Digital Twins for Canal, Hydrokinetic Turbine and Array Power Modeling & Optimization
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13 HYDRO ENERGY↗
SEARCH · Engineering Papers
Results for “power optimizer”
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.
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Maximized Information Gain of Next Generation Pulsed Power Using Optimized Design of Z-Machine Experiments
This project develops a Bayesian optimization approach to extracting insights from Z Machine experimental data to determine if and how these insights can be used to extrapolate to a larger facility. The primary goal is to address the scientific challenge of informing how confidently experimental conditions can be predicted on a next generation facility, the design of which requires the reliable extrapolation of current high energy density technologies to regimes yet unobserved, except by costly high-fidelity computational models. Maximizing the use of presently available data and understanding how it informs future endeavors is critically important to enable transformative pulsed power and the science of extreme conditions. We explore a Bayesian optimization approach to experimental design which combines information theory, experimental data, and computational modeling to explore how information gain can be maximized.
97 MATHEMATICS AND COMPUTING↗
Leveraging Quantum Computing for Accelerated Classical Algorithms in Power Systems Optimization
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24 POWER TRANSMISSION AND DISTRIBUTION↗
Technology Solutions Case Study: Photovoltaic Systems with Module-Level Power Electronics
This guide will show how DC power optimizers and microinverters (both known as module-level power electronics) are being used in new and/or retrofit, single and multifamily homes.
DC power optimizer↗
Building America Case Study: Photovoltaic Systems with Module-Level Power Electronics
Direct current (DC) power optimizers and microinverters (together known as module-level power electronics, or MLPE) are one of the fastest growing market segments in the solar industry. According to GTM Research in The Global PV Inverter Landscape 2015, over 55% of all residential photovoltaic (PV) installations in the United States used some form of MLPE in 2014.
14 SOLAR ENERGY↗
INTEGRATION OF COAL-FIRED POWER PLANT OPTIMIZATION TOOLS WITH THE IDAES PLATFORM
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01 COAL, LIGNITE, AND PEAT↗
nnopf
SAND2026-19433O The nnopf tool addresses the optimal power flow (OPF) problem by optimizing electricity delivery from generating plants to consumers. At the same time, it minimizes costs and adheres to power grid constraints. While traditional methods for solving OPF can be computationally intensive, the tool mitigates this challenge by training neural networks on power grids to predict optimal solutions for the OPF problem. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.
Hart, William [Sandia National Lab. (SNL-CA), Live↗
Grid Optimization with Solar (GO-Solar) Experiences With: Data-Driven and Machine Learning Approaches for High-Pen PV Grids
Provides an overview of the innovations and challenges from the algorithm development portion of the GO-Solar project, including plain language introductions to matrix-completion based state estimation, multi-kernel learning based state forecasting, 'slow-scale' voltage-load sensitivity matrix linearized optimal power flow, and 'fast-scale' OPF plan following using on-line Provides an overview of the innovations and challenges from the algorithm development portion of the GO-Solar project, including plain language introductions to matrix-completion based state estimation, multi-kernel learning based state forecasting, 'slow-scale' voltage-load sensitivity matrix linearized optimal power flow, and 'fast-scale' OPF plan following using on-line multi-objective state.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Performance Modeling and Testing of Distributed Electronics in PV Systems; NREL (National Renewable Energy Laboratory)
Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order to give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses with and without distributed power electronics, along with experimental validation results. Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order to give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses.
14 SOLAR ENERGY↗
Faster-than-real-time Simulation with Demonstration for Resilient DER Integration
The US electric grid is facing operational, stability, and security challenges. Transmission system operators need some measure of visibility into distribution system renewable generation. Distribution system generation needs to support transmission system voltage. The grid is experiencing an expansion in measurement systems. How to take full advantage of this expansion and defend against attacks, both cyber and physical, poses additional challenges. The Faster-than-real-time Simulation with demonstration for Resilient DER Integration project set out to do the following: a. Flatten the voltage profile through the feeders and system for cost saving and voltage stabilization needs. b. Increase the amount of intermittent distributed energy resources (IDERs) that could be deployed on a utility feeder and provide 100% or more energy needed for the demands on that feeder, and c. based on an accurate model (Digital Twin) of the utilities system, be able to detect any abnormalities on the utilities distribution system. To manage the voltage and increase IDER penetration (a,b), Graph Trace Analysis is employed in a time-series, optimal power flow to coordinate the time-varying feedback control setpoints of a distribution feeder’s utility control devices. Under the coordinated control are a Load Tap Changing Transformer, a voltage regulator, and five switched capacitor banks. The feeder serves over 2000 customers, the feeder secondaries are modeled, and the feeder has 2.3 MW of PV generation, corresponding to a 17.4% penetration of PV generation. The feeder model has over 12,000 components, where every customer load bus and PV generator are modeled. The accuracy of the power flow solution is compared against historical meter voltage measurements, the improvement in conservation voltage reduction energy savings as a function of the coordinated control desired voltage profile is investigated, and the increase in PV penetration of the coordinated control over the existing control is presented. To achieve improved control performance while observing system operation constraints, bellwether Advanced Metering Infrastructure (AMI) voltage measurements are used to adjust the desired voltage profile used by the optimal power flow analysis. To detect and alleviate or negate attacks or failures on the distribution and transmission utility grids (c) the grid needs to be resilient and self-healing. In this project software was designed to do just that. At the center of the software is an Integrated System Model (ISM) that spans from transmission to secondary distribution. The ISM is employed in real-time abnormality detection, voltage stability forecasting, and multi-mode control. Testing results are presented for: 1—attacks on utility infrastructure; 2—energy savings from optimal control; 3—distribution system control response during a low voltage transmission system event; 4—cyber-attacks on PV inverters, where physical inverters are used in hard-ware-in-the-simulation-loop studies. Contributions of this work include real-time analysis that spans from three-phase transmission through secondary distribution; an approach for detecting abnormalities that employs measurements from three independent measurement systems; and a multi-mode distribution system control that responds to cyber-attacks, physical attacks, equipment failures, and transmission system needs.
Integrated System Model, Graph Trace Analysis, Adv↗
pnnl/oedisi_dopf
OpenEDI - System Integration (OEDI-SI) - PNNL Distributed Optimal Power Flow (DOPF)
Slay, Tylor↗
On Anomaly Detection for Transactive Energy Systems with Competitive Market
Two anomaly-detection criteria are proposed for transactive energy systems with competitive markets. Participants of transactive energy systems seek an optimal power allocation through hybrid economic-control methods to facilitate the integration of various types of distributed energy resources to power distribution systems. In transactive energy systems, every participant is assumed to be a rational entity, and consumers have diminishing marginal utility and suppliers have increasing marginal cost. With the first proposed anomaly-detection criterion, the monotonicity of marginal cost and marginal utility are examined. The impact of line flow constraints is also taken into consideration. Then, the second anomaly-detection criterion is proposed for TESs with marginal cost and marginal utility which change faster than a certain rate. The second criterion is more accurate than the first one for TES with marginal cost and marginal utility which change faster than a certain rate, but it requires the knowledge of that rate. Neither criteria requires more data than those necessary to find the optimal power allocation and the market-clear price in a transactive energy system. Therefore, the proposed criteria do not disclose any more data than necessary. As the monotonicity of marginal cost and marginal utility in a TES with competitive markets results in convex objective functions in an optimization problem and strongly convex ones when marginal cost and marginal utility changes faster than a certain rate, the two detection criteria are also applicable to anomaly detection of general convex optimization problems. Simulations are carried out to show the efficacy of the proposed criteria to detect anomalies caused by cyberattacks.
Wang, Peng↗
Heliostat Field Optimization for Power Tower Solar Industrial Process Heat Applications
This presentation reviews how to develop techno-economic models to support the assessment and development of new heliostat concepts. As well as quantify tradeoffs and interactions of heliostat design, manufacturing, and operation to illustrate R&D benefits on a "total system" level.
HelioCon↗
Conservation Voltage Reduction (CVR) via Two-Timescale Control in Unbalanced Power Distribution Systems
Voltage control devices are employed in power distribution systems to reduce the power consumption by operating the system closer to the lower acceptable voltage limits; this technique is called conservation voltage reduction (CVR). The different modes of operation for system’s legacy devices (with binary control) and new devices (e.g. smart inverters with continuous control) coupled with variable photovoltaic (PV) generation results in voltage fluctuations which makes it challenging to achieve CVR objective. This paper presents a two-timescale control of feeder’s voltage control devices to achieve CVR that includes (1)a centralized controller operating in a slower time-scale to coordinate voltage control devices across the feeder and (2) local controllers operating in a faster timescale to mitigate voltage fluctuations due to PV variability. The centralized controller utilizes a three-phase optimal power flow model to obtain the decision variables for both legacy devices and smart inverters. The local controllers operate smart inverters to minimize voltage fluctuations and restore nodal voltages to their reference values by adjusting the reactive power support. The proposed approach is validated using the IEEE-123 bus (medium-size) and R3-12.47-2 (large-size) feeders. It is demonstrated that the proposed approach is effective in achieving the CVR objective for unbalanced distribution systems.
optimal control, distributed generation, fluctuati↗
Personal Fuel Economy in Conventional and Hybrid Electric Vehicles: Integrating Driving Cycle Predictions with Power Distribution Optimization
Improving fuel efficiency and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel efficiency in laboratory controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This study uses standard driving cycles as a base for comparison, while aiming to develop fuel efficiency estimates based on personalized driving cycles that can inform consumers’ vehicle purchase and use decisions. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: aggressive, normal, or calm. Then the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate personal fuel efficiency for conventional vehicles and HEVs. To optimize personal fuel efficiency estimates, the Equivalent Consumption Minimization Strategy (ECMS) is applied. Depending on the driving style (aggressive, normal, or calm) and the driving scenario (city or highway driving), conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel efficiency was 13% lower in calm city driving, but almost 34% higher for aggressive highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on application of case-based reasoning and used to tune the power distribution in HEVs, their fuel consumption can improve by up to 12% in city driving. Implications and limitations of the findings are discussed in the paper.
Liu, Jun↗
OPFLearn.jl [SWR-21-109]
OPFLearn.jl is a Julia package for creating datasets for machine learning approaches to solving AC optimal power flow (AC OPF). It was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. Over time this input space tightens around the relaxed AC OPF feasible region to increase the percentage of feasible load profiles found while uniformly sampling the input space. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets". To use OPFLearn.jl a PowerModels network data dictionary is required (can be loaded from Matpower ".m" files) to define the network the dataset is being created for.
Joswig-Jones, Trager↗
OPFLearn.jl v0.1.2 5/18/2023 [SWR-21-109]
OPFLearn.jl is a Julia package for creating datasets for machine learning approaches to solving AC optimal power flow (AC OPF). It was developed to provide researchers with a standardized way to efficiently create AC OPF datasets that are representative of more of the AC OPF feasible load space compared to typical dataset creation methods. The OPFLearn dataset creation method uses a relaxed AC OPF formulation to reduce the volume of the unclassified input space throughout the dataset creation process. Over time this input space tightens around the relaxed AC OPF feasible region to increase the percentage of feasible load profiles found while uniformly sampling the input space. Load samples are processed using AC OPF formulations from PowerModels.jl. More information on the dataset creation method can be found in our publication, "OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets". To use OPFLearn.jl a PowerModels network data dictionary is required (can be loaded from Matpower ".m" files) to define the network the dataset is being created for.
Joswig-Jones, Trager↗
Recovery Act: A Measurement -- Management Technology for Improving Energy Efficiency in Data Centers and Telecommunication Facilities (Final Technical Report)
Data center (DC) electricity use is increasing at an annual rate of over 20% and presents a concern for the Information Technology (IT) industry, governments, and the society. A large fraction of the energy use is consumed by the compressor cooling to maintain the recommended operating conditions for IT equipment. The most common way to improve the DC efficiency is achieved by optimally provisioning the cooling power to match the global heat dissipation in the DC. However, at a more granular level, the large range of heat densities of today's IT equipment makes the task of provisioning cooling power optimized to the level of individual computer room air conditioning (CRAC) units much more challenging. Distributed sensing within a DC enables the development of new strategies to improve energy efficiency, such as hot spot elimination through targeted cooling, matching power consumption at rack level with workload schedule, and minimizing power losses. The scope of Measurement and Management Technologies (MMT) is to develop a software tool and the underlying sensing technology to provide critical decision support and control for DC and telecommunication facilities (TF) operations. A key aspect of MMT technology is integration of modeling tools to understand how changes in one operational parameter affect the overall DC response. It is demonstrated that reduced ordered models for DC can generate, in less than 2 seconds computational time, a three dimensional thermal model in a 50 kft 2 DC. This rapid modeling enables real time visualization of the DC conditions and enables "0what if" scenarios simulations to characterize response to "disturbances". One such example is thermal zone modeling that matches the cooling power to the heat generated at a local level by identifying DC zones cooled by a specific CRAC. Turning off a CRAC unit can be simulated to understand how the other CRAC utilization changes and how server temperature responds. Several new sensing technologies were added to the existing MMT platform: (1) air contamination (corrosion) sensors, (2) power monitoring, and (3) a wireless environmental sensing network. All three technologies are built on cost effective sensing solutions that increase the density of sensing points and enable high resolution mapping of DCs. The wireless sensing solution enables Air Conditioning Unit (ACU) control while the corrosion sensor enables air side economization and can quantify the risk of IT equipment failure due to air contamination. Validation data for six test sites demonstrate that leveraging MMT energy efficiency solutions combined with industry best practices results in an average of 20% reduction in cooling energy, without major infrastructure upgrades. As an illustration of the unique MMT capabilities, a data center infrastructure efficiency (DCIE) of 87% (industry best operation) was achieved. The technology is commercialized through IBM System and Technology Lab Services that offers MMT as a solution to improve DC energy efficiency. Estimation indicates that deploying MMT in existing DCs can results in an 8 billion kWh savings and projection indicates that constant adoption of MMT can results in obtainable savings of 44 billion kWh in 2035. Negotiations are under way with business partners to commercialize/license the ACU control technology and the new sensor solutions (corrosion and power sensing) to enable third party vendors and developers to leverage the energy efficiency solutions.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗