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DOE OSTI2025

Distributed Control and Optimization of Virtual Inertia in Power Systems

The integration of renewable energy sources into power grids through inverter-based resources (IBRs) reduces system inertia, leading to faster frequency dynamics and potential grid instability. This paper proposes a distributed, consensus-based approach for the real-time control and optimization of inertia sources during system disturbances, enhancing both system stability and economic performance. Furthermore, we integrate this approach with the traditional inertia placement problem, demonstrating its suitability for existing frameworks. We validate its effectiveness in a 4-bus test system and a two-area 8-bus test system, showcasing its potential for scalable, dynamic inertia management in low-inertia grids.

Yadav, Ajay [ORNL] (ORCID:000000016111881X)↗
DOE OSTI2024

Rare Earth Elements (REE) and Critical Minerals (CM) in Middle Pennsylvanian-Age Coals and Associated Sediments in the Central Appalachian Basin, Eastern U.S.A.

The Central Appalachian Basin (CAB) of Kentucky, Tennessee, West Virginia and Virginia has a long history of coal mining and oil and gas extraction that has empowered the regional and national economies, the development of infrastructure, and a highly trained energy resources work force. As our societal demands for advanced technologies have rapidly increased in recent years, coal-related materials are viewed as an important new unconventional domestic source of critical minerals (CM) that are required for telecommunications, aerospace and transportation industries, electronics, the transition to low-carbon emissions energy production, and many consumer products. Coal-related materials encompass coal, associated sediments, coal mining waste materials, produced waters, and ash residues from coal-fired power plants. An important objective of the Evolve Central Appalachia Project (Evolve CAPP), sponsored by the U.S. Department of Energy (DOE) National Energy Technology Laboratory (NETL) is to assess the quantity and distribution of CM resources in the CAB region. The rare earth elements (REE) are considered highest priority, although other important CM such as niobium, gallium, and zirconium are known to occur in the Middle Pennsylvanian-age coals and sediments. Working with coal industry partners who provided access to drill cores, coal-related sediments and waste materials, over 600 samples have been collected for laboratory analysis, and over 730 materials have been scanned using portable x-ray fluorescence (pXRF) equipment. The application of pXRF provides the means for real-time semiquantitative analysis of CM content at very close spacings (typically 2-3 inch intervals) along drill core and in-situ channel samples that span the roof, coal seam, and floor rock. The comparison of pXRF geochemical data with laboratory results, geologic data, and downhole spectral gamma logs can provide high resolution input to lithologic and depositional models for future CM resource evaluations. The preliminary findings show that pXRF is capable of accurately measuring low concentrations of many of the CM with a high level of confidence (Ba, Cr, Ga, K, Nb, Rb, Sr, Th, Y), whereas for others (La, Ce, Co, Mn, Nd, Ni, Sc, Ti, V) the detection limits are very high or spectral interferences increase the uncertainty. Notably, the mean abundances of Y (34 ppm), La (86 ppm), Ga (40 ppm), and V (146 ppm) in coal underclays in the CAB region are up to 7X enriched compared with the overlying coal. These values also exceed the reported concentrations in published reference materials for upper continental crustal rocks (Rudnick and Gao, 2003), North American Shale Composite (Gromet et al., 1984), and North American coal (Finkelman, 1993). The pXRF data are in part verified by laboratory results that indicate the mean Y abundance (36 ppm) is highly correlated (R2 = 0.807) with total REE (ΣREE). The correlation is even higher (R2 = 0.957) with heavy REE (ΣHREE). Applying these correlations to the pXRF data for the coal underclays, the mean estimated values for ΣREE+Y and ΣHREE+Y are 270 ppm and 58 ppm, respectively. Although these average values are not considered high, the range of Y measured by pXRF in the coal underclays extended as high as 114 ppm, which would suggest ΣREE+Y equal to 847 ppm. The mean abundances of Zr (192 ppm) and Th (21 ppm) in coal underclays are also enriched compared with the overlying coal and these results likely reflect the presence of resistant detrital heavy minerals such as monazite, xenotime, and zircon in the underclay matrix. Several of the profiled coal seams and associated wall rocks contained thin volcanic ash layers up to 4-5 inches in thickness. The extent to which these ash fall layers provided a source for CM under the paleoenvironmental conditions that resulted in coal deposits in the CAB remains to be fully studied. Continuing investigations in the Evolve CAPP study area will include laboratory determinations of mineralogic and clay compositions, and evaluations of CM geochemical mobility in the coal and coal underclays.

Lassetter, Billy↗
DOE OSTI2020

A High-resolution Regional Wave Resource Characterization For The U.S. West Coast

Objectives/Scope: Wave resource characterization is a critical step for wave energy converter deployment in the coastal ocean and relies on long-term, high-resolution wave datasets. This study presents a detailed modeling study of the wave resource along the U.S. West Coast (Washington, Oregon, and California), a coastal region that was identified with high wave energy potential in earlier studies. Methods, Procedures, Process: The wave hindcast covers a 32-year period from 1979 to 2010 and is based on a multi-resolution, unstructured-grid SWAN model framework. Model configuration closely follows and meets the requirements recommended by the International Electrotechnical Commission Technical Specification (IEC TS) for wave energy resource assessment and characterization (Class 2 - feasibility study). The model domain covers the entire U.S. Exclusive Economic Zone (EEZ) in the West Coast and has a spatial resolution varying from ~300 m in the nearshore region (20 km from the shoreline) to ~2500 m within the EEZ and ~5000 m at the open boundary, which extends beyond the EEZ. The model was forced by hourly 2-D wave spectra produced by a two-way nested WaveWatch III model, which covers the global ocean domain and the broader U.S. West Coast region domain with spatial resolutions of 0.5 degree and 10 arc-minutes, respectively. Both wave models are forced by hourly, 0.5-degree wind forcing obtained from NCEP’s Climate Forecast System Reanalysis (CFSR) product. Results, Observations, Conclusions: The standard model output for the SWAN model includes 3-hourly output for the six IEC wave resource parameters (e.g., omnidirectional wave power) at each grid point and hourly 2-D spectra at more than 50 NDBC buoys. Extensive model validation was achieved by comparing the six model-predicted IEC parameters with those derived from field observations at representative NDBC buoys. The error statistics indicated the model’s satisfactory performance. Further analyses were conducted to systematically evaluate the temporal and spatial distributions of wave energy potential and wave climate along the U.S. West Coast. Results suggest that Washington and Oregon coasts have similar nearshore wave resource, which is significantly higher than resources in Southern California. Strong seasonal variations are also observed, e.g., high wave energy tends to occur in the winter months. In summary, this study produced the first high-resolution, comprehensive dataset on wave energy distribution along the U.S. West Coast. Novel/Additive Information: The results are being used by the National Renewable Energy Laboratory to update the MHK Atlas, which was originally derived from NOAA’s 4-arc-minute WaveWatch III model output. In addition, the monthly averaged wave energy climatology dataset can be readily shared to support a variety of research and application efforts within the EEZ of the U.S. West Coast.

Wang, Taiping↗
DOE OSTI2023

Microgrid energy scheduling under uncertain extreme weather: Adaptation from parallelized reinforcement learning agents

Microgrids are useful solutions for integrating renewable energy resources and providing seamless green electricity to minimize carbon footprint. In recent years, extreme weather events happened often worldwide and caused significant economic and societal losses. Such events bring uncertainties to the microgrid energy scheduling problems and increase the challenges of microgrid operation. Traditional optimization approaches suffer from the inaccuracy of the uncertain microgrid model and the unseen events. Existing reinforcement learning (RL) - based approaches are also hampered by the limited generalization and the increasing computational burden when stochastic formulations are required to accommodate the uncertainties. This paper proposes a new parallelized reinforcement learning (PRL) method based on the probabilistic events to handle the microgrid energy uncertainties. Specifically, several local learning agents are employed to interact with pertinent microgrid environments in a distributed manner and report outcomes to the global agent, which will optimize microgrid energy resources online during extreme events. The stochastic microgrid energy optimization problem is reformulated to include all possible scenarios with probabilities. The advantage estimate functions of learning agents are designed with a backward sweep to transfer the outcomes to the value function updating process. Two simulation studies, stochastic optimization and online testing, are performed to compare with several existing RL approaches. Results substantiate that the proposed PRL method can achieve up to 20% optimization performance improvement with 4 and 28 times less computation cost than Q-learning with experience replay and multi-agent Q-learning approaches, respectively.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

The "Perfect Storm" for Distributed Wind Markets

Close to 1 GW of distributed wind has been installed across all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands. Yet the number of installations varies greatly by state, even when wind resources are similar. This article examines three key factors that over the past thirteen years have shown to be enablers of success for distributed wind installations in the United States: 1) favorable policy environments, including consistent incentives, administrative support for cutting through red tape, and favorable interconnection, net metering, and permitting policies; 2) high electricity prices that would be displaced by wind energy generation, and, 3) favorable wind resources, enabling steady production of energy. Not all conditions need to be met simultaneously, yet of the studied markets for distributed wind in Iowa, New York, Washington, and Oregon, the most successful ones exhibit at least two out of the three conditions mentioned. We describe steps that policymakers, regulators, and market players have taken in the past to increase distributed wind installations in the United States.

Foster, Nikolas AF↗
DOE OSTI2025

A Model Predictive Control to Improve Grid Resilience

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations.

microgrid↗
DOE OSTI2025

A Practical Approach to Inertia Assessment: Analyzing New York's Grid Response to Real Events

The non-uniform distribution of inverter-based resources (IBRs) such as solar and wind energy leads to the formation of weakly coupled regions within the broader interconnection. This highlights the urgency of investigating regional dynamics. This paper proposes a practical framework for estimating regional inertia using actual event measurements from the New York (NY) region. The regional frequency is determined using measurement data from frequency disturbance recorders (FDRs) deployed across NY. The rate-of-change of frequency (RoCoF) is estimated using a hybrid methodology that combines the benefits of both moving and non-moving window techniques. By utilizing the power mismatch value and the estimated RoCoF, various inertia metrics such as regional RoCoF, inertia arrival time, and inertia percentage relative to the interconnection inertia are calculated. The identified confirmed-events that took place between 2017–2022 in NY are used in the study. This assessment of inertia using real event data reveals the true behavior of the grid under stress. The results indicate that as the generation mix in NY evolves with the rise of IBRs, both the RoCoF levels and the regional inertia undergo significant changes.

Dulal, Saurav [University of Tennessee, Knoxville ↗
DOE OSTI2023

National Distributed Wind Energy Deployment Network

The National Distributed Wind Deployment Network seeks to broaden the types of stakeholders considering distributed wind deployment, document the barriers and opportunities they have identified, and develop resources and supports to help them deploy the technology.

deployment↗
DOE OSTI2024

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. Here, this study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

14 SOLAR ENERGY↗
DOE OSTI2024

Characterizing model uncertainties in simulated coast-to-offshore wind over the northeast U.S. using multi-platform measurements from the TCAP field campaign

Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used to provide estimates of the offshore wind energy resource owing to their large spatial coverage compared to available observations. Nevertheless, spatiotemporal distribution of model biases is highly dependent on factors including model configuration, location, and the interplay of multi-scale physical processes. Here, in this study, we focus on the characterization of model uncertainties in simulated coast-to-offshore winds over the northeast U.S., by varying sea surface temperature (SST) forcings, surface layer (SL) and planetary boundary layer (PBL) parameterizations, as well as identifying biases that may be directly passed from initial and boundary conditions. Multiple measurements, including aircraft data collected during the U.S. Department of Energy's Two-Column Aerosol Project (TCAP) experiment, are used to constrain the model results and facilitate quantitative comparisons. Our analysis indicates while SST forcing has notable impacts on simulated air temperature and moisture within PBL, the modeled winds are in general more sensitive to the choices of SL and PBL physics than to SST. The model’s forcing data not only controls the vertical dependence of wind speed errors, but also alters regional variability in wind speed’s spatial correlation. Bias comparisons between ERA5 reanalysis and ensemble simulations revealed significant similarity, particularly in wind speed biases during winter, underscoring their dependency on initial and boundary conditions. Coastal and offshore near-surface wind speed biases tend to exhibit much higher similarity in winter than in summer due to the presence of much stronger and more persistent synoptic wind conditions. This study highlights the importance of accurate atmospheric forcing and parameterization choices in improving wind forecasts and suggests the potential for extrapolating coastal wind biases to offshore locations, aiding wind energy forecasting and informing the Wind Forecast Improvement Project-3 (WFIP3).

17 WIND ENERGY↗
DOE OSTI2020

Distributed Small-Signal Stability Conditions for Inverter-Based Unbalanced Microgrids

The proliferation of inverter-based generation and advanced sensing, controls, and communication infrastructure have facilitated accelerated deployment of microgrids. A coordinated network of microgrids can maintain reliable power delivery to critical facilities during extreme events. Low-inertia offered by the power-electronics–interfaced energy resources, however, can present significant challenges to ensuring stable operation of the microgrids. In this work, distributed small-signal stability conditions for inverter-based microgrids are developed that involve the droop-controller parameters and the network parameters (e.g. line impedances, loads). The distributed closed-form parametric stability conditions derived in this paper can be verified in a computationally efficient manner, facilitating reliable design and operations of networks of microgrids. Dynamic phasor models have been used to capture the effects of electromagnetic transients. Furthermore, numerical results are presented, along with PSCAD simulations, to validate the analytical stability conditions. Effects of design choices, such as the conductor types, and inverter sizes, on the small-signal stability of inverter-based microgrids are investigated to derive useful engineering insights.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Safeguarding the Grid: Diverse Resources for Resilience [In My View]

Reports on the vulnerability of the power grid. From wildfires, hurricanes, and floods to cyberattacks, the grid is vulnerable to natural and human-made disasters, which can cause widespread outages that last from hours to months and leave lasting economic impacts. Imagine a coastal region in the United States that has just been hit by a hurricane and lost all power. Besides the electricity infrastructure going out, communications networks, rail transportation, and gasoline pumps may also stop working after the emergency event, which often delays repair efforts to the grid. Unresponsive communications make it difficult to dispatch workers to repair the grid, and missing transportation links delay shipments of replacement equipment for the grid’s restoration.

cybersecurity↗
DOE OSTI2022

Evolving Metrics for Resource Adequacy Assessment

Resource adequacy analysis quantifies the likelihood of capacity shortfall on a power system in a probabilistic manner. Using a combination of statistical techniques and power system fundamentals, the analysis typically evaluates hundreds or thousands of stochastic random samples (replications) of varying load, generator outages, variable renewable energy availability, and other aspects of power system uncertainty. In this range of uncertainty, there are - at times - periods where the power system's available resources are insufficient to meet system demand, referred to as a shortfall event. Today's power systems' rapidly evolving generation mix is changing the types of data needed by system planners and regulators, which can often render traditional resource adequacy metrics insufficient for ensuring resource adequacy for tomorrow's grid. In this paper we provide a critical assessment of traditional measures of shortfall risk in power systems, discussing their shortcomings and how they compare to metrics used in other domains. From this analysis we propose four steps forward for improving power system resource adequacy risk metrics in the future.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗
DOE OSTI2024

Developing IEEE Std 2800-Compliant Algorithms for Transmission-Connected Inverter-Based Resources: Preprint

This study addresses the compliance of Inverter-based Resources (IBRs) with the IEEE Std 2800, a leading standard that defines interconnection and interoperability requirements for IBRs integrated into transmission systems. Focusing on abnormal grid scenarios, the research evaluates the specific demands on IBRs, proposing a comprehensive controller development framework. This framework caters to maintaining ride-trhough operation or implementing strategic disconnections in line with IEEE Std 2800, alongside managing currents during voltage ride-through scenarios. The effectiveness of this proposed controller framework is rigorously validated through case studies, employing a MATLAB/Simulink model of an IBR to test its performance under diverse grid fault conditions, ensuring the IBRs' alignment with standard requirements and their robust performance in enhancing grid reliability.

IEEE Std 2800↗
DOE OSTI2023

Black Start with Inverter-Based Resources: Hardware Testing

Black start, or grid restoration after a wide-spread power outage, is a critical service on the power system that has historically been provided by transmission-connected synchronous generators. As the power system transitions to rely on more distributed and inverter-based resources it will be critical that these resources can also provide black start services. In this work we investigated battery energy storage and solar photovoltaics technical capabilities and limitations to provide black start services through hardware testing in an experimental microgrid testbed. This hardware demonstration of inverter-based resources providing black start functions can help inform grid operators on how to include these types of resources in their black start plans.

24 POWER TRANSMISSION AND DISTRIBUTION↗