Hamann, Hendrik F. | Engineering Papers

DOE OSTI2024

Foundation Models for the Electric Power Grid

Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex systems and dynamics can be applied to many downstream applications. Therefore, advances in FMs can find uses in electric power grids, challenged by the energy transition and climate change. This paper calls for the development of FMs for electric grids. We highlight their strengths and weaknesses amidst the challenges of a changing grid. It is argued that FMs learning from diverse grid data and topologies, which we call grid foundation models (GridFMs), could unlock transformative capabilities, pioneering a new approach to leveraging AI to redefine how we manage complexity and uncertainty in the electric grid. Finally, we discuss a practical implementation pathway and road map of a GridFM-v0, a first GridFM for power flow applications based on graph neural networks, and explore how various downstream use cases will benefit from this model and future GridFMs.

AI-based power flow simulation↗

DOE OSTI2023

Finding a contaminant source in a volume of flowing fluid

A method includes: mapping a boundary of a volume of flowing fluid; partitioning the volume by a computational mesh; finding a contaminant location at a first sensor that is disposed within the volume; obtaining a measured velocity of the flowing fluid within the volume; generating a reversed velocity vector field within the mesh, in response to the measured velocity; time stepping the contaminant location from the first sensor along the reversed velocity vector field until the contaminant location intersects the boundary of the volume; and finding a contaminant source at the intersection of the time stepped contaminant location with the boundary of the volume.

Lopez-Marrero, Vanessa↗

DOE OSTI2023

Server rack for improved data center management

Methods and systems for data center management include collecting sensor data from one or more sensors in a rack; determining a location and identifying information for each asset in the rack using a set of asset tags associated with respective assets; communicating the sensor and asset location to a communication module; receiving an instruction from the communication module; and executing the received instruction to change a property of the rack.

Bermudez Rodriguez, Sergio↗

DOE OSTI2022

Server rack for improved data center management

Methods and systems for data center management include collecting sensor data from one or more sensors in a rack; determining a location and identifying information for each asset in the rack using a set of asset tags associated with respective assets; communicating the sensor and asset location to a communication module; receiving an instruction from the communication module; and executing the received instruction to change a property of the rack.

Bermudez Rodriguez, Sergio A.↗

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 OSTI2022

Using probabilistic solar power forecasts to inform flexible ramp product procurement for the California ISO

How can independent system operators (ISOs) take advantage of probabilistic solar forecasts to lower generation costs and improve reliability of power systems? We discuss one three-step approach for doing so, focusing on how such forecasts might help the California Independent System Operator (CAISO) prepare unexpected net load ramps, where net load equals gross demand minus wind and solar production. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty reflected in the forecasted prediction intervals (independent variables) to error distributions for net load ramp forecasts for the CAISO real-time market (dependent variable) using machine learning and quantile regression. Projected ramp forecast errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Detailed descriptions are provided on the quantile regression and kth-nearest neighbor categorization methods for accomplishing that translation. Finally, a multiple time-scale look-ahead market simulation model is applied to a 118-bus IEEE Reliability Test System, modified to represent the CAISO generation mix and demand distributions. The model runs quantify how solar-conditioned ramp requirements can, first, decrease operating costs by reducing requirements compared to often conservative unconditional methods and, second, decrease generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. Solar-conditioned ramp requirements are found to reduce generation operating costs by about 2% for the test system (which would be equivalent to over $\$100$ million per year for a CAISO-size system).

14 SOLAR ENERGY↗

DOE OSTI2022

Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

How can probabilistic solar forecasts lower costs and improve reliability for independent system operator (ISO) markets? We tackle this question in three steps. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty in those forecasts to error distributions for net load ramps for the California ISO (CAISO) using statistical and machine learning methods. Projected net load errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Finally, a multi-period look-ahead production cost model quantifies how conditional ramp requirements can a) decrease operating costs by lowering requirements compared to often conservative unconditional methods, and b) reduce generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. In addition to the products just described (quantification of solar uncertainty, its translation into requirements for ramp capability product, and quantification of the benefits of more accurate ramp requirements), this project also developed a visualization system that alerts system operators of ramp and uncertainty conditions within the network based on solar forecasts. The system is called Resource Forecast and Ramp Visualization for Situational Awareness (RaVIS). These four products represent significant advances in the state-of-the-art of probabilistic solar forecasting, development of weather-informed reserve requirements, production costing methods for estimating the benefits of more accurate reserve requirements, and visualization of system status, respectively. Yet the products are also practical and can be immediately implemented, potentially enabling system operators to save millions of dollars in ramp product procurement costs per year.

14 SOLAR ENERGY↗

DOE OSTI2022

Solar forecasting using machine learned cloudiness classification

Methods and systems for predicting irradiance include learning a classification model using unsupervised learning based on historical irradiance data. The classification model is updated using supervised learning based on an association between known cloudiness states and historical weather data. A cloudiness state is predicted based on forecasted weather data. An irradiance is predicted using a regression model associated with the cloudiness state.

Hamann, Hendrik F.↗

DOE OSTI2020

Heuristic based analytics for gas leak source identification

Heuristic-based techniques for gas leak source identification are provided. In one aspect, a method for identifying a location of a gas leak source includes: obtaining gas sensor data and wind data synchronously from a gas leak detection system having a network of interconnected motes comprising gas sensors and wind sensors, with the gas sensors arranged around possible gas leak sources in a given area of interest; identifying the location of the gas leak source using the gas sensor data and wind data; and determining a magnitude of gas leak from the gas leak source using the location of the gas leak source and a distance d between the location of the gas leak source and a select one of the gas sensors from which the gas sensor data was obtained. A gas leak detection system is also provided.

03 NATURAL GAS↗

DOE OSTI2020

Filter replacement lifetime prediction

A system for increasing filter economy includes a memory configured to receive measurements of contaminants in an internal and an external environment. A cost effectiveness module is configured to determine a cost of a corrosion rate increase if unfiltered external air intake is increased for cooling, to determine a cost of increased air pressure to filter external air, and to determine if the cost of filtering external air exceeds the cost of the corrosion rate increase. An air intake module is configured to increase an intake of unfiltered external air if it is determined that the cost of filtering external air exceeds the cost of the corrosion rate increase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

Filter replacement lifetime prediction

Systems for predicting a filter lifetime. A filter monitoring system includes a filter effectiveness history based on contaminant sensor information associated with a filter received from one or more contaminant sensors. A lifetime module is configured to determine a rate of filter consumption based on the filter effectiveness history and to determine a remaining filter lifetime based on the determined rate of filter consumption.

42 ENGINEERING↗

DOE OSTI2020

Parameter-dependent model-blending with multi-expert based machine learning and proxy sites

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Parameter-dependent model-blending with multi-expert based machine learning and proxy sites

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Parameter-dependent model-blending with multi-expert based machine learning and proxy sites

A parameter-based multi-model blending method and system are described. The method includes selecting a parameter of interest among parameters estimated by each of a set of individual models, running the set of individual models with a range of inputs to obtain a range of estimates of the parameters from each of the set of individual models, and identifying, for each of the set of individual models, critical parameters among the parameters estimated, the critical parameters exhibiting a specified correlation with an error in estimation of the parameter of interest. For each subspace of combinations of the critical parameters, obtaining a parameter-based blended model is based on blending the set of individual models in accordance with the subspace of the critical parameters, the subspace defining a sub-range for each of the critical parameters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Next-generation geospatial-temporal information technologies for disaster management

Traditional geographic information systems (GIS) have been disrupted by the emergence of Big Data in the form of geo-coded raster, vector, and time-series Internet-of-Things data. This article discusses the application of new scalable technologies that go far beyond relational databases and file-based storage on spinning disk or tape to incorporate both storage and processing data in the same platform. The roles of the Apache Hadoop Distributed File Systems and NoSQL key-value stores such as the Apache Hbase are discussed, along with indexing schemes that optimally support geospatial-temporal use. Here, we highlight how this new approach can rapidly search multiple GIS data layers to obtain insights in the context of early warning, impact evaluation, response, and recovery to earthquake and wildfire disasters.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Techniques for evaluating optimum data center operation

Techniques for modeling a data center are provided. In one aspect, a method for determining data center efficiency is provided. The method includes the following steps. Target parameters for the data center are obtained. Technology pre-requisite parameters for the data center are obtained. An optimum data center efficiency is determined given the target parameters for the data center and the technology pre-requisite parameters for the data center.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2018

Unsupervised Clustering-Based Short-Term Solar Forecasting

Solar forecasting accuracy is highly affected by weather conditions, therefore, weather awareness forecasting models are expected to improve the forecasting performance. However, it may not be available or reliable to classify different forecasting tasks by only using predefined meteorological weather categorization. In this paper, an unsupervised clustering-based (UC-based) solar forecasting method is developed for short-term (1-h-ahead) global horizontal irradiance (GHI) forecasting. This UC-based method consists of three parts: GHI time series unsupervised clustering, pattern recognition, and UC-based forecasting. The daily GHI time series is first clustered by an Optimized Cross-validated ClUsteRing (OCCUR) method, which determines the optimal number of clusters and best clustering results. Then, support vector machine pattern recognition is adopted to recognize the category of a certain day using the first four hours' data in the forecasting stage. GHI forecasts are generated by the most suitable models in different clusters, which are built by a two-layer machine learning based multi-model (M3) forecasting framework. The developed UC-M3 method is validated by using 1-year of data with 13 solar features from three information sources. Numerical results show that 1) UC-based models outperform non-UC (all-in-one) models with the same M3 architecture by approximately 20%; and 2) M3-based models also outperform the single-algorithm machine learning models by approximately 20%.

14 SOLAR ENERGY↗

DOE OSTI2018

A Methodology for Quantifying Reliability Benefits From Improved Solar Power Forecasting in Multi-Timescale Power System Operations

Solar power forecasting improvements are mainly evaluated by statistical and economic metrics, and the practical reliability benefits of these forecasting enhancements have not yet been well quantified. This paper aims to quantify reliability benefits from solar power forecasting improvements. To systematically analyze the relationship between solar power forecasting improvements and reliability performance in power system operations, an expected synthetic reliability (ESR) metric is proposed to integrate state-of-the-art independent reliability metrics. The absolute value and standard deviation of area control errors (ACE), and the North American Electric Reliability Corporation Control Performance Standard 2 (CPS2) score are calculated through the multi-timescale scheduling simulation, including the day-ahead unit commitment (DU), real-time unit commitment (RU), real-time economic dispatch (RE), automatic generation control (AGC) sub-models. The absolute area control error in energy (AACEE), CPS2 violations, CPS2 score, and standard deviation of the raw ACE are all calculated and combined as the ESR metric. Numerical simulations show that the reliability benefits of multi-timescale power system operations are significantly increased due to the improved solar power forecasts.

24 POWER TRANSMISSION AND DISTRIBUTION↗