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DOE OSTI2021

Design of Resilient Electric Distribution Systems for Remote Communities: Surgical Load Management using Smart Meters

This paper describes a systematic process of designing resilient electric distribution systems and microgrids using smart meters for surgical load management (SLM) as part of Advanced Metering Infrastructure (AMI). The work focuses on selection approach, integration, and interoperability aspects for AMI in microgrids. SLM is proposed as a granular control methodology for serving selective critical loads across different distribution feeders in the system during extreme events. The surgical load shedding as well as load pick-up provides a robust approach for maximizing critical load served in a resource-constrained electric distribution system or a microgrid. We present the case of a 20 MW islanded microgrid in Cordova, AK, USA, which is the demonstration site for field validation of resilience enhancement technologies for the DOE-funded Grid Modernization project RADIANCE. Cordova microgrid is an islanded distribution grid that provides an environment to prove the approach, and the techniques may also be applicable to other regional distribution systems.

microgrids↗

DOE OSTI2020

Reverse and Forward Engineering of Local Voltage Control in Distribution Networks

The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. As such, in this paper we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with non-incremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the non-incremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms respectively for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This work presents another step towards developing a new foundation—network dynamics as optimization algorithms—for distributed real-time control and optimization of future power networks.

42 ENGINEERING↗

DOE OSTI2021

Clutter Distributions for Tomographic Image Standardization in Ground-Penetrating Radar

Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce 3-D distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude and vary across different arrays, which makes a direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or log-normal distributions. Based on the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter- to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Lastly, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development that needs to be robust across multiple different arrays.

42 ENGINEERING↗

DOE OSTI2018

New Educational Modules Using a Cyber-Distribution System Testbed

At Washington State University (WSU), a modern cyber-physical system testbed has been implemented based on an industry grade distribution management system (DMS) that is integrated with remote terminal units (RTUs), smart meters, and a solar photovoltaic (PV). In addition, the real model from the Avista Utilities distribution system in Pullman, WA, is modeled in DMS. The proposed testbed environment allows students and instructors to utilize these facilities for innovations in learning and teaching. For power engineering education, this testbed helps students understand the interaction between a cyber system and a physical distribution system through industrial level visualization. The testbed provides a distribution system monitoring and control environment for students. Compared with a simulation based approach, the testbed brings the students' learning environment a step closer to the real world. The educational modules allow students to learn the concepts of a cyber-physical system and an electricity market through an integrated testbed. Furthermore, the testbed provides a platform in the study mode for students to practice working on a real distribution system model. Here, this paper describes the new educational modules based on the testbed environment. Three modules are described together with the underlying educational principles and associated projects.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Topology Estimation using Graphical Models in Multi-Phase Power Distribution Grids

Power distribution grids are structurally operated radially, such that energized lines form a collection of trees with a substation at the root of each tree. The operational topology may change from time to time, however tracking these changes, even though important for the distribution grid operation and control, is hindered by limited real-time monitoring. This work develops a learning framework to reconstruct the radial operational structure of the distribution grid from synchronized voltage measurements. To detect operational lines our learning algorithm uses conditional independence tests for continuous random variables that is applicable to a wide class of probability distributions and Gaussian injections in particular. Furthermore, our algorithm applies to the practical case of unbalanced three-phase power flow. The performance is validated on AC power flow simulations over three phase IEEE distribution grid test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2019

Individual Load Model Parameter Estimation in Distribution Systems Using Load Switching Events

There currently exists a mature literature on modeling the aggregate load of a distribution feeder by making use of measurements at its feeder-head at substation. The primary application of such feeder-aggregated load models is in sub-transmission or transmission system analysis. However, there is a growing need in practice also to model each individual load across the feeder. If available, such individual load models have applications in power distribution system analysis, e.g., to better integrate distributed energy resources or to improve power quality and reliability. Motivated by this observation, in this paper, we propose a new method for individual load modeling in power distribution systems. It works by using the measurements only at the feeder-head. It takes an innovative approach to analyzing the load switching events across the distribution feeder itself, instead of or in addition to relying on upstream voltage events that are commonly used in feeder-aggregated load modeling. By tracking the downstream load switching events, the proposed method can make a robust estimation of the ZIP load model parameters for all individual loads. The proposed method is examined on small illustrative test-feeders as well as the IEEE 33-bus test system under various operating scenarios. The adverse impact of errors in measurements and system parameters are also investigated on the performance of the developed load modeling method.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2018

A New LMP-Sensitivity-Based Heterogeneous Decomposition for Transmission and Distribution Coordinated Economic Dispatch

Transmission and distribution coordinated economic dispatch (TDCED) will be necessary to settle the problems caused by high penetrations of distributed generators in future grids. An efficient heterogeneous decomposition algorithm (HGD) was proposed to solve the TDCED, but it may diverge occasionally. To overcome this shortcoming, a new HGD (N-HGD) with enhanced convergency is proposed and studied in this paper. Compared with the previous HGD, the N-HGD similarly decomposes the TDCED into transmission ED and distribution ED subproblems, but it considers the sensitivity of the boundary locational marginal price to load injections to enhance convergency and avoid divergence. Two kinds of sensitivity computing methods, M-c and M-p, are studied. M-c is based on a centralized sensitivity equation, executed on the transmission side. M-p is based on a probing mechanism and can be executed distributedly on the distribution side. The reason for the N-HGD's improved convergency is theoretically analyzed, and the optimality is proven. Numerical tests show that N-HGD performs better than the original HGD, as well as a penalty-based modified HGD that needs a careful parametertuning process. Furthermore, compared with M-c, an M-p-based N-HGD is preferable because it has a much lower computational cost and no additional communicational cost.

distribution↗

DOE OSTI2020

Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systems

This paper develops a model-free volt-VAR optimization (VVO) algorithm via multi-agent deep reinforcement learning (DRL) in unbalanced distribution systems. This method is novel since we cast the VVO problem in distribution networks to an intelligent deep Q-network (DQN) framework, which avoids solving a specific optimization model directly when facing time-varying operating conditions in the systems. We consider statuses/ratios of switchable capacitors, voltage regulators, and smart inverters installed at distributed generators as the action variables of the agents. A delicately designed reward function guides these agents to interact with the distribution system, in the direction of reinforcing voltage regulation and power loss reduction simultaneously. The forward-backward sweep method for radial three-phase distribution systems provides accurate power flow results within a few iterations to the DRL environment. The proposed method realizes the dual goals for VVO. We test this algorithm on the unbalanced IEEE 13-bus and 123-bus systems. Numerical simulations validate the excellent performance of this method in voltage regulation and power loss reduction.

41 EE - Solar Energy Technologies Office (EE-4S)↗

DOE OSTI2021

Resource selection functions based on hierarchical generalized additive models provide new insights into individual animal variation and species distributions

Habitat selection studies are designed to generate predictions of species distributions or inference regarding general habitat associations and individual variation in habitat use. Such studies frequently involve either individually indexed locations gathered across limited spatial extents and analyzed using resource selection functions (RSFs) or spatially extensive locational data without individual resolution typically analyzed using species distribution models. Both analytical methodologies have certain desirable features, but analyses that combine individual- and population-level inference with flexible non-linear functions may provide improved predictions while accounting for individual variation. Here, we describe how RSFs can be fit using hierarchical generalized additive models (HGAMs) using widely available software, providing a means to explore individual variation in habitat associations and to generate species distribution maps. We used GPS tracking data from golden eagles Aquila chrysaetos from across eastern North America with four environmental predictors to generate monthly distribution models. We considered three model structures that assumed different amounts of individual variation in the functional relationship between predictors and habitat use and used k-fold cross-validation to compare model performance. Models accounting for individual variability in shape and smoothness of functional responses performed best. Eagles exhibited the least amount of individual variation in response to land cover variables during winter months, with most individuals more closely adhering to the population-level trend. During the summer months, eagles exhibited more substantial individual variation in shape and smoothness of the functional relationships, suggesting some need to account for individual variation in eagle habitat use for both inferential and predictive purposes, during this time of year. Because they allow users to blend flexible functions with random effects structures and are well-supported by a variety of software platforms, we believe that HGAMs provide a useful addition to the suite of analyses used for modeling habitat associations or predicting species distributions.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Decisive role of wriggling vibrations in the formation of angular and spin distributions of products originating from binary and ternary fission of oriented nuclei

It is shown that the multiplicities and angular and energy distributions of neutrons and photons evaporated from thermalized fragments originating from the spontaneous and low-energy induced fission of nuclei, the relative yields of ground and isomeric states of final fragments, and the features of delayed neutrons emitted upon the beta decay of the above fragments can successfully be described by employing nonequilibrium distributions of spins and relative orbital angular momenta of fission fragments formed in the vicinity of the scission point for the fissile nucleus being studied. It is also shown that these distributions, which are characterized by large mean values of the spins and orbital angular momenta directed orthogonally to the symmetry axis of the fissioning nucleus are successfully constructed upon simultaneously taking into account zero-mode transverse wriggling and bending vibrations of a fissile compound nucleus in the vicinity of its scission point, the wriggling vibrations being dominant. It is confirmed that the zero-mode wriggling vibrations considered immediately above are directly involved in the formation of the angular distributions of fragments originating from the spontaneous and low-energy fission of nuclei. This makes it possible to describe successfully such distributions for photofission fragments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Distributionally Robust Partially Observable Markov Decision Process with Moment-Based Ambiguity

In this paper, we consider a distributionally robust partially observable Markov decision process (DR-POMDP), where the distribution of the transition-observation probabilities is unknown at the beginning of each decision period, but their realizations can be inferred using side information at the end of each period after an action being taken. We build an ambiguity set of the joint distribution using bounded moments via conic constraints and seek an optimal policy to maximize the worst-case (minimum) reward for any distribution in the set. We show that the value function of DR-POMDP is piecewise linear convex with respect to the belief state and propose a heuristic search value iteration method for obtaining lower and upper bounds of the value function. We conduct numerical studies and demonstrate the computational performance of our approach via testing instances of a dynamic epidemic control problem. Our results show that DR-POMDP can produce more robust policies under misspecified distributions of transition-observation probabilities as compared to POMDP but has less costly solutions than robust POMDP. The DR-POMDP policies are also insensitive to varying parameter in the ambiguity set and to noise added to the true transition-observation probability values obtained at the end of each decision period.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems

This paper studies the trade-off between the degree of decentralization and the performance of a distributed controller in a linear-quadratic control setting. We study a system of interconnected agents over a graph and a distributed controller, called k-distributed control, which lets the agents make control decisions based on the state information within distance k on the underlying graph. This controller can tune its degree of decentralization using the parameter k and thus allows a characterization of the relationship between decentralization and performance. We show that under mild assumptions, including stabilizability, detectability, and a subexponentially growing graph condition, the performance difference between k-distributed control and centralized optimal control becomes exponentially small in k. Finally, this result reveals that distributed control can achieve near-optimal performance with a moderate degree of decentralization, and thus it is an effective controller architecture for large-scale networked systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Analytical Solutions for Precipitation Size Distributions at Steady State

Analytical solutions are derived for the steady-state size distributions of precipitating rain and snow particles assuming growth via collection of suspended cloud particles. Application of the Liouville equation to the transfer of precipitating mass through size bins in a “cascade” yields a characteristic gamma distribution with a Marshall–Palmer exponential tail with respect to particle diameter. For rain, the principle parameters controlling size distribution shape are cloud droplet liquid water path and cloud updraft speed. Stronger updrafts lead to greater concentrations of large precipitating drops and a peak in the size distribution. The solutions provide a means for relating size distributions measured in the air or on the ground to cloud bulk microphysical and dynamic properties.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Power distribution estimation method for SMR using ex-core detectors: experimental demonstration by plural control rod patterns at KUCA

The power distribution estimation method based on the ex-core detectors, or PHOEBE, was demonstrated at Kyoto University Critical Assembly, KUCA. Generally, core monitoring systems use in-core neutron detectors. Since inside the core is a harsh environment, the maintainability and reliability of the detectors are deteriorated. On the other hand, the environment outside the core is milder: core monitoring by the ex-core detectors improves the maintainability and reliability especially for small modular reactors and micro-reactors. However, neutron information from the inner region of the core is lost at the ex-core detectors. To recover the information, the authors proposed to utilize the power correlation between the fuel regions. PHOEBE concept was demonstrated at KUCA with distorted power distributions simulated by control rod patterns. The relative power distribution estimated by PHOEBE agreed with that calculated by Monte Carlo simulation code MVP. PHOEBE approximately reproduced the trend of the distorted power distributions calculated by MVP code. By contrast, the case of without power correlation between fuel regions produced significant different power distributions. Therefore, the advantage of considering the power correlation between fuel regions was also demonstrated. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2026

Loss Factors for Small Distributed Wind Turbines Based on Field Data in the United States

While wind energy production loss due to unavailability, environmental impacts, curtailment, and other causes has been studied and characterized at the utility-scale wind farm level, observation-based characterization of project loss is lacking for distributed wind energy, particularly for projects involving small wind turbines. Contemporary tools and research that support pre-construction distributed wind energy characterization present a wide range of default loss factors to convert gross energy estimates to net: 7-18%. We hypothesize that we can use generation observations from operational distributed wind projects to develop more accurate representations of loss. Using a density-based filtering technique on distributed wind power generation timeseries, we determine periods of typical performance and use them with regression algorithms in a measure-correlate-predict fashion to simulate what the generation would have been during periods of atypical or unreported performance. From there, the actual versus predicted generation leads to the establishment of observation-informed loss factors (median = 17%) for small, single turbine installation distributed wind projects.

17 WIND ENERGY↗

DOE OSTI2017

Challenges in reducing the computational time of QSTS simulations for distribution system analysis.

The rapid increase in penetration of distributed energy resources on the electric power distribution system has created a need for more comprehensive interconnection modelling and impact analysis. Unlike conventional scenario - based studies , quasi - static time - series (QSTS) simulation s can realistically model time - dependent voltage controllers and the diversity of potential impacts that can occur at different times of year . However, to accurately model a distribution system with all its controllable devices, a yearlong simulation at 1 - second resolution is often required , which could take conventional computers a computational time of 10 to 120 hours when an actual unbalanced distribution feeder is modeled . This computational burden is a clear l imitation to the adoption of QSTS simulation s in interconnection studies and for determining optimal control solutions for utility operations . Our ongoing research to improve the speed of QSTS simulation has revealed many unique aspects of distribution system modelling and sequential power flow analysis that make fast QSTS a very difficult problem to solve. In this report , the most relevant challenges in reducing the computational time of QSTS simulations are presented: number of power flows to solve, circuit complexity, time dependence between time steps, multiple valid power flow solutions, controllable element interactions, and extensive accurate simulation analysis.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Robust metal-ceramic coaxial cable sensors for distributed temperature monitoring in fossil energy power systems

The project aims to develop a new type of low cost, robust metal-ceramic coaxial cable (MCCC) Fabry-Pérot interferometer (FPI) sensor and demonstrate the capability of cascading a series of FPI sensing points in a single MCCC for real-time distributed monitoring of temperature up to 1000°C. The technical accomplishments of this project include: (i) development of metal and ceramic materials for fabricating the MCCC suitable for use in high temperature (up to 1000oC) fossil fuel, (ii) design, construction and test of single point MCCC-FPI sensors for high temperature measurement at deviation within ±2.7%, (iv) demonstration of the MCCC-FPI sensors for real-time distributed temperature measurement with desired sensitivity, spatial resolution, stability, and response speed that are important to practical applications, and (v) development of a novel MCCC-FPI sensor platform for measuring high frequency dielectric constants for solids and liquids as function of temperature. Single point MCCC-FPI sensors are fabricated based on the FPI parameters, including the relationship between reflector width and reflectance as well as the inter-reflector distance for a FPI, estimated by model calculations. MCCC-FPI sensors have been made using insulators of packed ceramic powders, ceramic tubes, and air or inert gases; outer conductors of various metal tubes; inner conductors of wires of the same metal materials as the tubes used; and reflectors of dense ceramic discs, casted ceramic discs and air gaps. The insulator, conductor and reflector materials are investigated for their thermal, chemical, and structural stabilities under temperatures up to 1000°C in simulated coal-derived syngas. Based on the experimental findings, alumina tube and discs, and stainless steel tube and wire are selected for reflector, insulator and outer and inner conductors, respectively. Air or inert gases are found to be useful for reflectors (in the form of narrow groves) or insulators (in the form of empty chamber cavity between ceramic discs) in construction of MCCC-FPI sensors. Single point MCCC-FPI sensors have been fabricated using stainless steel as conductors, dense α-alumina tubes as dielectric insulators, and two air gaps in the insulator as reflectors. The MCCC-FPI sensor with air gap reflectors has been demonstrated for measuring temperature between 200 and 500°C using microwave as sensing signal in a frequency range of 2 – 8 GHz. The temperature measurement is achieved by monitoring the frequency shift (Δƒ) of a selected resonant peak of the microwave interferogram reflected from the pair of air gap reflectors. The MCCC-FPI sensor exhibited excellent linear temperature-dependence for Δƒ, good accuracy (deviation ±2.7%), and reasonable response speed (<180 s for stabilization). Thermal drifting of the sensing signal was also rather minimal for the MCCC-FPI sensors after operating for more than one week at around 500oC. MCCC-FPI sensors with each containing 2 or 3 pairs of reflectors (i.e. 2 or 3 FPIs in a single cable) have been fabricated and demonstrated for distributed temperature measurement between 250 and 550°C in a microwave frequency range of 3-6 GHz. The measurement is achieved by monitoring the frequency shift of a selected resonant peak of the reflected microwave interferogram, which is obtained by the join-time-domain signal processing method. The results demonstrate that the FPI sensing points in both the three-point and two-point MCCC sensors can measure distributed high temperature with good reproducibility and consistency in multiple runs of measurement tests. The sensor signal displays excellent linear dependence between temperature and the corresponding frequency shift. The 2-point and 3-point MCCC-FPI sensors also exhibit fast response to environmental temperature changes. A three-point MCCC-FPI sensor has been further examined for temperature measurement up to 1000°C for multiple heating-cooling cycles and the results prove that the sensor is suitable for distributed temperature measurement up to 1000°C. MCCC-FPI sensors with more than 4 FPIs point in a single line are made in two structures: the first uses alumina tube as insulation and air gaps as reflectors; and the second uses air chamber as insulation and complete circular alumina discs (2 mm-thick) as reflectors. MCCC-FPIs in neither of the two structures are able to exceed four FPIs in a cable because the reflections from the FPIs are too strong for the microwave to transmit through >4 FPIs over a long distance. Specially shaped alumina discs made by removing parts of the circular discs are found to decrease the microwave reflectance because of reduced reflection areas. However, the smallest reflectance (in terms of percentage of the incident signal) obtained in this work is as high as 7.5%, which is still too large to construct more than 10 FPIs in a single MCCC. Nevertheless, the feasibility of the proposed multipoint MCCC-FPI has been demonstrated using air insulation and 2 mm-thick low-ε r Teflon discs as reflectors on a 2 m-long cable containing 10 FPIs (with reflectance <5% on each FPI). These results indicate that the contrast of dielectric constant between the insulation and reflector has a more significant impact on the reflectance. Thus, future research efforts should also be directed to developing ceramic materials (dense or porous) with low dielectric constant values which, when used as insulation or reflector, can realize large numbers of FPIs in a single cable over a long distance. To assist the development of high temperature dielectric materials, a novel MCCC-FPI sensor platform is developed and successfully demonstrated for measuring high frequency dielectric constants for fluids, packed dielectric solids, and ceramic tube materials and determining ε r dependencies on temperature and chemical composition, etc..

36 MATERIALS SCIENCE↗

DOE OSTI2017

The Distributed Geothermal Market Demand Model (dGeo): Documentation

The National Renewable Energy Laboratory (NREL) developed the Distributed Geothermal Market Demand Model (dGeo) as a tool to explore the potential role of geothermal distributed energy resources (DERs) in meeting thermal energy demands in the United States. The dGeo model simulates the potential for deployment of geothermal DERs in the residential and commercial sectors of the continental United States for two specific technologies: ground-source heat pumps (GHP) and geothermal direct use (DU) for district heating. To quantify the opportunity space for these technologies, dGeo leverages a highly resolved geospatial database and robust bottom-up, agent-based modeling framework. This design is consistent with others in the family of Distributed Generation Market Demand models (dGen; Sigrin et al. 2016), including the Distributed Solar Market Demand (dSolar) and Distributed Wind Market Demand (dWind) models. dGeo is intended to serve as a long-term scenario-modeling tool. It has the capability to simulate the technical potential, economic potential, market potential, and technology deployment of GHP and DU through the year 2050 under a variety of user-defined input scenarios. Through these capabilities, dGeo can provide substantial analytical value to various stakeholders interested in exploring the effects of various techno-economic, macroeconomic, financial, and policy factors related to the opportunity for GHP and DU in the United States. This report documents the dGeo modeling design, methodology, assumptions, and capabilities.

15 GEOTHERMAL ENERGY↗