SEARCH · Engineering Papers

Results for “Operating systems”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 325 records · Page 18

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Development of Smart Ventilation Control Algorithms for Humidity Control in High-Performance Homes in Humid U.S. Climates

Past field research and simulation studies have shown that high performance homes experience elevated indoor humidity levels for substantial portions of the year in humid climates. This is largely the result of lower sensible cooling loads, which reduces the moisture removed by the cooling system. These elevated humidity levels lead to concerns about occupant comfort, health and building durability. Use of mechanical ventilation at rates specified in ASHRAE Standard 62.2-2013 are often cited as an additional contributor to humidity problems in these homes. Past research has explored solutions, including supplemental dehumidification, cooling system operational enhancements and ventilation system design (e.g., ERV, supply, exhaust, etc.). This project’s goal is to develop and demonstrate (through simulations) smart ventilation strategies that can contribute to humidity control in high performance homes. These strategies must maintain IAQ via equivalence with ASHRAE Standard 62.2-2013. To be acceptable they must not result in excessive energy use. Smart controls will be compared with dehumidifier energy and moisture performance. This work explores the development and performance of smart algorithms for control of mechanical ventilation systems, with the objective of reducing high humidity in modern high performance residences. Simulations of DOE Zero-Energy Ready homes were performed using the REGCAP simulation tool. Control strategies were developed and tested using the Residential Integrated Ventilation (RIVEC) controller, which tracks pollutant exposure in real-time and controls ventilation to provide an equivalent exposure on an annual basis to homes meeting ASHRAE 62.2-2013. RIVEC is used to increase or decrease the real-time ventilation rate to reduce moisture transport into the home or increase moisture removal. This approach was implemented for no-, one- and two-sensor strategies, paired with a variety of control approaches in six humid climates (Miami, Orlando, Houston, Charleston, Memphis and Baltimore). The control options were compared to a baseline system that supplies outdoor air to a central forced air cooling (and heating) system (CFIS) that is often used in hot humid climates. Simulations were performed with CFIS ventilation systems operating on a 33% duty-cycle, consistent with 62.2-2013. The CFIS outside airflow rates were set to 0%, 50% and 100% of 62.2-2013 requirements to explore effects of ventilation rate on indoor high humidity. These simulations were performed with and without a dehumidifier in the model. Ten control algorithms were developed and tested. Analysis of outdoor humidity patterns facilitated smart control development. It was found that outdoor humidity varies most strongly seasonally—by month of the year—and that all locations follow the similar pattern of much higher humidity during summer. Daily and hourly variations in outdoor humidity were found to be progressively smaller than the monthly seasonal variation. Patterns in hourly humidity are driven by diurnal daily patterns, so they were predictable but small, and were unlikely to provide much control benefit. Variation in outdoor humidity between days was larger, but unpredictable, except by much more complex climate models. We determined that no-sensor strategies might be able to take advantage of seasonal patterns in humidity, but that real-time smart controls were required to capture variation between days. Sensor-based approaches are also required to respond dynamically to indoor conditions and variations not considered in our analysis. All smart controls face trade-offs between sensor accuracy, cost, complexity and robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Analysis Report for Exascale Storage Requirements for Scientific Data.

Over the next 10 years, the Department of Energy will be transitioning from Petascale to Exascale Computing resulting in data storage, networking, and infrastructure requirements to increase by three orders of magnitude. The technologies and best practices used today are the result of a relatively slow evolution of ancestral technologies developed in the 1950s and 1960s. These include magnetic tape, magnetic disk, networking, databases, file systems, and operating systems. These technologies will continue to evolve over the next 10 to 15 years on a reasonably predictable path. Experience with the challenges involved in transitioning these fundamental technologies from Terascale to Petascale computing systems has raised questions about how these will scale another 3 or 4 orders of magnitude to meet the requirements imposed by Exascale computing systems. This report is focused on the most concerning scaling issues with data storage systems as they relate to High Performance Computing- and presents options for a path forward. Given the ability to store exponentially increasing amounts of data, far more advanced concepts and use of metadata will be critical to managing data in Exascale computing systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

DSO+T: Transactive Energy Coordination Framework (DSO+T Study: Volume 3)

This report describes a transactive energy coordination scheme designed to integrate into existing day-ahead and real-time wholesale energy markets. This scheme was evaluated in the Distribution System Operator with Transactive (DSO+T) study to assess the engineering and economic performance of the transactive energy coordination of a large-scale deployment of distributed energy resources (DER). Transactive agents were developed for a range of DERs (heating, ventilation, and air conditioning units, water heaters, batteries, and electric vehicles) that optimize flexibility over a 48-hour horizon and adjust their strategy in response to changes in real-time prices. A transactive energy coordination scheme, executed by a DSO retail market operator, aggregates these DER bids from participating customers and clears them against a DSO supply curve using a double auction market mechanism. The process of constructing the price-quantity DSO supply curve includes distribution-level transportation constraints (for example, substation congestion limits) and forecast locational marginal price of the DSO’s connected transmission node. The resulting day-ahead and real-time quantities are then bid into a competitive wholesale market operated by an independent system operator. This report also details additional capabilities for proper marketplace simulation such as wholesale price, weather, and load forecasting. The report concludes with a discussion of lessons learned and key design features required to ensure successful operation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Evaluation of Use Cases and Types of Databases for Hosting Wind Power Data

The goal of the National Wind Power Production Data Dashboard project is to develop a publicly available platform to model, process, and share wind power with uncertainty quantification for the current and future onshore and offshore wind plants across the continental United States. As of 2021, more than 70,000 utility-scale wind turbines have been installed across the United States and this number is expected to grow in the next few years. A large-scale wind power production database is needed for stakeholders (policy makers, operators, and researchers) to access easily for their decision-making. Currently, meeting that need is a challenge. Most wind power operators and system operators focus on regions of their own interest, and the data are usually inaccessible to the public.

17 WIND ENERGY↗

DOE OSTI2024

Planning and Operations in Electricity Markets Under System Tansformation: Key Findings

This report summarizes a set of key findings that have been developed through a set of interconnected research activities performed by five institutions between January 2020 and December 2023. The project team, comprising Argonne National Laboratory, the National Renewable Energy Laboratory, Lawrence Berkeley National Laboratory, the Electric Power Research Institute, and Johns Hopkins University, collective engaged with the North American Independent System Operators and Regional Transmission Operators (ISO/RTOs) to identify the key challenges they are facing and opportunities for the project team to provide technical assistance in several prioritized challenge areas.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

A Review on Protection Challenges on Transmission Lines Connected to Inverter Based Resources

The increased penetration of inverter-based resources (IBRs) connected to transmission grids is challenging how traditional electric power systems operate. Such systems were originally designed based on the dominant presence of synchronous generators. Some main challenges include the increased concerns about system stability due to the reduced system inertia caused by IBRs and the reduced protection reliability caused by the non-deterministic fault current response of IBRs. The protection relays’ algorithms have been designed and validated based on well-known fault current characteristics of synchronous generations, while the short-circuit responses of IBRs heavily depend on their vendor-specific control schemes, which are mainly set to protect inverter semiconductor switches and DC components. The magnitude of short current that IBRs contribute is typically between 1.1–1.5 times the rated current that may behave unstably and incoherently with the voltages. The control scheme of the IBRs also controls the active (P) and reactive (Q) support during system voltage sag and swell conditions. These characteristics challenge not only protective relay schemes but also existing short-circuit software tools to correctly model IBRs for fault studies and protection coordination analyses. Utilities, university researchers, research institutes, and relay manufacturers have performed studies to investigate different aspects of high IBR penetration. Almost all studies support 1) creating international and national standards to regulate the IBRs, 2) revising engineering tools to address shortcomings, and 3) adjusting the protective relay settings to resolve protection problems. This paper summarizes the studies and research conducted in recent years to quantify the impact of IBR penetration on transmission line protection and to identify potential solutions to improve the dependability and reliability of grid protection systems.

14 SOLAR ENERGY↗

DOE OSTI2024

A review on protection challenges of Transmission Lines connected to Inverter Based Resources

14 SOLAR ENERGY↗

DOE OSTI2019

Analysis of Wind Ramping Product Formulations in a Ramp-Constrained Power Grid

Flexible ramping products are designed to compensate the variability and uncertainty of load and intermittent generation. Since their market implementation by the California Independent System Operator (CAISO) and Midcontinent System Operator (MISO), flexible ramping products have garnered much attention. However, it is still unclear how to best formulate wind power plants’ participation in the ramping requirement. This paper investigates different wind ramping product formulations and increasing wind power penetration in the context of a security-constrained unit commitment (SCUC) model. We demonstrate that the ramping model that captures both the intra- and inter-temporal output ramp capability of individual wind power plants reflects the true ramp contribution of the wind fleet. With increasing wind penetration, wind generation curtailments can support the grid’s ramping needs. In addition, we found that increased wind penetration has the potential of lowering ramping and production costs. Numerical case studies performed on the TAMU 2000-bus synthetic network support the findings.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

DOE OSTI2017

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operator can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Probabilistic Forecasting of Generators Startups and Shutdowns in the MISO System Based on Random Forest

Solving security constrained unit commitment (SCUC) problems to plan an economical generation schedule for day-head electricity market has been an important research topic in recent years. Mixed integer programming method (MIP), the-state-of-art approach for solving SCUC problem, is known computationally hard when the number of binary status variables is large. In this paper, a machine learning-based algorithm - random forest (RF), was applied to forecast the startups (SU) and shutdowns (SD) hours of generators, based on historical hourly system condition observations in the Midcontinent Independent System Operator (MISO) system. The main purpose is to reduce the number of binary status variables, by fixing the SU/SD hours to a narrow range of high confidence. This would significantly reduce the size of the decision space, and therefore speed up SCUC solutions with reduced uncertainty.

Lin, Xinming↗

DOE OSTI2022

Masked fault detection for reliable low voltage cache operation

Systems, apparatuses, and methods for implementing masked fault detection for reliable low voltage cache operation are disclosed. A processor includes a cache that can operate at a relatively low voltage level to conserve power. However, at low voltage levels, the cache is more likely to suffer from bit errors. To mitigate the bit errors occurring in cache lines at low voltage levels, the cache employs a strategy to uncover masked faults during runtime accesses to data by actual software applications. For example, on the first read of a given cache line, the data of the given cache line is inverted and written back to the same data array entry. Also, the error correction bits are regenerated for the inverted data. On a second read of the given cache line, if the fault population of the given cache line changes, then the given cache line's error protection level is updated.

Ganapathy, Shrikanth↗

DOE OSTI2024

A Review on Protection Challenges of Transmission Lines Connected to Inverter-Based Resources

14 SOLAR ENERGY↗

DOE OSTI2015

Dynamic Operations Wayfinding System (DOWS) for Nuclear Power Plants

A novel software tool is proposed to aid reactor operators in respond- ing to upset plant conditions. The purpose of the Dynamic Operations Wayfind- ing System (DOWS) is to diagnose faults, prioritize those faults, identify paths to resolve those faults, and deconflict the optimal path for the operator to fol- low. The objective of DOWS is to take the guesswork out of the best way to combine procedures to resolve compound faults, mitigate low threshold events, or respond to severe accidents. DOWS represents a uniquely flexible and dy- namic computer-based procedure system for operators.

computer-based procedure↗

DOE OSTI2021

STOCHASTIC OPTIMAL POWER FLOW FOR REAL-TIME MANAGEMENT OF DISTRIBUTED RENEWABLE GENERATION AND DEMAND RESPONSE (Final Report)

To meet the grand challenge of a sustainable energy future, there has been a surge of interest in renewable energy. Today, the uncertainty associated with renewable resources is handled by using operating reserves. The high penetration of renewable resources, however, introduces difficult-to-control dynamics and challenges for power system operation. Decision support tools are necessary at the bulk system operational level to recognize and efficiently utilize renewable resources and distributed demand response products in concert with traditional grid resources. It is envisaged that responsive load can potentially have very significant cost advantages over either spinning or non-spinning ramping reserve. Critical decisions are made during hour(s)-ahead and real-time power system operation regarding the commitment and dispatch of generators to ensure power delivery is both reliable and economic. These decisions are typically made by a security constrained optimal flow, which determines future generator commitments, dispatches, and ensures adequate reserves are available in the event of a contingency (unexpected outage) or if future system conditions deviate from forecasts. However, security has been always based on a pre-specified subset of contingency constraints whose enforcement does not guarantee security under all possible future possibilities while also giving little or no weight to the likelihood of each contingent event or the severity of its consequences. Existing tools, which are based exclusively on deterministic optimization models, do not yield optimal operational decisions to address these new challenges, in terms of both reliability and cost-effectiveness. This project has focused on developing a stochastic optimal power flow (SOPF) framework, which integrates renewable resource uncertainty, load uncertainty, distributed storage (DS), demand response (DR) products, in a holistic manner to address the uncertainty associated with ever-increasing renewable resources, along with the inclusion of distributed demand response products in future power systems. A proof-of-concept problem was created using the Pennsylvania-Jersey-Maryland (PJM) power system network. Synthetic wind generation was added to the system to simulate 50% wind penetration. A 1-hour test of SOPF operation indicated more than 6% operational cost savings. The project continued by adding the Midwestern Independent System Operator (MISO) as a partner, with focus shifting from SOPF to Stochastic Look-Ahead Unit Commitment (SLAC). Unlike PJM, MISO is faced with significant renewable energy resources within its footprint and is challenged with substantial uncertainty in its operations. The SLAC distinguishes itself from existing tools that operators use. At best, today’s tools solve two to three cases independently, where one or two system parameters, such as forecasted load level (e.g., a low, base, and high forecast), are varied and the resulting scenarios are analyzed independently. The stochastic-based optimization of SLAC leverages statistical information from an ensemble of potential operational scenarios and their respective likelihood. The SLAC output can be translated into valuable information to the operator such as suggested commitments, optimal scheduling and dispatch of resources, reserve requirements at both locational and zonal resolutions, ramping availability and requirements, availability of demand response including operational guidance concerning the near-term and real-time coordination between distributed energy resources, and utilization of distributed storage resources. The developed SOPF/SLAC tool, a stand-alone tool compatible with existing EMSs, will provide system operators with unprecedented visibility, flexibility and predictability to these resources and operational guidance concerning the real-time coordination between DERs and DR/DS products. The game changing and practical impact of this disruptive technology will be dramatic and will usher in a new era in the electric power industry, wherein green energy concepts are fully embraced, and electric power costs are lowered throughout the nation.

42 ENGINEERING↗

DOE OSTI2018

Potential of Wind Power to Provide Flexible Ramping Products and Operating Reserve: Preprint

With increasing penetration levels of variable renewable energy sources, such as wind and solar, electric power systems need flexible resources that can provide fast-response ramping capabilities to counter the variability and uncertainty of renewable generation. Although the California Independent System Operator and Midcontinent System Operator have lauched ramp products, determining how wind power can participate using their flexible ramping models is still largely unclear. This paper proposes a security-constrained unit commitment (SCUC) model considering a flexible ramping constraint representing the system's flexible ramping requirements that account for the net load probabilistic forecast uncertainty. Through this explicit modeling of the uncertainty, the true cost of the flexible ramp service can be determined after solving the SCUC problem. In addition, the potential of wind power to provide operating reserves is analyzed. Numerical case studies demonstrate that with wind power providing both flexible ramping products and operating reserve, the system operating costs and ramp shortages will be reduced significantly.

17 WIND ENERGY↗

DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗

DOE OSTI2015

Review and Status of Wind Integration and Transmission in the United States. Key Issues and Lessons Learned

The objective in electric power system operation is to use generation and transmission resources within organizational constraints and operational rules and regulations to reliably and costeffectively balance load and generation. To meet this objective, system operational practices have been created to accommodate the innate variability and uncertainty that comes from a variety of sources, such as uncertainty of demand forecasts, whether a specific generating unit will be available when called upon, the variability of demand from many different types of customers, and others. As more wind power is connected to the power system, operating experiences acquired during the past several years have generally confirmed the findings of wind integration studies: wind energy increases the level of variability and uncertainty that a system operator must manage.

17 WIND ENERGY↗

DOE OSTI2015

Power System Protection and Resilient Metrics

During a real-time power system event, a system operator need to conservatively reduce the operating limits while changing system conditions are analyzed. The time it takes to develop new operating limits could affect millions of transmission system users especially if this event is classified by NERC as a Category D type event (extreme events resulting in the loss of two or more bulk electric system elements) . Controls for the future grid must be able to perform real-time analysis, identify new reliability risks, and set new SOLs (System Operating Limit) for real-time operations. In this paper we are inducing “Resilience Metrics” requirements that describe how systems operate at an acceptable level of normalcy despite disturbances or threats. We consider the interdependencies inherent in critical infrastructure systems and discuss some resilience metrics that can be distributed in current supervisory control and data acquisition (SCADA) to provide a level of state awareness. This level of awareness provides knowledge that can be used to characterize and reduce the risk of cascading events. A “resilience power system agent” is proposed that provides attributes to measure and perform this metrics.

42 ENGINEERING↗