OT-HEALER: A REAL-TIME DECENTRALIZED SELFSECURITY/ HEALING TOOL FOR DER OT RESILIENCY
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24 POWER TRANSMISSION AND DISTRIBUTION↗
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Impacts of Integrating Topology Reconfiguration and Vehicle-to-Grid Technologies on Distribution System Operation
Autonomous electric vehicles (AEVs) provide unique opportunities to cope with the uncertainties of distributed energy generation in distribution networks. But the effects are limited by both inherent radial topology and the behaviors of decentralized AEVs. As such, we investigate the potential benefits of dynamic distribution network reconfiguration (DDNR), taking into account AEVs' spatial-temporal availability and their charging demand. We propose a mixed integer programming model to optimally coordinate the charging/discharging of AEVs with DDNR, while satisfying AEVs' original travel plan. Numerical studies based on a test system overlaying the IEEE 33-node test feeder and Sioux Falls transportation network show that DDNR and AEV complement each other, which improves the operation of the distribution system. We also conduct sensitivity analyses on inputs including renewable fluctuation and AEVs penetration level.
33 ADVANCED PROPULSION SYSTEMS↗
Mitigating Phase Unbalance for Distribution Systems with High Penetrations of Solar PV (Final Technical Report)
Distribution system operators have traditionally limited unbalance among phases by maintaining similar loadings on each phase. High penetrations of distributed solar PV continually change the net loading on each phase, resulting in time-varying phase unbalances that can damage three-phase devices such as three-phase motors, violate grid codes, and increase technical losses. This project has developed several control strategies for the reactive power outputs of solar PV inverters in order to mitigate power quality issues related to phase unbalance. These control strategies include a decentralized approach that is solely based on local measurements, distributed and grouped approaches that consider subsets of loads and PV generators, and a centralized approach that leverages measurements from a variety of locations in order to compute optimal reactive power setpoints for each inverter. Variants of the controllers handle challenges relevant to practical implementations, including noisy measurements, delayed communications, and reactive power limits. Moreover, the project developed theory that provides convergence guarantees for systems with multiple interacting controllers as well as “balanceability” certificates that ensure satisfaction of phase unbalance requirements with variable loading. The controllers were integrated with NRECA’s Open Modeling Framework (omf.coop) and evaluated using actual distribution system models obtained from several NRECA member utilities. Application of the controllers results in significant improvements to phase unbalance in these test cases with decreases from base case levels of over 3% to under 0.5%, which is within the 2% IEC phase unbalance standard.
14 SOLAR ENERGY↗
Adaptive Control Parameters for Dispersal of Multi-Agent Mobile Ad Hoc Network (MANET) Swarms
A mobile ad hoc network is a collection of independent nodes that communicate wirelessly with one another. This paper investigates nodes that are swarm robots with communications and sensing capabilities. Each robot in the swarm may operate in a distributed and decentralized manner to achieve some goal. This paper presents a novel approach to dynamically adapting control parameters to achieve mesh configuration stability. The presented approach to robot interaction is based on spring force laws (attraction and repulsion laws) to create near-optimal mesh like configurations. In prior work, we presented the extended virtual spring mesh (EVSM) algorithm for the dispersion of robot swarms. This paper extends the EVSM framework by providing the first known study on the effects of adaptive versus static control parameters on robot swarm stability. The EVSM algorithm provides the following novelties: 1) improved performance with adaptive control parameters and 2) accelerated convergence with high formation effectiveness. Simulation results show that 120 robots reach convergence using adaptive control parameters more than twice as fast as with static control parameters in a multiple obstacle environment.
42 ENGINEERING↗
Demonstration of Decentralized Physics-Driven Learning
In typical artificial neural networks, neurons adjust according to global calculations of a central processor, but in the brain, neurons and synapses self-adjust based on local information. Contrastive learning algorithms have recently been proposed to train physical systems, such as fluidic, mechanical, or electrical networks, to perform machine-learning tasks from local evolution rules. However, to date, such systems have only been implemented in silico due to the engineering challenge of creating elements that autonomously evolve based on their own response to two sets of global boundary conditions. Here, we introduce and implement a physics-driven contrastive learning scheme for a network of variable resistors, using circuitry to locally compare the response of two identical networks subjected to the two different sets of boundary conditions. Using this method, our system effectively trains itself, optimizing its resistance values without the use of a central processor or external information storage. Once the system is trained for a specified allostery, regression, or classification task, the task is subsequently performed rapidly and automatically by the physical imperative to minimize power dissipation in response to the given voltage inputs. Here, we demonstrate that, unlike typical computers, such learning systems are robust to extreme damage (and thus manufacturing defects) due to their decentralized learning. Our twin-network approach is therefore readily scalable to extremely large or nonlinear networks, where its distributed nature will be an enormous advantage; a laboratory network of only 500 edges will already outpace its in silico counterpart.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Communication-Constrained Robust Control and Learning of Grid-Connected
The electric grid of things (EGoT) promises great potential for innovative grid services by tapping into vast load flexibility. However, the unique characteristics of EGoT, being a part of the cyber-physical electric power system, present both opportunities and challenges, especially concerning supply-demand balancing, stability, and communication constraints. Traditionally, centralized control was employed to ensure balance and stability in power systems. However, with the massive influx of EGoT devices, new strategies are needed to efficiently coordinate and control these distributed devices for optimal grid operations. While some studies have explored efficiency and economic models, there remains a gap in ensuring reliability under everyday operations and resilience during extreme conditions. Addressing this gap, this project develops the technology for an Energy Service Interface (ESI) that includes novel pricing, control, learning, and distributed optimization algorithms, which will enable utilities to recruit EGoT assets for crucial grid services such as load flexibility, voltage regulation, and situation-awareness. The key novelty of the proposed technology is the careful distribution of learning and control functions across utility and EGoT asset owners such that provably efficient and resilient grid operations are attained while respecting communication and information-exchange constraints. Specifically, the project team develops machine-learning enhanced load modeling methods to allow EGoT asset owners to learn their load capability and flexibility, and develops pricing-based and decentralized learning-based control so that asset owners can coordinate to meet system-wide demand-supply balance and reliability goals. For extreme situations involving high-impact, low-probability catastrophic events (termed the “black-sky” events), the team also develops (1) a “Feeder-Operating Center-on-a-Laptop” (FOCAL) software that can assist utility personnel in leveraging EGoT assets to accelerate the service recovery of damaged feeders, and (2) distributed optimization algorithms that can coordinate the operation points of EGoT devices under severe communication constraints. The proposed technology has been extensively tested and evaluated through simulations and on a testbed. In summary, as we transition into a more interconnected and digital power grid era, our project’s findings and developments offer a pivotal step toward guaranteeing both efficiency and resilience in the face of both everyday operations and rare “black-sky” events.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Complementarities of supply and demand sides in integrated energy systems
New small-scale demand-side technologies, such as micro combined heat and power technologies (uCHPs) and heat pumps (HPs), offer opportunities to increase system-wide efficiency. Furthermore, the technical and economic characteristics of demand-side technologies could also complement the supply side by providing system services, such as adequacy and flexibility, which are increasingly required due to high variable renewable energy penetration. A capacity expansion methodology that captures the interactions between the supply and demand sides is developed to find cost-optimal and adequate investment portfolios. For the case study presented, the integrated energy system leverages the technical and economic complementarities of different supply and demand-side technologies. As a result, system integration using demand-side technologies improves the value proposition for decentralization given that the technologies can provide heat demand, while also meeting electricity demand and providing adequacy and flexibility.
24 POWER TRANSMISSION AND DISTRIBUTION↗
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↗
Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution
Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.
Bretas, Arturo Suman↗
Data-Driven Unit Commitment Refinement - a Scalable Approach for Complex Modern Power Grids
Integration of renewable generation, which is often intermittent and decentralized, substantially increases the stochasticity and complexity of power grid operations. Future power systems planning will require significant computational capability to evaluate balance between demand and supply under varying conditions, both temporally and spatially. The standard approach for generation unit commitment is to use mixed-integer linear programming to find the optimal generation schedule considering ramping and generator constraints. In the future grid this poses computational scalability challenges because generation and demand are not known with certainty due to stochasticity in weather and complexity of the grid. To address this challenge, we present a data-driven unit commitment approach that can efficiently include stochastic weather impacts and contingency considerations to improve unit commitment. Our approach uses graph-based data analytics techniques on solutions to the security constrained (and possibly stochastic) economic dispatch problem to identify potential improvements to a given unit commitment. Recent breakthroughs in fully-parallel stochastic economic dispatch software allow this approach to be scalably deployed. Simulations on synthetic South Carolina and Texas grids show this method can improve grid reliability with security constraints over a set of contingencies, while also meaningfully lowering total generation cost.
Holt, Timothy↗
Using Mobile Charging Drones to Mitigate Battery Disruptions of Electric Vehicles on Highways
Our research explores innovative solutions to address the challenge of battery disruptions in electric vehicles (EVs) on highways. We propose a centralized fleet ownership model where a company manages a fleet of Mobile Charging Drones (MCDs) guided by a k-VRP (Vehicle Routing Problem) framework. This model is designed to tackle a multi-objective optimization issue with three primary goals: reduction of the overall operating costs, decrease in the cumulative waiting time, and minimization of the combined operating costs and waiting times. This approach extends beyond the usual VRP constraints, encompassing specific limitations for both MCDs and disrupted EVs (DEVs). Additionally, our study delves into the concept of decentralized fleet ownership through the lens of crowdsourcing. Preliminary numerical analyses indicate that the capital cost of MCDs is a significant factor on the charging service, and the system performance is sensitive to DEV owner's value of time (VOT) when VOTs are relatively low.
battery disruption↗
Distributed Load Shedding Application Architecture and Bi-Level Predictive Estimator Algorithm
Increasing penetrations of distributed renewables are decreasing the effectiveness of traditional decentralized under-frequency load shedding (UFLS) schemes. As more distribution circuits begin to back-feed the transmission system, operation of traditional UFLS may exacerbate frequency instability. This paper presents the conceptual framework for a data-rich environment to coordinate UFLS across multiple distribution providers based on the laminar coordination framework in order to ensure optimal adaptive setting of UFLS relays. Communication and control are enabled through a distributed implementation of the IEC 61968-1 Common Information Model message bus structure. In addition to the proposed architecture, a novel adaptive UFLS scheme informed by a bi-level state estimator to create optimal relay setpoints is introduced. Initial simulation results are presented for the IEEE 14-bus test system on scenarios leading to mis-operation of traditional UFLS.
Anderson, Alexander A.↗
Reinforcement Learning-based Output Structured Feedback for Distributed Multi-Area Power System Frequency Control
Load frequency control (LFC) is a key factor to maintain the stable frequency in multi-area power systems. As the modern power systems evolve from centralized to decentralized paradigm, LFC needs to consider the decentralized scheme that considers limited information from the information-exchange graph for the generator control of each interconnected area. This paper aims to solve a data-driven constrained LQR problem with mean-variance risk constraints and output structured feedback, and applies this framework to solve the LFC problem in multi-area power systems. By reformulating the constrained optimization problem into a minimax problem, the stochastic gradient descent max-oracle (SGDmax) algorithm with zero-order policy gradient (ZOPG) is adopted to find the optimal feedback gain from the learning, while guaranteeing the convergence. In addition, to improve the adaptation of the proposed learning method to new or varying models, we construct an emulator grid that approximates the dynamics of a physical grid and performs training based on this model. Once the feedback gain is obtained from the emulator grid, it is applied to the physical grid with a robustness test to check whether the controller from the approximated emulator applies to the actual system. Numerical tests show that the obtained feedback controller can successfully control the frequency of each area, while mitigating the uncertainty from the loads, with reliable robustness that ensures the adaptability of the obtained feedback gain to the actual physical grid.
Kwon, Kyung-bin↗
Assessment of CReST-VCT Deployment in Real-Time Environments: Communication System Examination
Incorporation of decentralized generation or distributed energy resources (DERs) such as solar and wind, is transforming the electricity grid. These resources have the potential to provide greater overall value when they are grid connected and coordinated between transmission and distribution systems. The Coordinated Real-Time Sub-Transmission Volt-VAr Control Tool (CReST-VCT) has been proposed to provide co-optimization between transmission and distribution systems maximizing DER utilization while maintaining overall grid stability. In this paper, we examine the communication systems that are necessary to support the integration of the CReST-VCT optimization mechanism in a real-time operational environment. The required communication interfaces will be identified based on the operation model of CReST-VCT. The characteristics of the communication systems at these interfaces will be described to establish the context within which CReST-VCT will operate. The related protocols and cybersecurity domain will be reviewed. Potential issues and concerns will then be identified to guide subsequent integration efforts.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Optimum Aggregation and Control of Spatially Distributed Flexible Resources in Smart Grid
This paper presents an algorithm to optimally aggregate spatially distributed flexible resources at strategic microgrid/smart-grid locations. The aggregation reduces a distribution network having thousands of nodes to an equivalent network with a few aggregated nodes, thereby enabling distribution system operators (DSOs) to make faster operational decisions. Moreover, the aggregation enables flexibility from small distributed flexible resources to be traded to different power and energy markets. A hierarchical control architecture comprising a combination of centralized and decentralized control approaches is proposed to practically deploy the aggregated flexibility. The proposed method serves as a great operational tool for DSOs to decide the exact amount of required flexibilities from different network section(s) for solving grid constraint violations. The effectiveness of the proposed method is demonstrated through simulation of three operational scenarios in a real low voltage distribution system having high penetrations of electric vehicles and heat pumps. Finally, the simulation results demonstrated that the aggregation helps DSOs not only in taking faster operational decisions, but also in effectively utilizing the available flexibility.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Hydrogen Production, Grid Integration, and Scaling for the Future
The Hydrogen Production, Grid Integration, and Scaling for the Future project will inform clean hydrogen production deployment across the planning, procurement, and operation stages of hydrogen production on the grid. Hydrogen production from renewables is a clean source of fuel which is near zero for greenhouse gas emissions and criteria pollutants. The results from this project will inform entities looking to build clean energy projects that produce good paying jobs in manufacturing, installation, maintenance and operation of these facilities. This project provides system characterization examples, multiple configurations, optimizations, and suggested metering and custody transfer points through multiple scenarios and hardware testing of NREL's state-of-the-art 1.25 MW polymer electrolyte membrane (PEM) electrolyzer system to characterize system performance in relevant scenarios. This work also creates a digital twin for emulation in the Advanced Research on Integrated Systems (ARIES) virtual environment and performs hardware-in-the-loop (HIL) testing of pilot-scale, decentralized, and centralized hydrogen systems.
centralized hydrogen production↗
Herbaceous Feedstock 2022 State of Technology Report
The U.S. Department of Energy promotes production of advanced liquid transportation fuels from lignocellulosic biomass by funding fundamental and applied research that advances the state of technology (SOT). As part of its involvement in this mission, Idaho National Laboratory completes an annual SOT report for nth-plant and 1st-plant herbaceous biomass feedstock logistics. The purpose of the SOT is to provide the status of feedstock supply system technology development for herbaceous biomass to biofuels relative to technical targets and cost goals from specific design cases, based on data and experimental results. Although conventional feedstock supply systems form the backbone of the emerging biofuels industry, they have limitations that restrict widespread implementation on a national scale. To meet the demands of the future industry, the feedstock supply system must shift from the conventional system to what has been termed “advanced” supply systems. In advanced designs, a distributed network of aggregation and processing centers, termed “depots,” are employed near the points of biomass production (i.e., the field or forest) to reduce feedstock variability and produce feedstocks of a uniform format, moving toward biomass commoditization. The 2022 Herbaceous SOT is part of a vision of achieving an implemented advanced feedstock supply system, which produces a stable, tradable commodity at the decentralized distributed depot. It utilizes feedstock fractionation by incorporating technologies that can separate the biomass into its anatomical fractions (leaves, husks, stems and cobs) to reduce impurities and produce fractions that satisfy downstream quality considerations. By using a series of air classification steps, this strategy can reduce the extrinsic ash in corn stover and produce enriched tissue fractions that can be blended to a conversion specification or converted individually in optimized biochemical conversion campaigns. Additionally, a majority of the leaves (which do not meet the quality specification) are separated out early and can be supplied to alternate markets. The 2022 Herbaceous SOT incorporates an advanced biomass fractionation and processing system to produce pellets enriched tissues from three-pass corn stover. The resulting enriched pellets are delivered to the biorefinery individually where they can be blended to a specification or converted in campaigns where the conditions are optimized for each tissue. Unused fractions can be sent to a a midstream market or to a different conversion process that is better suited to their properties to offset the cost of the delivered feedstock. The main benefits from the proposed system can be summarized as: (1) $6.86/dry ton (2016$) lower cost for the air classification due to elimination of the requirement to discard the high ash lights fraction; (2) $1.56/dry ton lower delivered cost by selling the unsuitable leaf fraction into the feed market as a midstream co-product (assuming a selling price that is 11% higher than their cost of production); (3) 0.98% increase in carbohydrate content (from 60.16% to 61.14%); and (4) 0.97% decrease in ash content (from 6.00% to 5.03%) compared to the 2021 Herbaceous SOT. Overall, the 2022 nth-plant Herbaceous SOT predicts a modeled delivered feedstock cost of $78.64/dry ton (2016$) if it is assumed that the enriched leaf fraction is sold at its production cost; this is a slight increase of $0.43/dry ton increase from the 2021 Herbaceous SOT nth-Supply case cost. The increased cost derived from a $0.38/dry ton increase in transportation and handling cost to procure more biomass (to replace the enriched leaf fraction that was not delivered to the biorefinery. The total preprocessing cost was $0.27/dry ton higher than the 2021 result because of updates to energy consumption, purchasing price and dry matter loss data for the rotary shear ($3.00/dry ton increase) and the pelleting mill ($4.52/dry ton increase). The data utilized were generated in pilot-scale tests in the Biomass Feedstock National User Facility (BFNUF) at INL and at Forest Concepts, including tests for rotary shear and pelleting of the air classified fractions. A greenhouse gas emissions analysis was performed by Argonne National Laboratory using the most up to date version of the Greenhouse Gases, Regulated Emissions, and Energy use in Transportation model (GREET®). The analysis showed an increase of 17.34 kg CO2e/dry ton from the 2021 SOT (67.71 kg CO2e/ton in the 2021 Herbaceous SOT to 85.05 kg CO2e/ton in the 2022 Herbaceous SOT). The net increase is primarily attributed to increased energy consumption in pelleting mill.
09 BIOMASS FUELS↗
Planning decentralized urban renewable energy systems using algal cultivation for closed-loop and resilient communities
To tackle climate challenges, communities need to harvest renewable energy and resources on site locally to close the loops for enhancing the resilience of communities facing unpredictable and uncertain future changes. A decentralization planning of urban renewable energy systems is proposed by treating urban waste streams and producing biomass through applying algal biotechnology. When applying algal technology as a renewable and decentralized energy source in urban systems, the overall performance can vary by levels of urban nutrients, solar and CO 2 resources, and the transportation cost when considering its application to different urban densities, urban form, and the spatial scale of urban settings. This research explores three potential impacts on the algal system’s energy performance: (1) urban density, (2) urban form in different contexts, and (3) spatial scale. The research examines the impacts by testing urban settings given in actual contexts in Atlanta, Georgia, USA. Four neighborhoods representing the high-density urban, mid-density urban, mixed suburban, and typical suburban areas are investigated. The density-scale–performance relationships are explored through testing different urban forms of neighborhoods in both hypothetical and actual neighborhood settings. A GIS-based model is developed to estimate the overall energy performance of the decentralized renewable energy system in urban environments. Results show that the energy performance is positive mainly for high-density urban neighborhoods with small-to-medium scales, up to 0.36 MJ per ton of municipal solid wastes for actual settings and 0.37 MJ for hypothetical cases. Neighborhoods with higher density have higher energy performance while up scaling has negative effects on the energy performance with a low degree of significance. Optimal scales are found as a 1-km radius in real test beds and 1.3 km in hypothetical settings, in which the results show trade-offs between scaling effects in the system efficiency gain and the transportation cost increase.
Quan, Steven Jige↗