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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Self-Organizing Map-Based Resilience Quantification and Resilient Control of Distribution Systems Under Extreme Events

Due to climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution system operators (DSO) to ensure that there is uninterrupted power supply to critical loads in their networks. To embed resilience into DSO's decision-making, resilience needs to be first quantified and then integrated into the system-level optimization. Therefore, this paper first develops a novel self-organizing map (SOM) based method (called SomRes) to quantify the time-varying resilience index of a system that can leverage the powerful classification property of SOMs and removes some of the disadvantages of subjective weight assignment methods. Using SomRes, a resilient resource allocation and operational dispatch algorithm is further developed to enhance system resilience against extreme events by considering the SomRes resilience index directly as the feedback. Here, the proposed resilience quantification approach is benchmarked with a state-of-the-art approach and the efficacy of the proposed resilient dispatch algorithm is demonstrated through several deterministic and statistical case studies on the IEEE 123-bus distribution system. Simulation studies show that the proposed SomRes quantification method is an appropriate indicator of system resilience, and the resilient resource allocation and dispatch strategy can significantly reduce critical load shedding under varying event propagation scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Experimental Evaluation of Load Rejection Over-Voltage from Grid-Tied Solar Inverters

This paper investigates the impact of load rejection over-voltage (LRO) from commercially available grid-tied photovoltaic (PV) solar inverters. LRO can occur when a local feeder or breaker opens and the power output from a distributed energy resource exceeds the load power. Simplified models of current controlled inverters can over-predict over-voltage magnitudes, thus it is useful to quantify testing. The load rejection event was replicated using a hardware testbed at the National Renewable Energy Laboratory (NREL), and a set of commercially available PV inverters was tested to quantify the impact of LRO for a range of generation-to-load ratios. The magnitude and duration of the over-voltage events are reported in this paper along with a discussion of characteristic inverter output behavior. The results for the inverters under test showed that maximum over-voltage magnitudes were less than 200 percent of nominal voltage, and much lower in many test cases. These research results are important because utilities that interconnect inverter-based DER need to understand their characteristics under abnormal grid conditions.

14 SOLAR ENERGY↗
DOE OSTI2020

Leveraging Fine-Grained Occupancy Estimation Patterns for Effective HVAC Control

As occupancy sensing technologies become mature, various occupancy sensors are increasingly deployed in commercial buildings for pervasive occupancy monitoring. These sensors provide occupant-count data, which contains rich spatiotemporal information about occupancy patterns. With long-term occupant-count data collected from a commercial building, we design three different predictive models that capture the occupancy dynamics and examine how a model predictive control of the HVAC system benefits from actual occupancy count prediction. Our analysis reveals that mispredictions of occupancy states, especially false positives and false negatives, may introduce inefficient control that leads to energy waste or user discomfort. To address this issue, we take a step further to design an adaptive model predictive controller that minimizes inefficient control actions according to misprediction types and distributions. A comprehensive evaluation is performed in OpenBuild and EnergyPlus simulators to study the effectiveness of the proposed end-to-end control strategy. The evaluation shows that the proposed solution reduces energy consumption by 29.5% while improving the average weighted occupants comfort by 86.7% in Predicted Mean Vote (PMV) over the fixed schedule strategy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

IoT-Based Comfort Control and Fault Diagnostics System for Energy-Efficient Homes

This project studies an Internet of Things (IoT)-based comfort control and fault diagnostics system (referred as iComfort in this report) for energy-efficient homes. The system delivers an occupant-comfort-oriented thermal environment adaptive to fault scenarios and achieves HVAC energy savings in a cost-effective and straightforward way. This smart iComfort home system consists of the following key features. 1) Cost-effectiveness and scalability of the entire hardware and software system: The system includes low-cost temperature, humidity, and airflow sensors, and a Raspberry Pi-based local hub that interfaces with the cloud and IoT-enabled devices. The cost is low, not only for sensors, but also the costs associated with sensor installation, system setup and commissioning, data communication and storage, and data analytics (e.g., the development of automated fault detection and diagnosis (AFDD), as well as adaptive control strategies that are both computationally efficient and practical to implement). 2) Energy performance and user satisfaction: The system delivers user satisfaction and energy savings. This includes a) ease of use, b) optimal occupant thermal comfort, and c) accurate system feedback (e.g., low false alarm of AFDD strategies). 3) Favorable demonstrated prototype performance: The prototype tested at the Pacific Northwest National Laboratory (PNNL) Lab Homes demonstrates the accuracy of fault detections and diagnoses and shows thermal comfort improvement and energy savings through adaptive and optimal HVAC operations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Distributed MPC for Efficient Coordination of Storage and Renewable Energy Sources Across Control Areas

In electric power systems, multiple entities are responsible for ensuring an economic and reliable way of delivering power from producers to consumers. With the increase of variable renewable generation it is becoming increasingly important to take advantage of the individual entities' (and their areas') capabilities for balancing variability. Hence, in this paper, we employ and extend the approximate Newton directions method to optimally coordinate control areas leveraging storage available in one area to balance variable resources in another area with only minimal information exchange among the areas. The problem to be decomposed is a model predictive control problem including generation constraints, energy storage constraints, and AC power flow constraints. Singularity issues encountered when formulating the respective Newton-Raphson steps due to intertemporal constraints are addressed and extensions to the original decomposition method are proposed to improve the convergence rate and required communication of the method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Effects of Substituents on the S N 2 Free Energy of Activation for α-O-4 Lignin Model Compounds

Current methods for processing lignocellulosic biomass fail to take full advantage of the phenolic chemicals available in lignin, the third most common polymer on the planet. The complexity of the lignin polymer structure, the lack of knowledge of the mechanisms by which it breaks down, and the difficulties in predicting and controlling the reaction product distribution make improvement in processing methods challenging. The use of model compounds, which contain only a few key features of the native polymer, allow the reactivity of the overall polymer to be investigated more simply by isolating key elements. In our previous work, we examined the mechanism of acidolysis for two α-O-4 lignin model compounds, benzylphenyl ether (BPE) and 1-(phenoxyethyl)benzene (PEB). In the present work, we examine the rate-limiting step of this mechanism, a nucleophilic attack on the α-carbon commensurate with protonation of the ether oxygen, for other α-O-4 model compounds that contain features more reminiscent of native lignin, including compounds based on native model dimers. The effects of individual substituents and combinations of substituents are also examined in order to determine their contributions to the reactivity of native lignin dimers. Simple relationships are examined between ground-state properties and reactivity. The results of these calculations show that the effects of substituents on the reactivity of BPE-based compounds are quite different from those of PEB-based compounds. While reasonable correlations can be found for BPE reactivity and properties across substituents, PEB shows much less predictable reactivity except among small subsets of substituents. Overall, the trends observed here provide useful information about the reactivity of α-O-4 bonds in lignin during acidolysis as a function of the local chemical environment.

Pelzer, Adam W.↗
DOE OSTI2020

The Heuristic Dynamic Programming Approach in Boost Converters

In this study, a heuristic dynamic programming controller is proposed to control a boost converter. Conventional controllers such as proportional-integral-derivative (PID) or proportional-integral (PI) are designed based on the linearized small-signal model near the operating point. Therefore, the performance of the controller during the start-up, the load change, or the input voltage variation is not optimal since the system model changes by varying the operating point. The heuristic dynamic programming controller optimally controls the boost converter by following the approximate dynamic programming. The advantage of the HDP is that the neural network-based characteristic of the proposed controller enables boost converters to easily cope with large disturbances. An HDP with a well-trained critic and action networks can perform as an optimal controller for the boost converter. To compare the effectiveness of the traditional PI-based and the HDP boost converter, the simulation results are provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

An Ensemble Optimization Framework for Coupled Design of Hydropower Contracts and Real-Time Reservoir Operating Rules

Often, revenues from hydropower generation depend on the operator's ability to provide firm power in the presence of uncertain inflows. The primary options available for optimizing revenue are negotiation of a firm power contract before operations begin and adjustment of the reservoir release during operations. Contract and release strategy optimization are closely coupled and most appropriately analyzed with stochastic real-time control methods. Here we use an ensemble-based approach to stochastic optimization that provides a convenient way to construct nonparametric revenue probability distributions to explore the implications of uncertainty. The firm power contract is a simplified bilateral fixed price agreement that partially insulates operator and buyer from price fluctuations. The release control laws and firm energy target are jointly optimized to maximize the operator's expected revenue. Revenue probability distributions and related spill performance statistics indicate that predictive operating strategies such as stochastic dynamic programming and model predictive control can give significantly better performance than standard deterministic operating rules. The performance obtained from batch optimization with perfect inflow information establishes a convenient upper bound on potential revenue and provides a baseline for assessing the significance of differences between real-time operating strategies. Sensitivity analysis indicates that the benefits of predictive operational strategies are greatest for reservoirs with medium nondimensional residence times and less important for reservoirs with large residence times. Overall, probabilistic analysis of the coupled hydropower contract-operations problem provides a realistic way to assess revenue and risk for reservoirs that must provide firm power when inflows are uncertain.

13 HYDRO ENERGY↗
DOE OSTI2025

Optimization-Based Dynamic Voltage Support of Microgrids Using Energy Storage Systems

A microgrid network is characterized by a high R/X ratio, making the voltage more sensitive to active power changes compared to bulk power systems, where the voltage is regulated primarily by reactive power. Due to its sensitivity, voltage control approaches for microgrids should also consider the active power input coupling, making it very different from conventional power systems. Additionally, as the energy costs associated with active and reactive powers are different and the operational conditions of microgrids connected to active distribution systems vary over time, the ideal controller to provide voltage support must be flexible enough to handle these technical and operational constraints. This paper proposes a model predictive control approach to provide dynamic voltage support using energy storage systems. This approach uses a simplified predictive model of the system to solve the model predictive control problem. By proper selection of model predictive control weighting parameters, the quality of service provided can be adjusted to achieve the desired performance. A simulation study in MATLAB/Simulink validates the proposed approach for the Cordova, Alaska microgrid. Results show that the performance of the voltage support can be adjusted depending on the choice of weight and constraints of the controller.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Airborne measurements of isoprene and monoterpene emissions from southeastern U.S. forests

Isoprene and monoterpene emission rates are essential inputs for atmospheric chemistry models that simulate atmospheric oxidant and particle distributions. Process studies of the biochemical and physiological mechanisms controlling these emissions are advancing our understanding and the accuracy of model predictions but efforts to quantify regional emissions have been limited by a lack of constraints on regional distributions of ecosystem emission capacities. We used an airborne wavelet-based eddy covariance measurement technique to characterize isoprene and monoterpene fluxes with high spatial resolution during the 2013 SAS (Southeast Atmosphere Study) in the southeastern United States. The fluxes measured by direct eddy covariance were comparable to emissions independently estimated using an indirect inverse modeling approach. Isoprene emission factors based on the aircraft wavelet flux estimates for high isoprene chemotypes (e.g., oaks) were similar to the MEGAN2.1 biogenic emission model estimates for landscapes dominated by oaks. Aircraft flux measurement estimates for landscapes with fewer isoprene emitting trees (e.g., pine plantations), were about a factor of two lower than MEGAN2.1 model estimates. The tendency for high isoprene emitters in these landscapes to occur in the shaded understory, where light dependent isoprene emissions are diminished, may explain the lower than expected emissions. This result demonstrates the importance of accurately representing the vertical profile of isoprene emitting biomass in biogenic emission models. Airborne measurement-based emission factors for high monoterpene chemotypes agreed with MEGAN2.1 in landscapes dominated by pine (high monoterpene chemotype) trees but were more than a factor of three higher than model estimates for landscapes dominated by oak (relatively low monoterpene emitting) trees. This results suggests that unaccounted processes, such as floral emissions or light dependent monoterpene emissions, or vegetation other than high monoterpene emitting trees may be an important source of monoterpene emissions in those landscapes and should be identified and included in biogenic emission models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Model predictive control for demand flexibility: Real-world operation of a commercial building with photovoltaic and battery systems

Hundreds of studies have investigated Model Predictive Control (MPC) for the optimal operation of building energy systems in the past two decades. However, MPC field tests are still uncommon, especially for small- and medium-sized commercial buildings and for buildings integrated with onsite renewables. This paper describes the implementation and the long-term performance evaluation of an MPC controller in a small commercial building equipped with behind-the-meter photovoltaics and electrochemical batteries. MPC controls space conditioning, commercial refrigeration, and the battery system. We tested two types of demand flexibility applications in the field: electricity bill minimization under time-of-use tariffs and responses to grid flexibility events. Results show that the proposed controller achieves 12% of annual electricity cost savings and 34% peak demand reduction against the baseline, while respecting thermal comfort and food safety. The field tests also demonstrate the ability of the MPC controller to provide a multitude of grid services including real-time pricing, demand limiting, load shedding, load shifting, and load tracking, using the same optimization framework.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Predicting distribution of malaria vector larval habitats in Ethiopia by integrating distributed hydrologic modeling with remotely sensed data

Abstract Larval source management has gained renewed interest as a malaria control strategy in Africa but the widespread and transient nature of larval breeding sites poses a challenge to its implementation. To address this problem, we propose combining an integrated high resolution (50 m) distributed hydrological model and remotely sensed data to simulate potential malaria vector aquatic habitats. The novelty of our approach lies in its consideration of irrigation practices and its ability to resolve complex ponding processes that contribute to potential larval habitats. The simulation was performed for the year of 2018 using ParFlow-Common Land Model (CLM) in a sugarcane plantation in the Oromia region, Ethiopia to examine the effects of rainfall and irrigation. The model was calibrated using field observations of larval habitats to successfully predict ponding at all surveyed locations from the validation dataset. Results show that without irrigation, at least half of the area inside the farms had a 40% probability of potential larval habitat occurrence. With irrigation, the probability increased to 56%. Irrigation dampened the seasonality of the potential larval habitats such that the peak larval habitat occurrence window during the rainy season was extended into the dry season. Furthermore, the stability of the habitats was prolonged, with a significant shift from semi-permanent to permanent habitats. Our study provides a hydrological perspective on the impact of environmental modification on malaria vector ecology, which can potentially inform malaria control strategies through better water management.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

High Penetration of Photovoltaic Generation Study – Flagstaff Community Power (Final Technical Report, Results of Phases 2-5)

APS’s renewable energy portfolio, driven in part by Arizona’s Renewable Energy Standard (RES) currently includes more than 1100 MW of installed capacity, equating to roughly 3000 GWh of annual production. Overall renewable production is expected to grow to 6000 GWh by 2025. It is expected that distributed photovoltaics, driven primarily by lower cost, will contribute to much of this growth and that by 2025, distributed installations will account for half of all renewable production (3000GHW). As solar penetration increases, additional analysis may be required for routine utility processes to ensure continued safe and reliable operation of the electric distribution network. Such processes include residential or commercial interconnection requests and load shifting during normal feeder operations. Circuits with existing high solar penetration will also have to be studied and results will need to be evaluated for adherence to utility practices or strategy. Increased distributed PV penetration may offer benefits such as load offsetting, but it also has the potential to adversely impact distribution system operation. These effects may be exacerbated by the rapid variability of PV production. Detailed effects of these phenomena in distributed PV applications continue to be studied. Comprehensive, high-resolution electrical models of the distribution system were developed to analyze the impacts of PV on distribution circuit protection systems (including coordination and anti-islanding), predict voltage regulation and phase balance issues, and develop volt/VAr control schemes. Modeling methods were refined by validating against field measurements. To augment the field measurements, methods were developed to synthesize high resolution load and PV generation data to facilitate quasi-static time series simulations. The models were then extended to explore boundary conditions for PV hosting capability of the feeder and to simulate common utility practices such as feeder reconfiguration. The modeling and analysis methodology was implemented using open source tools and a process was developed to aid utility engineers in future interconnection requests. Methods to increase PV hosting capacity were also explored during the course of the study. A 700kVA grid-supportive inverter was deployed on the feeder and each grid support mode was demonstrated. Energy storage was explored through simulation and models were developed to calculate the optimum size and placement needed to increase PV hosting capacity. A tool was developed to aid planners in assigning relative costs and benefits to various strategies for increasing PV hosting capacity beyond current levels. Following the completion of the project, APS intends to use the tools and methods to improve the framework of future PV integration on its system. The tools and methods are also expected to aid other utilities to accelerate distributed PV deployment.

14 SOLAR ENERGY↗
DOE OSTI2021

MPC-based double-layer real-time conditional cSelf-restoration for interconnected microgrids

In this report, we propose a novel model predictive control(MPC)-based real-time conditional self-restoration energy management system (CSR-EMS) for interconnected microgrids (IMGs) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). Superior to the existing IMG self-restoration methods, the “conditionality” of the proposed CSR-EMS can economically realize self-restoration and grid-assisted restoration during energy deficiency or faults, in both islanded and grid-connected modes. Cost minimization is implemented as the objective function to judge in real-time which restoration mode is economically preferred. The proposed CSR-EMS comprises two layers–the lower layer operates locally to eliminate electricity fluctuations created by RESs and ensure economic effectiveness within an MG, whereas the upper layer oversees the real-time operational status of the IMG system and determines power exchange among microgrids (MGs) during abnormalities. In detail, when a microgrid inside the IMG system experiences an energy deficiency, the CSR-EMS, on an MPC basis, intelligently optimizes power production from each dispatchable distributed generator (DG), ESS, power imported from the main grid, and power exchange among the IMGs to maintain the demand–supply balance, while considering system recovery cost, state of charge (SoC) of ESSs and operation modes of the IMGs (i.e., grid-connected or islanded mode). Simulation results and comparisons with existing IMG self-healing EMSs demonstrate the economic efficacy of the proposed CSR-EMS strategy during normal and abnormal operations, which can be used as an energy control framework for modern power systems with multiple interconnected microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

SolarPlus-Optimizer v0.1

With the falling costs of solar arrays and battery storage and reduced reliability of the grid due to natural disasters, small-scale local generation and storage resources are beginning to proliferate. However, very few software options exist for integrated control of building loads, batteries and other distributed energy resources. The available software solutions on the market can force customers to adopt one particular ecosystem of products, thus limiting consumer choice, and are often incapable of operating independently of the grid during blackouts. In this software package, we present the "Solar+ Optimizer" (SPO), a control platform that provides demand flexibility, resiliency and reduced utility bills, built using open-source software. SPO employs Model Predictive Control (MPC) to produce real time optimal control strategies for the building loads and the distributed energy resources on site. SPO is designed to be vendor-agnostic, protocol-independent and resilient to loss of wide-area network connectivity. The software was evaluated in a real convenience store in northern California with on-site solar generation, battery storage and control of HVAC and commercial refrigeration loads. Preliminary tests showed price responsiveness of the building and cost savings of more than 10% in energy costs alone.

Prakash, AnandKrishnan↗
DOE OSTI2023

Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes

The ability of recurrent neural networks (RNN) to model nonlinear dynamics of high dimensional process data has enabled data-driven RNN-based fault detection algorithms. Previous studies have focused on detecting faults by identifying the discrepancies in data distribution between the faulty and normal data, as reflected in prediction errors generated by RNN models. However, in industrial processes, variations in data distribution can also result from changes in normal control setpoints and compensatory control adjustments in response to disturbances, making it hard to differentiate between normal and faulty conditions. This paper proposes a fault detection method utilizing a long short-term memory (LSTM) encoder–decoder structure with regularized hidden dynamics and reversible instance normalization (RevIN) to compactly represent high-dimensional measurements for effective monitoring. During training, the hidden states of the model are regularized to form a low-dimensional latent space representation of the original multivariate time series data. As a result, the prediction errors of the latent states can be used to monitor the abnormal dynamic variations, while the reconstruction errors of the measured variables are used to monitor the abnormal static variations. Furthermore, the proposed indices can reflect operating conditions, even when the distribution of test data changes, which helps distinguish faults from normal adjustments and disturbances that controllers can settle. Here, data from numerical simulation and the Tennessee Eastman process are used to illustrate the effectiveness of the proposed fault detection method.

42 ENGINEERING↗
DOE OSTI2024

Topographical Controls on Hillslope-Scale Hydrology Drive Shrub Distributions on the Seward Peninsula, Alaska: Modeling Archive

The dataset is a model output used in a study that examined the role topography plays in determining shrub expansion by applying a coupled transect version of a mechanistic ecosystem model (ecosys) in a tundra hillslope site in the Seward Peninsula, Alaska. The study highlights the importance of mechanistically representing hillslope-scale coupled surface and sub-surface hydrology to accurately predict current plant distributions and future trajectories in Arctic ecosystems. The dataset contain ecosys model outputs used in a study: Article Title - Topographical Controls on Hillslope-Scale Hydrology Drive Shrub Distributions on the Seward Peninsula, Alaska. It includes the data files (ten *.csv files) used in creating figures in the publication and three *.pdf files (user guide and two Supplementary Information documents).The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗