: A Guide Without the Spin
This guide provides an overview of inertia’s role in the evolving power system. It describes the sources of inertia in the grid, the intrinsic interplay of inertia and other grid services, the factors that impact the amount of and need for inertia, and the changes that can be made to maintain reliability as it declines. The guide is intended for nontechnical audiences. And it introduces accessible examples to illustrate the concepts of inertia and other grid services that are required to maintain system frequency, which is a key indicator of health in the current grid. The guide also discusses alternatives to traditional inertia and provides several takeaways that may be informative to power system decision makers, planners, operators, analysts, and other stakeholders.
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An Overview of Renewable Energy Desk Activities for Power Grid Operations and Planning
This document summarizes how grid operators can address gaps in their planning and operations to maintain reliability as they pursue clean energy goals. When transitioning to higher renewable energy levels, many system operators configure a dedicated renewable energy desk to manage variable renewable energy resource operation. Establishing such a desk in the control room can be a key step in the modernization effort. A renewable energy desk in a control room is a specialized hub focused solely on monitoring, predicting, and managing the influx of energy from renewable sources.
ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗
How the U.S. Power Grid Kept the Lights on in Summer 2024
Maintaining the reliability of the bulk power system, which supplies and transmits electricity, is a critical priority of electric grid planners, operators, and regulators. As demand for electricity increases and the U.S. resource mix changes, how grid operators meet peak demand is changing. In summer 2024, grid operators in all regions maintained enough capacity to keep the lights and air conditioners on during periods of peak demand, even as older generators have been retired. And, an increasing number of regions used more solar and storage to meet peak demand. In this publication, we describe grid operations on the highest demand day in ERCOT and a few other regions and how solar and storage in particular worked together to help meet peak demand.
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Physics Informed Reinforcement Learning for Power Grid Control using Augmented Random Search
Wide adoption of deep reinforcement learning need to overcome several challenges in energy system domain, including scalability, learning from limited samples, and high-dimensional continuous state and action spaces. In this paper, we integrated physics-based information from the normal generator operation state formula in the reinforcement learning agent's neural network loss function, and applied an augmented random search agent to optimize the generator control under dynamic contingency. Simulation results demonstrated the reliability performance improvements in training speed, reward convergence, sampling efficiency, scalability, and transferability.
physics informed ML, Physics Informed Neural Netwo↗
Probabilistic Grid Reliability Analysis with Energy Storage Systems (ProGRESS): An Open-Source Tool for Assessing Reliability of the Electric Power Grid
Explore the source record for details and available documents.
Bera, Atri↗
Method of Using Power Grid as Large Antenna for Geophysical Imaging
A high-voltage power transmission system is used as an extremely large antenna to extract spatiotemporal space, physical, and geological information from geomagnetically induced currents (GIC). A differential magnetometer method is used to measure GIC and involves acquiring line measurements from a first fluxgate magnetometer under a high-voltage transmission line, acquiring natural field measurements from a reference magnetometer nearby but not under the transmission line, subtracting the natural field measurements from the line measurements, and determining the GIC-related Biot-Savart field from the difference. NASA warning and alarm systems can be triggered based on determinations of GIC amplitude levels that exceed a set threshold value.
Pulkkinen, Antti A.↗
A Study of Emerging Ancillary Service Markets in Non-Restricted Regions of the Western Power Grid (CRADA Final Report)
Rising penetrations of renewable energy generation in the Western United States pose new requirements for ancillary services, which are the services required, in addition to energy service, in order to maintain the system reliability. This project combines the research capabilities of the National Renewable Energy Laboratory (NREL) and the University of Colorado at Boulder (CU-Boulder) to provide more accurate information to market participants and regulators about the present and future size and composition of ancillary service markets in the non-restructured regions of the West. This information would provide market participants and state and federal regulators with a more transparent view of future ancillary service markets, and allow for more efficient system planning in both the private and public sectors of the electricity market.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Big Data Processing for Power Grid Event Detection
In this paper we present the application of big data processing for the development of machine learning(ML) models to detect relevant events in power gridoperations. This is based on almost 20TB of phasormeasurement unit data corresponding to up to two years of operation of three grid interconnections which provide power to most of the United States. A significant aspect of the work consists in having all data processing performed on a single standard GPU server, from pre-processing to ML model training and testing. We describe the data and computational infrastructure, challenges faced and methods used in dataprocessing, main findings and results. The ML approach employed for best utilization of the big data is also discussed, including sample results.
Paes Leao, Bruno↗
Distributed Coordination of Networked Microgrids for Voltage Support in Bulk Power Grids
The increasing deployment of distributed energy resources (DERs) and microgrids (MGs) in power distribution systems has enabled the adjustment of reactive power consumption as seen at the substation, which can be used to provide voltage support for the bulk power system (BPS). Leveraging this new capability will provide greater resiliency to the power system as a whole. Here, the goal of this paper is to develop and compare three different algorithms, namely distributed optimal power flow, distributed consensus algorithm, and fully decentralized collaborative autonomy for unbalanced distribution systems for microgrid coordination. These algorithms use networked MGs to support the BPS voltage when a contingency at the bulk grid results in abnormally low voltages, which may be a precursor to voltage collapse. Our comparative analysis includes both qualitative and quantitative assessments of the three algorithms and a discussion of the trade-offs between the decentralized and distributed methods in normal and disrupted conditions. Each algorithm was evaluated on the modified IEEE 13-bus system and a real power distribution system at Chattanooga, Tennessee, that encompasses more than 4500 buses. Each algorithms excels differently and may be suited for different scenarios depending on the condition, operations, and priorities of the power and communication systems.
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Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables
Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.
affinity propagation↗
Enhanced Power Grid Maintenance Planning and Quantum-Inspired Combinatorial Prospects
Efficient and reliable scheduling of maintenance for power generation and transmission infrastructure is essential for minimizing operational costs and ensuring grid stability. This paper introduces an integrated optimization framework for coordinated maintenance scheduling of generators and transmission lines under resource and reliability constraints. The model minimizes a composite cost function including maintenance and generation costs, as well as penalties for delayed maintenance, while satisfying N−1 security constraints, operational limits, and crew availability. Case studies on the IEEE 300-bus test system demonstrate the effectiveness of the proposed approach in producing feasible and cost-effective maintenance schedules. To address scalability and combinatorial complexity, the model is mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem, enabling exploration of solution approaches based on Quantum Imaginary Time Evolution (QITE). While the QUBO reformulation provides a foundation for future quantum-inspired optimization, this study focuses primarily on the development and demonstration of the classical optimization framework and illustrates the potential applicability of QITE in large-scale maintenance scheduling.
Chen, Yang [ORNL] (ORCID:0000000271693874)↗
MI Power Grid Advanced Planning
Presentation on NREL insights from integrated planning processes.
distribution planning↗
Synthetic Power Grids from Real World Models
Research communities, vendors, and utilities need access to realistic power system models, however, there are confidentiality concerns that prevent their access to real world data. Similar concerns apply to the exchange of power system models between different organizations. This DOE-sponsored work is part of a broader effort to address these issues by creating tools for automatic generation of realistic publicly available power system models. In particular, this work presents a novel methodology for rapidly generating synthetic AC OPF power system models based on real-world topologies, power system parameters, ``realism'' metrics, and the desired complexity of the synthetic modes. Our technique relies on recent insights into the topological statistics of real-world power systems.
Young, Stephen J.↗
Coordination of Wind Farm and Pumped-Storage Hydro for a Self-Healing Power Grid
The increasing penetration of wind energy poses great challenges to the operation of power systems in normal and emergency states. However, energy storage technologies can help accommodate wind power uncertainty and variability due to their flexible characteristics. This paper focuses on the restoration phase, and provides a novel coordination strategy of wind and pumped-storage hydro (PSH) units for a faster and reliable self-healing process. The wind-PSH assisted power system restoration is formulated as a two-stage adaptive robust optimization problem. The first-stage problem determines the start-up sequence of generators and the energization times of transmission paths; and the second-stage problem decides load pickup sequences, wind power dispatch levels, and PSH units' operating modes. The column-and-constraint generation decomposition algorithm is applied to solve the two-stage adaptive robust optimization problem, which has a mixed-integer optimization in the inner-level problem. The developed coordination strategy is tested on the modified IEEE 39-bus system. Numerical results demonstrate that the coordinated wind and PSH units can increase the total energy served, enhance wind power dispatchability, and reduce wind power curtailment.
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Architectural Basis for Highly Distributed Transactive Power Grids: Frameworks, Networks, and Grid Codes
This paper provides an architectural framework for highly distributed Transactive Energy grids. This framework guides the engineering of transactive distribution systems, informs the necessary interoperability standards, and fills in the gap in the ability of electric distribution utilities to write the Grid Codes that define how prosumer and third party devices plug into such a grid. With this architecture, a major hurdle to Transactive Grid deployment is resolved.
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