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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Application of Banking Scoring and Rating for Coherent Risk Measures in Electricity Systems ABSCORES

This project developed a framework for asset and system risk management that can be incorporated into current electricity system operations to improve economic efficiency and establish an Electric Assets Risk Bureau. We leveraged scoring and ratings from banking and financial institutions alongside current optimization methods in dispatching power systems to help system operators and electricity markets schedule resources. This approach is based on the observation that there are major discrepancies between the power scheduled by a system operator and the actual power generated/consumed. These discrepancies—exacerbated by unplanned contingencies (e.g., natural disasters)—are caused by multiple factors, including the different financial, environmental and risk preferences of power producers, consumers, and aggregators. We developed a framework that counteracts two failures in electricity system operations: imperfect information and missing markets for products. The technical approach included five tasks. Tasks 1 and 2 supported the development of risk scores at the asset level with historical data collected for this project. Tasks 3, 4, and 5 incorporated scoring into decision-making at the system level. The proposed effort achieved PERFORM's Program Objectives because the proposed outputs and algorithms do not exist in the electricity industry and are an innovative approach to managing risk. Since the acknowledged need to better assess and act upon risk profiles for grid assets has not been met by the industry, this project will also impact ARPA-E's Mission Areas, including improving energy efficiency and giving the U.S. a technological lead in advanced energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NASA NTRS1993

Planning: Management of predictability and uncertainty and keeping abreast of developments

The purpose of this study is to propose method to set up and control of a space mission plan such as that of the HERMES spaceplane. The interest of this subject, other than its complexity, is due to the need to manage imprecision and uncertainty during a mission, as well as changes in between missions. Under these conditions, the set up and control of a flight plan require certain special attention and this has led us to define a certain number of qualities: mastery of complexity in order to resolve conflicts between activities: configuration, resource and time management; consideration of various criteria such as risk minimization or the attainment of mission objectives; robustness and flexibility to allow for hazards and deviations from the norm during operation without having to draw up new plans; aptness for replanning by making changes to the plan without having to set up the whole plan again; and memorization and explanation facility in order to manage developments between missions.

Bastien-Thiry, Christophe↗

DOE OSTI2020

DeepGrid: Robust Deep Reinforcement Learning-based Contingency Management

Increasing uncertainty raised by the integration of renewable energy resources requires an enormous number of simulations to be carried out for the security assessment of the power grid. However, it is challenging to assess the steady-state and dynamic security indices for different system contingency events by doing an exhaustive analysis in real-time due to the computational and communication constraints. One promising solution is using data-driven techniques along with the system models to train an intelligent contingency management framework to better handle the contingencies in real-time. Nevertheless, implementing a data-driven technique to obtain the best remedial actions necessitates to account for the effect of the measurement noise on the performance of the contingency management. To tackle these challenges, we leverage a robust deep reinforcement learning (DRL) algorithm called Double Deep Q-Network (DDQN) to design a recommender system capable of prescribing optimal control actions with the help of the real-time digital simulator (RTDS). The use of RTDS system in combination with the advanced DRL algorithm allows to explore a wide variety of system contingencies in order to derive better remedial actions. The performance of the proposed algorithm is evaluated in IEEE 9-bus system under different loading conditions, and different network configurations in presence of noisy measurements.

Ghasemkhani, Amir↗

NASA NTRS2005

Handling Trajectory Uncertainties for Airborne Conflict Management

Airborne conflict management is an enabling capability for NASA's Distributed Air-Ground Traffic Management (DAG-TM) concept. DAGTM has the goal of significantly increasing capacity within the National Airspace System, while maintaining or improving safety. Under DAG-TM, autonomous aircraft maintain separation from each other and from managed aircraft unequipped for autonomous flight. NASA Langley Research Center has developed the Autonomous Operations Planner (AOP), an onboard decision support system that provides airborne conflict management (ACM) and strategic flight planning support for autonomous aircraft pilots. The AOP performs conflict detection, prevention, and resolution from nearby traffic aircraft and area hazards. Traffic trajectory information is assumed to be provided by Automatic Dependent Surveillance Broadcast (ADS-B). Reliable trajectory prediction is a key capability for providing effective ACM functions. Trajectory uncertainties due to environmental effects, differences in aircraft systems and performance, and unknown intent information lead to prediction errors that can adversely affect AOP performance. To accommodate these uncertainties, the AOP has been enhanced to create cross-track, vertical, and along-track buffers along the predicted trajectories of both ownship and traffic aircraft. These buffers will be structured based on prediction errors noted from previous simulations such as a recent Joint Experiment between NASA Ames and Langley Research Centers and from other outside studies. Currently defined ADS-B parameters related to navigation capability, trajectory type, and path conformance will be used to support the algorithms that generate the buffers.

Barhydt, Richard↗

DOE OSTI2023

Stochastic Look-Ahead Commitment: A Case Study in MISO

This paper introduces the Stochastic Look Ahead Commitment (SLAC) software prototyped and tested for the Midcontinent Independent System Operator (MISO) look ahead commitment process. SLAC can incorporate hundreds of wind, load, and net scheduled interchange (NSI) uncertainty scenarios. It uses a progressive hedging method to solve a novel two-stage stochastic unit commitment. The first stage commitment decisions, made only for those generators whose decision to commit or not in each time period cannot be deferred, can cover the uncertainties within the next three hours. The second stage includes both the dispatch for each of the scenarios and the commitment decisions that can be deferred. Study results on 15 MISO production days show that SLAC may bring economic and reliability benefits under uncertainty.

MATHEMATICS AND COMPUTING↗

NASA NTRS2018

Required Time of Arrival as a Control Mechanism to Mitigate Uncertainty in Arrival Traffic Demand Management

The objective of this study is to explore the use of Required Time of Arrival (RTA) capability on the flight deck as a control mechanism on arrival traffic management to improve traffic delivery accuracy by mitigating the effect of traffic demand uncertainty. The uncertainties are caused by various factors, such as departure error due to the difference between scheduled departure and the actual take-off time. A simulation study was conducted using the Multi Aircraft Control System (MACS) software, a comprehensive research platform developed in the Airspace Operations Laboratory (AOL) at NASA Ames Research Center. The Crossing Time (CT) performance (i.e. the difference between target crossing time and actual crossing time) of the RTA for uncertainty mitigation during cruise phase was evaluated under the influence of varying two main factors: wind severity (heavy wind vs. mild wind), and wind error (1 hour, 2 hours, and 5 hours wind forecast errors). To examine the CT performance improvement made by the RTA, the comparison to the CT of the aircraft that were not assigned with RTA (Non-RTA) under the influence of the selected factors was also made. The Newark Liberty International Airport (EWR) was chosen for this study. A total 66 inbound traffic to the EWR (34 of them were airborne when the simulation was initiated, 32 were pre-departures at that time) was simulated, where the pre-scripted departure error was assigned to each pre-departure (61 conform to their Expected Departure Clearance Time, which is +-300 seconds of their scheduled departure time). The results of the study show that the delivery accuracy improvement can be achieved by assigning RTA, regardless of the influence of the selected two factors (the wind severity and the wind information inaccuracy). Across all wind variances, 66.9 (265 out of 396) of the CT performance of the RTA assigned aircraft was within +- 60 seconds (i.e. target tolerance range) and 88.9 (352 out of 396) aircraft met +-300 seconds marginal tolerance range, while only 33.6 (133 out of 396) of the Non-RTA assigned aircrafts CT performance achieved the target tolerance range and 75.5 (299 out of 396) stayed within the marginal. Examination of the impact of different error sources i.e. departure error, wind severity, and wind error suggest that although large departure errors can significantly impact the CT performance, the impacts of wind severity and errors were modest relative the targeted +- 60 second conformance range.

required time of arrival (RTA)↗

DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids With Responsive Loads (Final Report)

The evolution of the power system to the reliable, efficient and sustainable system of the future will involve development of both demand- and supply-side technology and operations. Ambitious national and state-level goals around the decarbonization of electricity relies on the integration of very high levels of renewable resources, most of which are variable and intermittent. The use of demand response is an ideal approach to counterbalance the intermittency of renewable generation and brings the consumer into the spotlight. Though individual consumers are interconnected at the low-voltage distribution system, these resources are typically modeled as variables at the transmission network level. Demand-side participation cannot be leveraged effectively without explicitly including the distribution system dynamics in the optimization-based wholesale market operations. This project grew from a vision for co-optimized interaction of distribution systems, or microgrids, with the high-voltage transmission system. In this framework, microgrids encompass consumers, distributed renewables and storage. The energy management system of the lower voltage system (distribution or microgrid) can also sell (buy) excess (necessary) energy from the transmission system. Until recently, very little research had been conducted on the co-optimization of these two systems due to computational limitations. However, advances in computational capabilities, and the judicious use of decomposition methods and innovative approximation methods for high-dimension dynamic programming made this goal a viable objective for this project, leading to a fundamental shift in the ability to integrate and fully utilize demand-side resources. To this end, the modeling framework developed introduces a novel co-optimization framework, to include the operations of both the transmission and distribution systems (or microgrids) in operational decision making. This framework was used to analyze renewable and distributed generation along with responsive demand and to compare the capability of co-optimized systems to perform with higher levels of variable renewables. An ideal microgrid is defined as an electric entity capable of operating in both interconnected (with the high-voltage grid) and islanded mode. As such, the microgrid should incorporate generating units (traditional units and intermittent) and if needed, exchange power with the high-voltage grid. The interplay between the microgrid and high-voltage grid motivated the development of the co-optimization approach to ensure efficient performance of the interconnected network. Results show that the use of a bi-level optimization approach is an appropriate structure, capable of co-optimizing a transmission system with multiple distribution systems and microgrids. While increasing the number of connected systems provides increasing flexibility for renewables integration this can also the economic benefits to the low-voltage subsystems with each additional system connected. Comparison of a traditional single-level decision structure with the co-optimization approach illustrates a reduction in overall system cost under co-optimization, while specific cost allocations to transmission and distribution systems are changed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Management of Risk and Uncertainty Through Optimized Co-Operation of Transmission Systems and Microgrids with Responsive Loads (Final Report)

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS1997

Managing Technical and Cost Uncertainties During Product Development in a Simulation-Based Design Environment

An approach for objective and quantitative technical and cost risk analysis during product development, which is applicable from the earliest stages, is discussed. The approach is supported by a software tool called the Analytical System for Uncertainty and Risk Estimation (ASURE). Details of ASURE, the underlying concepts and its application history, are provided.

Karandikar, Harsh M.↗

NASA NTRS2005

Automated Planning and Scheduling for Space Mission Operations

Research Trends: a) Finite-capacity scheduling under more complex constraints and increased problem dimensionality (subcontracting, overtime, lot splitting, inventory, etc.) b) Integrated planning and scheduling. c) Mixed-initiative frameworks. d) Management of uncertainty (proactive and reactive). e) Autonomous agent architectures and distributed production management. e) Integration of machine learning capabilities. f) Wider scope of applications: 1) analysis of supplier/buyer protocols & tradeoffs; 2) integration of strategic & tactical decision-making; and 3) enterprise integration.

scheduling↗

NASA NTRS2011

Flight Departure Delay and Rerouting Under Uncertainty in En Route Convective Weather

Delays caused by uncertainty in weather forecasts can be reduced by improving traffic flow management decisions. This paper presents a methodology for traffic flow management under uncertainty in convective weather forecasts. An algorithm for assigning departure delays and reroutes to aircraft is presented. Departure delay and route assignment are executed at multiple stages, during which, updated weather forecasts and flight schedules are used. At each stage, weather forecasts up to a certain look-ahead time are treated as deterministic and flight scheduling is done to mitigate the impact of weather on four-dimensional flight trajectories. Uncertainty in weather forecasts during departure scheduling results in tactical airborne holding of flights. The amount of airborne holding depends on the accuracy of forecasts as well as the look-ahead time included in the departure scheduling. The weather forecast look-ahead time is varied systematically within the experiments performed in this paper to analyze its effect on flight delays. Based on the results, longer look-ahead times cause higher departure delays and additional flying time due to reroutes. However, the amount of airborne holding necessary to prevent weather incursions reduces when the forecast look-ahead times are higher. For the chosen day of traffic and weather, setting the look-ahead time to 90 minutes yields the lowest total delay cost.

Mukherjee, Avijit↗

DOE OSTI2024

Generalized Relationship Linking Water Balance and Vegetation Productivity across Site-to-Regional Scales

Evapotranspiration (ET) is a pivotal component in catchment-scale water balance and is essential for informed watershed management. Nevertheless, uncertainties in ET observation or modeling have been hindering effective water resources management. This study addresses this gap by establishing a robust, generalized linear relationship between ET and gross primary productivity (GPP) at the catchment scale. We test the linearity of the relationships between monthly GPP and ET data at 380 near-natural catchments across various climatic and landscape conditions in the contiguous U.S., yielding Pearson’s r ≥ 0.6 for 97% of the 380 catchments. We then develop a regionalization strategy to parameterize this GPP-ET relationship at the catchment scale by identifying and utilizing the linkages between the parameter values and extensively available hydroclimatic and landscape data. We demonstrate the efficacy of the proposed GPP-ET relationship and parameter regionalization strategy by their combined predictive capacity, where the predicted monthly GPP matches well with remote-sensing-based GPP product, achieving Kling-Gupta Efficient (KGE) values ≥ 0.5 for 92% of the catchments. In addition, we verify the relationship and its parameter regionalization at 35 AmeriFlux sites with KGE ≥ 0.5 for 25 sites, suggesting that the new relationship is transferable across the site, catchment, and regional scales. Furthermore, our findings are valuable for improving remote-sensing-based estimation of monthly ET and diagnosing coupled water–carbon simulations in land surface and Earth system models.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Uncertainty Analysis Considerations for Wind Tunnel Managers

Explore the source record for details and available documents.

Uncertainty↗

NASA NTRS2019

Parameterization-Induced Uncertainties and Impacts of Crop Management Harmonization in a Global Gridded Crop Model Ensemble

Global gridded crop models (GGCMs) combine agronomic or plant growth models with gridded spatial input data to estimate spatially explicit crop yields and agricultural externalities at the global scale. Differences in GGCM outputs arise from the use of different biophysical models, setups, and input data. GGCM ensembles are frequently employed to bracket uncertainties in impact studies without investigating the causes of divergence in outputs. This study explores differences in maize yield estimates from five GGCMs based on the public domain field-scale model Environmental Policy Integrated Climate (EPIC) that participate in the AgMIP Global Gridded Crop Model Intercomparison initiative. Albeit using the same crop model, the GGCMs differ in model version, input data, management assumptions, parameterization, and selection of subroutines affecting crop yield estimates via cultivar distributions, soil attributes, and hydrology among others. The analyses reveal inter-annual yield variability and absolute yield levels in the EPIC-based GGCMs to be highly sensitive to soil parameterization and crop management. All GGCMs show an intermediate performance in reproducing reported yields with a higher skill if a static soil profile is assumed or sufficient plant nutrients are supplied. An in-depth comparison of setup domains for two EPIC-based GGCMs shows that GGCM performance and plant stress responses depend substantially on soil parameters and soil process parameterization, i.e. hydrology and nutrient turnover, indicating that these often neglected domains deserve more scrutiny. For agricultural impact assessments, employing a GGCM ensemble with its widely varying assumptions in setups appears the best solution for coping with uncertainties from lack of comprehensive global data on crop management, cultivar distributions and coefficients for agro-environmental processes. However, the underlying assumptions require systematic specifications to cover representative agricultural systems and environmental conditions. Furthermore, the interlinkage of parameter sensitivity from various domains such as soil parameters, nutrient turnover coefficients, and cultivar specifications highlights that global sensitivity analyses and calibration need to be performed in an integrated manner to avoid bias resulting from disregarded core model domains. Finally, relating evaluations of the EPIC-based GGCMs to a wider ensemble based on individual core models shows that structural differences outweigh in general differences in configurations of GGCMs based on the same model, and that the ensemble mean gains higher skill from the inclusion of structurally different GGCMs. Although the members of the wider ensemble herein do not consider crop-soil-management interactions, their sensitivity to nutrient supply indicates that findings for the EPIC-based sub-ensemble will likely become relevant for other GGCMs with the progressing inclusion of such processes.

Folberth, Christian↗

DOE OSTI2022

How Can Probabilistic Solar Power Forecasts Be Used to Lower Costs and Improve Reliability in Power Spot Markets? A Review and Application to Flexiramp Requirements

Net load uncertainty in electricity spot markets is rapidly growing. There are five general approaches by which system operators and market participants can use probabilistic forecasts of wind, solar, and load to help manage this uncertainty. These include operator situation awareness, resource risk hedging, reserves procurement, definition of contingencies, and explicit stochastic optimization. We review these approaches, and then provide a case study in which a method for using probabilistic solar forecasts to define needs for reserves is developed and evaluated. The case study has three parts. First, we describe building blocks for enhancing the Watt-Sun solar forecasting system to produce probabilistic irradiance and power forecasts. Second, relationships between Watt-Sun forecasts for multiple sites in California and the system's need for flexible ramp capability (flexiramp) are defined by machine learning and statistical methods. Third, the performance of present methods to defining flexiramp requirements, which are not conditioned on weather and renewables forecasts, is compared with that of probabilistic solar forecast-based requirements, using a multi-timescale production costing model with an 1820-bus representation of the WECC power system. Significant potential savings in fuel and flexiramp procurement costs from using solar-informed reserve requirements are found.

14 SOLAR ENERGY↗

DOE OSTI2024

Wildfire management decisions outweigh mechanical treatment as the keystone to forest landscape adaptation

Modern land management faces unprecedented uncertainty regarding future climates, novel disturbance regimes, and unanticipated ecological feedbacks. Mitigating this uncertainty requires a cohesive landscape management strategy that utilizes multiple methods to optimize benefits while hedging risks amidst uncertain futures. We used a process-based landscape simulation model (LANDIS-II) to forecast forest management, growth, climate effects, and future wildfire dynamics, and we distilled results using a decision support tool allowing us to examine tradeoffs between alternative management strategies. We developed plausible future management scenarios based on factorial combinations of restoration-oriented thinning prescriptions, prescribed fire, and wildland fire use. Results were assessed continuously for a 100-year simulation period, which provided a unique assessment of tradeoffs and benefits among seven primary topics representing social, ecological, and economic aspects of resilience. Projected climatic changes had a substantial impact on modeled wildfire activity. In the Wildfire Only scenario (no treatments, but including active wildfire and climate change), we observed an upwards inflection point in area burned around mid-century (2060) that had detrimental impacts on total landscape carbon storage. While simulated mechanical treatments (~ 3% area per year) reduced the incidence of high-severity fire, it did not eliminate this inflection completely. Scenarios involving wildland fire use resulted in greater reductions in high-severity fire and a more linear trend in cumulative area burned. Mechanical treatments were beneficial for subtopics under the economic topic given their positive financial return on investment, while wildland fire use scenarios were better for ecological subtopics, primarily due to a greater reduction in high-severity fire. Benefits among the social subtopics were mixed, reflecting the inevitability of tradeoffs in landscapes that we rely on for diverse and countervailing ecosystem services. This study provides evidence that optimal future scenarios will involve a mix of active and passive management strategies, allowing different management tactics to coexist within and among ownerships classes. Our results also emphasize the importance of wildfire management decisions as central to building more robust and resilient future landscapes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Combining model-based and model-free methods for stochastic control of distributed energy resources

Modern distribution systems are experiencing a fast transformation with the growing penetration of distributed energy resources (DERs). Along with the economic and environmental benefits of DERs, challenges arise to address the uncertainties caused by their inherent volatility. If properly coordinated, however, DERs have the potential to provide the controllability that grid operators need. Here, we propose a hierarchical control framework that combines the model-based and model-free methods for stochastic DER control in distribution systems. The upper-level scheduler considers a chance-constrained optimal power flow problem (model-based) that schedules DER setpoints to minimize the operational cost and maintain the operating reserve. The lower-level distributed DER controllers absorb real-time disturbances and uncertainties using the extremum seeking control (model-free) to achieve grid objectives. The combination of model-based and model-free methods allows us to take the advantages of both methods to effectively manage the uncertainty in distribution systems. The proposed work is demonstrated on the IEEE 13-node feeder.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Human Health Risk Assessment for Improper Landfill Disposal of End-of-Life CdTe PV

The present work is a continuation of the 2020 IEA PVPS Task 12 Human Health Risk Assessment Methods for PV Part 3: Module Disposal Risks. The 2020 report performed a human health risk assessment (HHRA) for disposal of a cadmium telluride (CdTe) PV module in an unlined landfill, focusing solely on risks from cadmium. This study extends the 2020 HHRA on CdTe PV, analyzing eleven constituent elements: Cd, Se, Te, Cu, Si, Cr(III), Mo, Sn, Zn, Ni, and Al. The present HHRA was performed through two methods: utilization of the U.S. Environmental Protection Agency's (USEPA) Delisting Risk Assessment Software (DRAS V.4.0) on eight exposure pathways for cancer risk and non-cancer hazards; and comparison of exposure point concentrations to federal standards for groundwater, surface water, air, and soil exposure pathways. Cancer risks and non-cancer hazards posed by elemental leaching through all evaluated exposure pathways, using both methods, were found to be several orders of magnitude below USEPA health-protective thresholds. Cadmium exhibited both the highest risks and lowest uncertainty considering data availability on chemical content, leachate, and federal screening levels.

chemicals↗