A Framework for Coordinated Self-Assembly of Networked Microgrids Using Consensus Algorithms
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24 POWER TRANSMISSION AND DISTRIBUTION↗
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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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Understanding Line Losses and Transformer Losses in Rural Isolated Distribution Systems
Rural, isolated power systems in the mainland U.S. and in states like Alaska and Hawaii are powered by assets like diesel generators. These rural, isolated power systems also cannot operate at the higher band of medium voltage (like 69kV). They are primarily in the 12 to 14 kV range to keep the cost of the distribution investments lower. Because of this mid-band medium voltage range, the line losses and distribution transformers losses consume significant diesel consumption (almost 10 percent of the peak load). This work considers one such power system powering an isolated system and presents key findings online losses, and transformer losses. Understanding and documenting the impacts is critical for these communities operating their power systems and take actions to reduce expensive diesel consumption. In this paper, we will present one such typical grid and model it in electromagnetic transients (EMT) domain. We used the tower structure, under ground cabling installation to develop high fidelity models of lines. We also used high fidelity models of distribution transformers to present the no-load losses and full load loses. We will also present technical solutions available commercially off-the-shelf to reduce these losses and reduce diesel consumption. This work will be a primer for communities to understand the technical challenges and to understand the possible solution available to solve such challenges for rural, isolated power system operators.
blackstart↗
Water-Soluble Copper(I) Hydroxide Catalyst and Its Formation in Ligand-Free Suzuki–Miyaura Cross-Coupling Reactions
Copper has emerged as an alternative metal for metal-catalyzed cross-coupling reactions due to its low cost, readily availability, and low toxicity. Here this article reports a water-soluble active molecular catalyst and its formation in the ligand-free Suzuki–Miyaura (SM) cross-coupling reactions with copper iodide as the precatalyst in aqueous solutions. The SM coupling is homogeneous in nature, and the molecular catalyst is Cu(OH) in its singlet electronic state as identified by experimental and computational UV–vis absorption spectroscopy. The Cu(OH) catalyst is generated through the leaching of oval-shaped Cu 2 O nanoparticles, which are characterized with X-ray Auger electron spectroscopy, X-ray absorption spectroscopy, and transmission electron microscopy. The soluble Cu(OH) species is stable for at least 4 weeks under ambient conditions.
cross-coupling↗
Conditional Karhunen–Loève regression model with Basis Adaptation for high-dimensional problems: Uncertainty quantification and inverse modeling
Here, we propose a methodology for improving the accuracy of surrogate models of the observable response of physical systems as a function of the systems’ spatially heterogeneous parameter fields, with applications to uncertainty quantification and parameter estimation in high-dimensional problems. Practitioners often formulate finite-dimensional representations of spatially heterogeneous parameter fields using truncated unconditional Karhunen–Loève expansions (KLEs) for a certain choice of unconditional covariance kernel and construct surrogate models of the observable response with respect to the KLE coefficients. When direct measurements of the parameter fields are available, we propose improving the accuracy of these surrogate models by representing the parameter fields via conditional Karhunen-Loève expansions (CKLEs). CKLEs are constructed by conditioning the covariance kernel of the unconditional expansion on the direct measurements of the parameter field via Gaussian process regression, and then truncating the corresponding KLE. We apply the proposed methodology to constructing surrogate models via the Basis Adaptation (BA) method of the stationary hydraulic head response, measured at spatially discrete observation locations, of a groundwater flow model of the Hanford Site, as a function of the 1000-dimensional representation of the model’s log-transmissivity field. We find that BA surrogate models of the hydraulic head based on CKLEs are more accurate than BA surrogate models based on unconditional expansions for forward uncertainty quantification tasks. Furthermore, we find that inverse estimates of the hydraulic transmissivity field computed using CKLE-based BA surrogate models are more accurate than those computed using unconditional BA surrogate models.
97 MATHEMATICS AND COMPUTING↗
Dispatch optimization of electric thermal energy storage within System Advisor Model
A stand-alone electric thermal energy storage (ETES) system converts low-value electricity into heat using resistance heating elements. During periods of high-value electricity, an ETES system uses a thermodynamic power cycle to convert stored thermal energy back to electricity. These dispatchable systems derive value from their ability to store energy when prices are low and generate electricity when prices are favorable, i.e., energy arbitrage. Consequently, dispatch optimization of system operations, through maximizing revenue subject to system constraints, is essential to evaluate the economic value of a particular system design. While stand-alone ETES systems offer potential advantages as dispatchable grid storage technologies, there is a lack of a neutral third-party, publicly available, open-source model to evaluate the performance, dispatch, and financial viability of these systems. To address this problem, we have developed a techno-economic model for stand-alone ETES systems, within National Renewable Energy Laboratory's (NREL's) System Advisor Model (SAM). We implement a mixed-integer linear program to determine an ETES optimal operating schedule that maximizes electricity sales less maintenance costs caused by operation and cycling given temporal-varying grid electricity prices. Our contributions include a mixed-integer linear program for energy arbitrage of an ETES system, an ETES performance model through a publicly-available software (i.e., SAM), and an exercise of our model through case studies that compare ETES operational strategies and annual financial metrics. With our dispatch optimization model, we were able to improve revenue by 20% compared to a myopic heuristic while reducing the operational cost of the ETES system through decreases in cycle starts, cycles per day, and heater starts.
24 POWER TRANSMISSION AND DISTRIBUTION↗
How robust are estimates of key parameters in standard viral dynamic models?
Mathematical models of viral infection have been developed, fitted to data, and provide insight into disease pathogenesis for multiple agents that cause chronic infection, including HIV, hepatitis C, and B virus. However, for agents that cause acute infections or during the acute stage of agents that cause chronic infections, viral load data are often collected after symptoms develop, usually around or after the peak viral load. Consequently, we frequently lack data in the initial phase of viral growth, i.e., when pre-symptomatic transmission events occur. Missing data may make estimating the time of infection, the infectious period, and parameters in viral dynamic models, such as the cell infection rate, difficult. However, having extra information, such as the average time to peak viral load, may improve the robustness of the estimation. Here, we evaluated the robustness of estimates of key model parameters when viral load data prior to the viral load peak is missing, when we know the values of some parameters and/or the time from infection to peak viral load. Although estimates of the time of infection are sensitive to the quality and amount of available data, particularly pre-peak, other parameters important in understanding disease pathogenesis, such as the loss rate of infected cells, are less sensitive. Viral infectivity and the viral production rate are key parameters affecting the robustness of data fits. Fixing their values to literature values can help estimate the remaining model parameters when pre-peak data is missing or limited. We find a lack of data in the pre-peak growth phase underestimates the time to peak viral load by several days, leading to a shorter predicted growth phase. On the other hand, knowing the time of infection (e.g., from epidemiological data) and fixing it results in good estimates of dynamical parameters even in the absence of early data. While we provide ways to approximate model parameters in the absence of early viral load data, our results also suggest that these data, when available, are needed to estimate model parameters more precisely.
59 BASIC BIOLOGICAL SCIENCES↗
Thoughts and Hypotheses on the Metrics and Needs for the Stability of Highly Inverter-Based Island Systems
As levels of wind, photovoltaics (PVs), and battery energy storage on power systems rise, it is useful to be able to quickly compare how much generation (instantaneous power and annual energy) comes from these inverter-based resources (IBRs). A commonly used metric is the percentage of inverter-based generation (% IBR) in a dispatch scenario (as a percentage of the total generation or total load), which can be useful for estimating when certain operational challenges arise (high rates of change of frequency, underdamped control interactions, low system strength, low fault current availability, and so on). Another similar and commonly used metric pioneered on the relatively large island of Ireland, system nonsynchronous penetration (SNSP), is similar to the % IBR and is subject to similar limitations. SNSP has the advantage of handling high-voltage dc in a defined way.
fault currents↗
Enhancing Distribution System Resiliency Using Grid-Forming Fuel Cell Inverter: Preprint
Legacy inverters interfacing distributed energy re-sources are traditionally grid-following (GFL) in nature. GFL assets typically follow real power and reactive power set points. Recently, inverters with grid-forming (GFM) capability are gaining attention as GFM assets can increase the resiliency of the distribution system under stressed conditions. These GFM inverters can use photovoltaics, batteries, or fuel cells as their energy source. In this paper, we present information on inverters interfacing fuel cell assets, specifically with GFM capability. By introducing a fuel cell powered GFM coupled with hydrogen production and storage, the GFM can continuously provide GFM activities during periods of low renewable resource availability, and/or during power outages exceeding typical electric battery duration. Finally, we present information on the need for updates on interconnection and interoperability standards that can be leveraged by utilities for including fuel cell inverters in their asset mix.
fuel cell inverters↗
Autonomous Energy Systems: Building Reliable, Resilient, and Secure Electrified Communities
Technological changes across energy systems are forcing utilities and operators to reconsider their methods for managing power delivery, but few operators have adopted advanced controls and operational software. Their challenge is that every system has peculiar requirements, and the available solutions are relatively new, untested, and difficult to integrate into an operational environment. Through extensive collaboration with utilities and cooperatives, the National Renewable Energy Laboratory has realized the need for autonomous and optimized management of energy resources, leading to the development of Autonomous Energy Systems, a packaged set of controls that is ready to be integrated into existing control rooms.
automation↗
Modeling Power Plant Siting Opportunities and Constraints in the Eastern Interconnection
The electrical transmission grid is a critical part of the U.S. national infrastructure, and its modernization is a U.S. Department of Energy (DOE) priority. We describe the Energy Zones Mapping Tool (EZMT), a unique, powerful, and public web-based system with multi-criteria decision analysis (MCDA) models for more than 20 power plant technologies and many other capabilities. Through a case study on natural gas combined cycle (NGCC) power plants in the Eastern Interconnection (EI), we provide an example of incorporating an EZMT MCDA model into a larger planning context, with projections of interconnection-level capacity expansion, thermoelectric power plant retirements, and water availability. Our results provide insights on candidate NGCC site distributions and the criteria influencing them. The case study provides both an efficient methodology for performing similar analyses and hundreds of candidate NGCC power plant sites that can be studied in more detail as potential project sites.
20 FOSSIL-FUELED POWER PLANTS↗
Applying the Risk Management Framework: The Distributed Energy Resource Risk Manager
As part of a multiyear effort, the National Renewable Energy Laboratory (NREL) has dedicated resources to understand and identify cybersecurity weaknesses in distributed energy resources (DERs) by performing assessments. Due to a lack of standardization and rapidly increasing adoption of DERs, there is a critical need to address cybersecurity needs for DER systems in an interactive way. Furthermore, federal agencies, which are required to obtain an authority to operate, are challenged by the complexities of including their DERs. To help meet this need, in early 2020, NREL released the Distributed Energy Resources Cybersecurity Framework (DERCF) and accompanying Web application. This process is supported by the Risk Management Framework (RMF) developed by the National Institute of Standards and Technology. This project, referred to as the DERCF RMF application, expands on the existing DERCF work to include methods that support walking a user through the seven RMF steps. The tool will be available for download at no cost from [link ]. The purpose of this paper is to describe the steps the DERCF team at NREL took to understand Steps 1-5 of the RMF process. Additionally, this document will identify future work on the first five steps as well as a plan for Steps 6 and 7.
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GMLC Technical Assistance to States: DER Interconnection Workshop #3
NREL hosted this workshop to help state public utility commissions (PUCs) understand the materials and support available to assist in distributed energy resource (DER) interconnection activities. This workshop focused on DER interconnection and included four speakers, whose material will be posted at https://www.nrel.gov/grid/ieee-standard-1547/workshops.html. The slides in this deck were for the facilitator to provide context to the attendees as well as additional resources on the topics discussed.
DER↗
Developing Renewable Energy Applications for Water Treatment Technologies and Transmission
The need for smaller treatment facilities for more isolated or rural municipal entities and industrial water treatment facilities continues to grow. From environmental justice and energy needs in remote communities that have never been serviced by larger scale utilities to smaller onsite treatment requirements to meet discharge regulations, energy efficient treatment systems that can leverage renewable energy sources can be an important asset to owners. Climate change and population dynamics have changed the needs for distributed water treatment systems and the manner in which they function. New treatment technologies that can operate under dynamic conditions and leverage variable energy sources (e.g., renewable energy, RE) can provide substantial efficiencies and cost savings. The National Renewable Energy Laboratory (NREL) is embarking on research projects that will perform multi-scale studies to investigate treatment technologies on varying source water quality that can operate under unsteady state flow conditions (timing) in response to energy availability, supply, and demand. A pilot-scale system will be built to assess modularity and scaling of treatment system operating conditions and performance. Modeling will also be used to determine feasibility and integration into grid emulation platforms to understand the use and value of operation with RE. Optimization will be completed for the treatment and distribution systems to document strengths and weaknesses of using RE power sources. Results of this effort will advance dynamic water treatment understanding under variable conditions and integration of RE sources to power these systems. This will lead to further decarbonization of municipal and private treatment facilities while supporting a paradigm shift in water treatment process operation and system design to achieve net-zero goals and move toward energy-sensitive resilient water treatment systems.
ENERGY PLANNING, POLICY, AND ECONOMY↗
Validating Protection System Behavior with Machine Learning in a Master State Overseer
As power system protection devices continue the widespread transition from analog to digital, they become increasingly intricate. The internal functions and communication between critical grid components must now be significantly more complex to keep up with the demands of the modern smart grid. This brings increased difficulty in maintenance and monitoring, making it harder to identify potential misoperation, power anomalies, and cyber threats. Such issues are often only pinpointed after an exhaustive and costly post-mortem analysis, when a major outage or damage has already occurred. A solution is needed for validating protection systems as they operate, independently evaluating grid state and confirming whether the protection system is behaving accordingly. As opposed to incident response, this acts as a constant verification mechanism that raises a flag when subtler issues are noticed, catching them earlier and preventing larger incidents. This work presents the implementation of such a system, expanding on the prototype developed by the authors in a previous paper. This is accomplished with a machine learning (ML) system capable of validating the performance of protection systems by classifying anomalous events and characterizing protection system responses based solely on available current and voltage measurements. Additionally, this system is contextualized within a larger, modular Master State awareness Overseer (MSO) framework, responsible for monitoring, analyzing, and managing an electric grid.
24 - POWER TRANSMISSION AND DISTRIBUTION↗
Economic Case for Replacing High-Emitting Peaker Plants with Fuel Cells for Automotive Applications
The identification of clean and cost-effective solutions to replace high-emitting peaker plants and support a just transition is a challenge faced by utilities across the US today. However, falling costs of hydrogen production as well as the widespread availability of fuel cells for automotive applications have made them an attractive option for a zero-emission peak power supply. This study evaluates the techno-economics, operation, and environmental justice impacts of siting a peaker plant based on fuel cells for automotive applications through the lens of the existing Intermountain Power Plant, in order to supply peak power to the Los Angeles basin. Compared to the fossil fuel-fired peakers in operation today, the fuel cell peaker would be lower-cost up to a 17% capacity factor with Inflation Reduction Act incentives while also reducing air pollution in environmental justice communities. With corresponding transmission upgrades, the Intermountain site could host up to a 5 GW fuel cell peaker in the future.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Synthetic residential load models for smart city energy management simulations
The ability to control tens of thousands of residential electricity customers in a coordinated manner has the potential to enact system-wide electric load changes, such as reduce congestion and peak demand, among other benefits. To quantify the potential benefits of demand-side management and other power system simulation studies (e.g. home energy management, large-scale residential demand response), synthetic load datasets that accurately characterize the system load are required. This study designs a combined top-down and bottom-up approach for modelling individual residential customers and their individual electric assets, each possessing their own characteristics, using time-varying queueing models. The aggregation of all customer loads created by the queueing models represents a known city-sized load curve to be used in simulation studies. The three presented residential queueing load models use only publicly available data. An open-source Python tool to allow researchers to generate residential load data for their studies is also provided. The simulation results presented consider the ComEd region (utility company from Chicago, IL) and demonstrate the characteristics of the three proposed residential queueing load models, the impact of the choice of model parameters, and scalability performance of the Python tool.
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Distributed Energy Resources Cybersecurity Framework & Risk Manager
Distributed Energy Resource Cybersecurity Framework (DER-CF) provides a holistic assessment for evaluating the cybersecurity posture of DER systems - filling an important gap that expands upon existing cybersecurity frameworks for more modern energy systems. The DER-CF is available as a written framework and an interactive Web tool. Distributed Energy Resource Risk Manager (DER-RM) process adheres closely to NIST's seven risk management steps: prepare, categorize, select, implement, assess, monitor, and authorize. This added feature is independent of the DER-CF's existing self-assessment and allows managers to focus on the RMF process.
cybersecurity↗