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At least 523 records · Page 29

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DOE OSTI2025

West Coast Offshore Wind Transmission Study

As a primary federal investigation of the utility of transmission to support offshore wind emergence in the Western Interconnect, the West Coast Offshore Wind Transmission study evaluates five distinct West Coast-wide transmission scenarios and trades the economic benefits and costs of six development pathways from 2035 to 2050. In this time, 33 GW of OSW generation and supporting transmission are explored within the context of 350 additional GW of generation to support 100% clean electricity. Geospatial analysis informed plausible transmission topologies and cost estimates. All pathways to intraregional or interregional coordination transmission builds showed more than $\$$14 billion in present value cost savings, with a maximum of $\$$25 billion of savings in the interregional case. These savings are primarily driven by fuel costs and increased availability of power flows enabled by new transmission networks. Key considerations of clean, dispatchable generation options, geographic disaggregation of costs, system reliability reinforcements, resilience, and community values are also presented and discussed.

17 WIND ENERGY↗

DOE OSTI2020

Impacts of Ternary-Pumped Storage Hydropower on U.S. Western Interconenction with Extremely High Renewable Penetrations

In recent years, ternary pumped storage hydropower (T-PSH), as one of the advanced PSH technologies, has attracted more attention from the industry. The interest is because of the capability of providing fast power support for the future electric grid with high penetration of renewable energy (RE). This paper focuses on studying the impact of T-PSH on frequency response of the U.S. Western Interconnection under different penetration levels of RE, which are from 20% to 80%. The detailed model of T-PSH is developed and implemented in the Western Interconnection grid model based on the GE Positive Sequence Load Flow (PSLF) platform, which can capture the unique dynamics of hydraulic short-circuit (HSC) mode and fast mode switching of T-PSH. To reveal the dynamic benefit from T-PSH, the frequency response of the Western Interconnection has been compared with and without T-PSH. In addition, a new solution is discussed that uses T-PSH to improve system stability issues caused by high RE penetration.

13 HYDRO ENERGY↗

DOE OSTI2024

Grid-Ready Energy Analytics Training with Data (“GREAT with Data”)

GridEd is a collaborative educational initiative consisting of the Electric Power Research Institute (EPRI), 5 Partner Universities (Stony Brook University, The University of Texas at Austin, University of California – Riverside, Virginia Tech, Washington State University), and participating industry sponsors. This educational initiative focuses on developing and training the next generation of power engineers so they can help shape the electric grid of the future by anticipating and fulfilling the needs of changing electric industry requirements. GridEd is leveraging electric industry research to educate a future electric grid workforce by empowering new and continuing education students, not only to become competent and well-informed engineers, but also to participate and influence major technological, social, and policy decisions that address critical global challenges. GridEd’s activities are centered around four core pillars: Enhancement of university power systems engineering curricula; Professional development and training for a diverse electric industry workforce; Stimulating students to join the movement for the next generation of power engineers, and; Improve workforce development efforts in the electric utility industry. Major accomplishments over the course of the project were: Over 50 unique professional short courses were delivered by more than 30 instructors across the GridEd network; Over 3,600 unique learners, many who took multiple courses, received more than 27,000 professional development hours (PDH) and over 1,000 certificates of completion; Approximately 2,900 unique learners undertook a course that was offered LIVE online or in-person; Approximately 700 unique learners undertook a course that was offered as computer-based training (CBT); Over 35 unique university courses were delivered by more than 30 instructors to 1,500 university students across the GridEd network; One-hundred-and-fifty-seven (157) students were funded to completed 43 student projects in topics of power systems and data science, and; Six (6) Historically Black Colleges and Universities (HBCUs) recruited as Affiliate Universities via Utility Partners. The professional training initiative and workforce development activities launched by this project will be sustained through EPRI’s collaborative business model with industry. Stimulating students to join the power engineering workforce of the future and the enhancement of tertiary training may continue to need government support.

14 SOLAR ENERGY↗

DOE OSTI2022

Supervised Learning for Distribution Secondary Systems Modeling: Improving Solar Interconnection Processes

The current interconnection process and hosting capacity analysis for distributed energy resources (DERs), such as photovoltaics (PV) and battery energy storage systems, are based on analyzing grid network constraints (voltage and thermal) using only medium-voltage distribution network models. This is because most utilities do not have secondary low-voltage system models that connect service transformers and residential customers. This is important because in many cases the main impact of interconnecting DERs could occur on the low-voltage distribution systems. This paper proposes a supervised learning method to approximate local secondary models to improve the interconnection process. The proposed supervised learning method includes a decision tree model that predicts the secondary topology and a logistic regression model that predicts conductor types. The case studies demonstrate the benefits of including secondary low-voltage circuits in the interconnection process. We report the proposed modeling methodology is readily scalable and thus can reduce the cost and effort of PV interconnection for the industry and stakeholders.

14 SOLAR ENERGY↗

DOE OSTI2024

Voltage Optimization

Voltage optimization refers to a volt-var optimization technique which was originally designed to minimize energy consumption and improve end-use efficiency on the distribution system. This reduces source voltage at a substation, which lowers generation demand, reduces system losses, and improves system stability. This has been the conventional implementation of voltage optimization: focusing on regulation of voltages throughout the distribution system via coordinated adjustments of load tap changers, line-voltage regulators, switched shunt compensation, and the targeted placement of new shunt compensation. These methods have received increased focus following a growth in distributed-energy resources (DERs) interconnecting at the distribution level, given their developing capability to participate in voltage regulation. This is paired with the intermittency challenges they pose, load imbalances, and voltage fluctuations. Voltage optimization has long been an area of extensive research within the distribution system, with various strategies and philosophies demonstrating a range of effects. The conventional focus on the distribution system owes to the typical operation of transmission systems, which assumes firm local resources and flows strictly from generation resources to loads. The strategies employed in distribution have also been optimized for this typically radial configuration and operation of the distribution system.

24 - POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid

Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Nodal Admittance Matrix Based Area Partition Method for Small-Signal Stability Analysis of Large-Scale Power Electronics Based Power Systems

In power electronics-based power systems (PEPSs), small-signal stability is an important factor for system design and operation, where the impedance-based approach is often used. However, unlike small-scale PEPSs with simple and straightforward impedance models, it would take much more efforts to derive the large-scale PEPSs impedance model, which is very complicated and sometimes may get wrong results due to the elimination of right-half plane (RHP) poles during the impedance aggregation process. To simplify the derivation procedure and analyze the small-signal stability of large-scale PEPSs, this paper proposes a nodal admittance matrix (NAM) based area partition method. In this method, the large-scale PEPS is divided into several sub-areas, and the stability is analyzed within the sub-area first, and then the interconnection stability among these sub-areas is analyzed. The proposed method is scalable and can help to locate the weakest areas/converters that may cause instability in the whole system. In this paper, the concept of the proposed method and its application to an example system are introduced. Experimental results are also given to validate the effectiveness of the proposed method.

Qiao, Liang↗

DOE OSTI2024

DS-GL: Advancing Graph Learning via Harnessing the Power of Nature within Dynamic Systems

With the rapid digitization of the world, an increasing number of real-world applications are turning to nonEuclidean data, modeled as graphs. Due to their intrinsic high complexity and irregularity, learning from graph data demands tremendous computational power. Recently, CMOS-compatible Ising machines, i.e., dynamic systems composed of CMOS components, have emerged as a new approach that harnesses the inherent power of natural annealing within dynamic systems to efficiently resolve binary optimization problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL) tasks, Ising machines face significant hurdles: (i) they are inherently binary and thus ill-suited for real-valued problems; (ii) their expensive all-to-all coupling network that guarantees effective natural annealing poses daunting scalability concerns. To address these challenges, this paper proposes a nature-powered graph learning framework dubbed DS-GL, which is the first effort to transform the process of solving graph learning problems into the natural annealing process within a parameterized dynamic system embodied as a CMOS chip. To tackle the two major hurdles, DS-GL first augments the Ising machine architecture to modify the self-reaction term of its Hamiltonian function from linear to quadratic, effectively serving as an energy regulator. This adjustment maintains the system’s original physical interpretation while enabling it to process continuous, real-valued data. Second, to address the scaling issue, DS-GL further upgrades the real-valued dense Ising machine by decomposing it into a mesh-based multi-PE dynamic system that supports efficient distributed spatial-temporal co-annealing across different PEs through sparse interconnects. By exploiting the inherent sparsity and component structures in real-world graphs, DS-GL is able to map complex graph learning tasks onto the scalable dynamic system while maintaining high accuracy. Evaluations with three diverse GL applications across six real-world datasets, including traffic flow and COVID-19 prediction, show that DS-GL can deliver from 102× to 106× speedups and 500× energy reduction over Graph Neural Networks on GPUs, with 5% - 20% accuracy enhancement.

Song, Ruibing↗

DOE OSTI2025

Quantifying Message Aggregation Optimisations for Energy Savings in PGAS Models

Upon breaking past the exascale barrier, HPC systems are facing their greatest challenge yet - a power wall that must be addressed through new methods in both hardware and software. While energy costs are becoming a major issue at all levels, of particular concern is that of the network, as the relative cost of moving data is increasing faster than ever. The partitioned global address space (PGAS) model is critical within certain HPC domains, but is known to suffer from the small message problem, where irregular many-to-many access patterns result in congesting the network with excessive numbers of small messages. To address this, the conveyor aggregation library was developed to defer individual messages and group them for subsequent bulk processing. In this paper, we investigate its impact on energy use related to the network, with a focus on the Slingshot 11 interconnect. We will demonstrate that this strategy is not only highly performant, but also crucial to reducing energy footprints to remain within target power envelopes.

Welch, Aaron [ORNL]↗

DOE OSTI2022

Addressing Electric Aviation Infrastructure Cybersecurity Implementation

This technical report evaluates key considerations of cybersecurity systems in preparation for the electrification of both legacy and new aviation services. The objectives are to consider the landscape of components and interconnections, review sensitivities and the criticality of data and operations, and aim to understand the applicability of existing best practices or the need for new ones associated with the cybersecurity of electric charging systems for the aviation sector. Given the early stages of electric aviation deployment, developers have the opportunity to plan and integrate cybersecurity strategies into the complete electric aviation system during the initial stages of design and procurement and will benefit from employing a framework for continuous cybersecurity monitoring. This paper presents initial considerations of key cybersecurity strategies and their application to forthcoming electric aviation systems.

airports↗

DOE OSTI2023

Architecture for Co-Simulation of Transportation and Distribution Systems with Electric Vehicle Charging at Scale in the San Francisco Bay Area

This work describes the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI) architecture for the co-simulation of distribution and transportation systems to evaluate EV charging impacts on electric distribution systems of a large metropolitan area and the surrounding rural regions with high fidelity. The current co-simulation is applied to Oakland and Alameda, California, and in future work will be extended to the full San Francisco Bay Area. It uses the HELICS co-simulation framework to enable parallel instances of vetted grid and transportation software programs to interact at every model timestep, allowing high-fidelity simulations at a large scale. This enables not only the impacts of electrified transportation systems across a larger interconnected collection of distribution feeders to be evaluated, but also the feedbacks between the two systems, such as through control systems, to be captured and compared. The findings are that with moderate passenger EV adoption rates, inverter controls combined with some distribution system hardware upgrades can maintain grid voltages within ANSI C.84 range A limits of 0.95 to 1.05 p.u. without smart charging. However, EV charging control may be required for higher levels of charging or to reduce grid upgrades, and this will be explored in future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Generative Design for Resilience of Interdependent Network Systems

Abstract Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria while the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on synthetic supply chain networks and power systems from the IEEE dataset have illustrated the applicability of the developed method for designing resilient interdependent network systems.

Engineering↗

DOE OSTI2022

Control and Grid Support Function Evaluation for a Three-Phase Back-to-Back Modular Multilevel Converter System

This paper focuses on the evaluation of advanced inverter controls for a 500kW back-to-back (B2B), silicon carbide, multilevel modular converter (MMC) used as a direct-connected medium-voltage grid intertie. A decentralized hierarchical controller architecture for the MMC system is presented, which uses the main controller to implement higher level grid interconnection functions per the IEEE 1547–2018 standards and the local controllers to execute lower level closed-loop functionality of the MMC stages. This paper presents and elaborates on the grid functionalities are elaborated and presented. The back-to-back converter system is equipped with local controllers and their corresponding functionalities are presented. Case study results for the corresponding functionalities presented show the effectiveness of the proposed architecture. To enhance the operation capabilities of the system, a parameter update scheme has been implemented and utilized in unison with the controller.

decentralized control↗

DOE OSTI2023

Optomechanical ring resonator for efficient microwave-optical frequency conversion

Phonons traveling in solid-state devices are emerging as a universal excitation for coupling different physical systems. Phonons at microwave frequencies have a similar wavelength to optical photons in solids, enabling optomechanical microwave-optical transduction of classical and quantum signals. It becomes conceivable to build optomechanical integrated circuits (OMIC) that guide both photons and phonons and interconnect photonic and phononic devices. Here, we demonstrate an OMIC including an optomechanical ring resonator (OMR), where co-resonant infrared photons and GHz phonons induce significantly enhanced interconversion. The platform is hybrid, using wide bandgap semiconductor gallium phosphide (GaP) for waveguiding and piezoelectric zinc oxide (ZnO) for phonon generation. The OMR features photonic and phononic quality factors of >1 × 10 5 and 3.2 × 10 3 , respectively. The optomechanical interconversion between photonic modes achieved an internal conversion efficiency $η_i$ = (2.1 ± 0.1)% and a total device efficiency $η_{tot}$ = 0.57 x 10 -6 at a low acoustic pump power of 1.6 mW. The efficient conversion in OMICs enables microwave-optical transduction for quantum information and microwave photonics applications.

42 ENGINEERING↗

DOE OSTI2021

Fault location in High Voltage Multi-terminal dc Networks Using Ensemble Learning

Precise location of faults for large distance power transmission networks is essential for faster repair and restoration process. High Voltage direct current (HVdc) networks using modular multi-level converter (MMC) technology has found its prominence for interconnected multi-terminal networks. This allows for large distance bulk power transmission at lower costs. However, they cope with the challenge of dc faults. Fast and efficient methods to isolate the network under dc faults have been widely studied and investigated. After successful isolation, it is essential to precisely locate the fault. The post-fault voltage and current signatures are a function of multiple factors and thus accurately locating faults on a multi-terminal network is challenging. In this paper, we discuss a novel data-driven ensemble learning based approach for accurate fault location. Here we utilize the eXtreme Gradient Boosting (XGB) method for accurate fault location. The sensitivity of the proposed algorithm to measurement noise, fault location, resistance and current limiting inductance are performed on a radial three-terminal MTdc network designed in Power System Computer Aided Design (PSCAD)/Electromagnetic Transients including dc (EMTdc).

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Risk-informed Hierarchical Control of Behind-the-Meter DERs with AMI Data Integration (Final Technical Report)

This project addresses several key barriers to implement the next generation demand response applications and provides a clear understanding of implementing hierarchical and standalone control using AMI data. Through this program, Eaton has developed and tested a meter-as-a-controller prototype with the help of other partners--- National Renewable Energy Laboratory (NREL), Electric Power Research Institute (EPRI), Pecan St Inc. (PSI), and Delaware Electric Cooperative (DEC). The controller can utilize residential controllable loads such as heating, ventilation, and air conditioner (HVAC), electric water heater and distributed energy resources like solar PV and battery energy storage systems for off-setting the demand that is required from the grid, thus providing reliable grid-services for demand reduction or peak shaving. The controller is also capable of coordinating the resources of the premises for better management and energy efficiency while meeting the comfort bound of the premises owner as quality-of-service. The development has been demonstrated in a three virtual-home setup at system performance lab of NREL with real appliances (HVAC, electric water heater, solar PV, and battery). The technology has also been proved through laboratory and field demonstration with successful interconnectivity (e.g., end-to-end communication and data exchange) between the residential appliances and utility through the RF network at Delaware Electric Co-op (DEC) in Delaware.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Battery Energy Storage System (BESS) Licensing and Permitting [Slides]

This presentation provides an overview of the licensing, permitting, and regulatory frameworks governing Battery Energy Storage Systems (BESS) across multiple international contexts. It outlines foundational permitting considerations - including safety standards, siting requirements, regulatory and environmental obligations, and interconnection processes - alongside typical pathways for installation, approval, and inspection. The presentation summarizes key technical standards such as NFPA 855 and IEEE guidelines, and emphasizes best practices for safe and effective deployment. A series of detailed case studies from South Africa, Chile, Mexico, Brazil, Costa Rica, Honduras, and the Dominican Republic illustrates how different countries structure approval authority, evaluate environmental impacts, and integrate BESS into national grid systems. The deck concludes with insights on emerging challenges, including evolving regulatory frameworks, financial viability, and the need for pricing mechanisms that accurately value storage services.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Understanding Economic and Deployment Benefits of Wind-PV Hybrid Power Plants

The least-cost approach to decarbonizing the U.S. electricity supply will likely involve a significantly expanded share of variable renewable energy (VRE) generation. Many strategies and approaches have been identified to address integration challenges in a VRE-dominated system, including standalone and coupled battery systems. In this study, we explored the current and future potential of utility-scale hybrid energy systems comprising PV, wind, and lithium-ion battery technologies (PV-wind-battery systems). Analysis results include temporal complementarity of wind and PV resources across the contiguous United States, with an emphasis on metrics that quantify reductions in variability that can be achieved through hybridization. We further use a price-taker model with simulated hourly energy and capacity prices to determine the revenue-maximizing dispatch of a range of PV-wind-battery configurations across Texas, from the present through 2050. We find that coupling PV, wind, and battery technologies allows for more effective utilization of interconnection capacity by increasing capacity factors to 60-80%+ and capacity credits to close to 100%, depending on battery capacity. We also compared the energy and capacity values of PV-wind and PV-wind-battery systems to the corresponding stability coefficient metric, which describes the location-and configuration-specific complementarity of PV and wind resources. Our results show that the stability coefficient effectively predicts the configuration-location combinations in which a smaller battery component can provide comparable economic performance in a PV-wind-battery system (compared to a PV-battery system). These PV-wind-battery hybrids can help integrate more VRE by providing smoother, more predictable generation and greater flexibility. Finally, capacity expansion modeling of PV-wind systems indicates that hybridization can reduce transmission expansion requirements associated with a heavily decarbonized U.S. electricity system.

complementarity↗