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slacgismo/advanced-load-modeling
This is the repository for "advanced-load-modeling" developed by SLAC National Accelerator Laboratory.
Idaho Falls Power Black Start Field Demonstration (Preliminary Outcomes Paper)
This April 2021 field demonstration builds upon a 2017 field demonstration in which it was determined IFP’s HPPs, on their own, can support islanded black start and operation up to 2.5 MW loading. Modeling and hardware-in-the-loop testing was used in the intervening period to design an energy storage solution, specifically using ultracapacitors, to reduce likelihood of generators tripping during the field demonstration. Overall, the 2021 field demonstration tested three different options for meeting IFP’s requirements: innovating the hydropower controls, synchronizing multiple HPPs on the system, and integrating an ultracapacitor energy storage system. This report documents the testing performed. Follow-on analysis will provide additional insights, for example, including a complete table of comparative results between the tests and scenarios. The analysis will also propose a refined design for an energy storage system to meet IFP’s grid islanded needs.
Electric Vehicle Infrastructure Consequence Assessment
With consumers’ growing interest in electric vehicles, extreme fast charging stations are poised to provide high-power charging to rapidly recharge light-duty passenger vehicles. High-power charging requires high-level communication between vehicle and charger to govern the charging process. The coupling of power and communication increases the potential scale of cyberattacks. Using a full Western Electricity Coordinating Council planning model, load manipulation from high-power charging infrastructure is investigated. Two cases of load manipulation are studied: (i) a discrete, widespread system event and (ii) loads modulated near the Western Interconnect’s resonant frequency. In (i) some generation trips and in (ii) oscillations are observed on the California Oregon Intertie. Neither scenario results in significant adverse effects to the grid.
Demand response of loads having thermal reserves
Systems and methods are described herein that improve grid performance by smoothing demand using thermal reserves. The smoothed demand can reduce peak loads as well as the ramp rate of demand that will otherwise require the use of inefficient, expensive generation sources. These improvements are tied to the selective switching on or off electrical loads that are coupled to thermal reserves, effectively using the thermal reserves as an energy storage mechanism. Historical data of past usage can be used to create load model and ensure that effects on customer comfort are minimized while still accomplishing the beneficial effects for the overall grid, which enables grid owners to both reduce their operational cost by avoiding expensive generation and improve system reliability by achieving more predictable power demand.
A large deformation multiphase continuum mechanics model for shock loading of soft porous materials
A large deformation, coupled finite-element (FE) model is developed to simulate the multiphase response of soft porous materials subjected to high strain-rate loading. The approach is based on the theory of porous media (TPM) at large deformations. Simplifications to the one-dimensional regime studied in the numerical simulations follow. An overview of several different time integration schemes is presented for the purpose of solving the nonlinear dynamic coupled balance of momenta (mixture and fluid) and balance of mass of the mixture equations. Numerical examples are presented for (i) verification against closed-form analytical solutions assuming small loads, (ii) demonstrating large deformation effects at high strain-rate, and (iii) showing differences in deformations between a single-phase elastodynamics model with occluded compressible pore fluid and a multiphase poroelastodynamics model at high strain-rate. The multiphase model shows that the relative motion of the pore fluid significantly dampens the deformation response of the solid skeleton as compared to the single-phase model, and makes it possible to extract quantitative values for the stresses of the different constituents, thereby allowing one to form preliminary conclusions about the onset of damage in the solid skeleton. The novelty of the current work is developing a multiphase, large deformation, mixture theory numerical model for high strain-rate loading of soft porous materials. It was discovered that explicit, adaptive time-stepping Runge–Kutta schemes offer high accuracy at relatively low cost when compared to traditional implicit or explicit central difference time-stepping schemes for shock-like loadings. Here, shock viscosity is added to the mixture momentum balance equation to regularize the shock front, and a stabilization term is added to the mixture mass balance equation to stabilize equal order interpolation finite elements for the coupled finite element solution of multiphase materials.
Tri-Level Linear Programming Model for Automatic Load Shedding Using Spectral Clustering
Traditional load shedding schemes can be inadequate in grids with high renewable penetration, leading to unstable events and unnecessary grid islanding. Although for both manual and automatic operating modes load shedding areas have been predefined by grid operators, they have remained fixed, and may be sub-optimal due to dynamic operating conditions. In this work, a distributed tri-level linear programming model for automatic load shedding to avoid system islanding is presented. Preventing islanding is preferred because it reduces the need for additional load shedding besides the disconnection of transmission lines between islands. This is crucial as maintaining the local generation-demand balance is necessary to preserve frequency stability. Furthermore, uneven distribution of generation resources among islands can lead to increased load shedding, causing economic and reliability challenges. This issue is further compounded in modern power systems heavily dependent on non-dispatchable resources like wind and solar. The upper-level model uses complex power flow measurements to determine the system areas to shed load depending on actual operating conditions using a spectral clustering approach. The mid-level model estimates the area system state, while the lower-level model determines the locations and load values to be shed. The solution is practical and promising for real-world applications.
Comparison of time-frequency-analysis techniques applied in building energy data noise cancellation for building load forecasting: A real-building case study
Time-frequency analysis that disaggregates a signal in both time and frequency domain is an important supporting technique for building energy analysis such as noise cancellation in data-driven building load forecasting. There is a gap in the literature related to comparing various time–frequency-analysis techniques, especially discrete wavelet transform (DWT) and empirical mode decomposition (EMD), to guide the selection and tuning of time–frequency-analysis techniques in data-driven building load forecasting. This article provides a framework to conduct a comprehensive comparison among thirteen DWT/EMD techniques with various parameters in a load forecasting modeling task. A real campus building is used as a case study for illustration. The DWT and EMD techniques are also compared under various data-driven modeling algorithms for building load forecasting. The results in the case study show that the load forecasting models trained with noise-cancelled energy data have increased their accuracy to 9.6% on average tested under unseen data. This study also shows that the effectiveness of DWT/EMD techniques depends on the data-driven algorithms used for load forecasting modeling and the training data. Hence, DWT/EMD-based noise cancellation needs customized selection and tuning to optimize their performance for data-driven building load forecasting modeling.
Aerodynamic Analysis of Wind Loading on Parabolic Trough Collectors Using High-Fidelity CFD Modeling
Wind loading on parabolic solar collector support structures is the key driver for material costs. As identified by NREL's CSP Best Practices project [1], ability to predict wind-loading in deep-arrays and the impact of edge effects has potential to decrease levelized cost of electricity (LCoE) by 15%. In this study, we present a high-fidelity computational model that can model the flow around deep arrays of parabolic troughs under varying turbulent inflow conditions. Ability to accurately reproduce atmospheric boundary layer (ABL) profiles is first demonstrated through Large Eddy Simulation (LES) simulations and compared against wind tunnel measurements. Thereafter, unsteady turbulent flow past single row and six row configurations is computed using hybrid RANS/LES simulations. Investigations of wind loading, through parametric tilt angle variations of single row and on waked collectors in six-row configuration, are used to highlight design considerations for parabolic troughs only possible through high-fidelity unsteady calculations.
Numerical study of damage in particulate composites during high-strain rate loading using novel damage model
Not provided.
Transfer Learning Trained LSTM Models for Household Load Profile Forecasting
Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.
Derivation of the Structural Integrity of Residual (SIR) glass model for the enhancement of waste loading
A new model based on glass structure to allow for enhanced waste loading in nuclear waste glass while maintaining chemical durability is proposed. The model is derived by splitting the molar concentrations of the targeted starting glass composition into theoretical crystalline phases anticipated to be observed during devitrification and a residual glass. An empirically derived relationship based on maintaining the residual glass structure, determined from a calculated non-bridging oxygen content, was demonstrated to successfully screen glasses for acceptable durability. The proposed model can successfully identify durable glass compositions containing 20–35 wt% Al 2 O 3 , a concentration that would significantly increase the projected waste loading in glasses processed at the Hanford Tank Waste Treatment and Immobilization Plant.
Evaluation of Equivalent Battery Model Representations for Thermostatically Controlled Loads in Commercial Buildings
Models for thermostatically controlled loads in commercial buildings often include many parameters and variables compared to residential buildings. As such, it is beneficial to use reduced-order models to represent these resources. A classic example of such a model is the Virtual Battery or Equivalent Battery Model (EBM). In this paper, the typical EBM is extended to higher-order commercial Heating, Ventilation, and Air-conditioning (HVAC) models and adapted for electric water heaters. Finally, we compare the performance of EBMs with detailed thermal models using three classic optimization problems - energy maximization, energy minimization, and power reference tracking. Our results show that the EBM-constrained and detailed thermal model-constrained problems produce similar outcomes in terms of temperature, power, and total energy consumption.
A wide-range thermodynamically consistent constitutive model for dynamic loading of partially saturated porous media
SUMMARY This paper presents a novel thermodynamically consistent constitutive model for partially saturated porous rocks across a wide range of conditions. The material states generated behind the shock wave from an explosive source can vary significantly, ranging from crushed and melted rock near the source to a poroelastic medium in the far field. In the model, rock strength is determined by the effective pressure, which is calculated using two independent equations of state: one for the solid rock and another for the pore fluid. The model accounts for shock-induced liquefaction resulting from fluid pressure buildup in the pore spaces near the explosive source. Simultaneously, it describes the increase in wave propagation speed due to elastic pore contraction in both dry and partially saturated rocks. This model is applied to investigate how fluid saturation affects the amplitude and shape of the generated waves, as well as the residual stress surrounding the cavity formed by spherical explosions.
Direct Envelope Modeling of Load-Resonant Inverter for Wireless Power Transfer Applications
This paper presents the application of direct envelope modeling technique to primary side LCC tuned resonant wireless power transfer system. The method adapted here decomposes the HF resonant inverter output to a fundamental component and two dominant sidebands. The small-signal dutycycle-to-primary-coil-current-envelope transfer function of a primary side LCC tuned WPT system is then derived as the summation of the responses to the fundamental and the sideband components. The theoretical and simulation results are in good agreement indicating the validity of the approach to be applied to WPT systems.
Bi-Level Linear Programming Model for Automatic Load Shedding: A Distributed Wide-Area Measurement System-based Solution
Load shedding is currently implemented as a two-step based approach. In the first step, manual load shedding is taken place, were system operators, using estimates, inform distribution utilities of predicted stressful conditions. Information provided include the potential use of energy reserves, as well as load shedding amount. In a second step, automatic load shedding is done. The latter is realized using protection relays. While considering frequency variation, pre-defined values of load to be shed and correspondent number of stages for such to be realized are transformed into relay settings. Under-frequency protection relays use only local measurements towards decision making, thus operate in a decentralized architecture. Decision making is done in milliseconds plus breaker time. While this approach has provided much system reliability, considering the new smart grid paradigm, where system dynamics are much faster due to increasing renewable resources penetration, in some operating conditions it will generate sub-optimal solutions, such as islanding. Phasor measurement units provide a source of information which can be useful for this problem. Centralized architecture-based solutions for automatic load shedding, as present in the state-of-the-art, require though total processing times which are not acceptable for real-life implementation. In this work, considering the above, a bi-level linear programming model is presented. The model is implemented considering a distributed architecture while leveraging phasor measurement units data. The upper-level model estimates the current system state. Results of this model are embedded in a lower-level model, which decision variables are the location and load value to be shed. Easy-to-implement model, built-on the classic weighted least squares solution, highlight potential aspects towards real-life applications.
Weather and Random Forest-based Load Profiling Approximation Models and its Transferability across Climate Zones
This study is to provide predictive understanding of the associations of various weather attributes with residential and commercial load profiles, for a variety of climate zones and seasons. In this work, machine learning (ML) approaches were used to identify and quantify the impacts of various weather attributes on residential and commercial electricity demand and its components across the western United States. Performance and transferability of the developed ML models were then evaluated across different temperate zones (e.g., southern, middle, and northern US) and across coastal, mid-continent, and wet zones, with inputs of weather condition data from the National Oceanic and Atmospheric Administration (NOAA) at representative weather stations. The predictive models were developed based on the ranked/screened factors using the regression tree (RT) and random forest (RF) approaches, for five different scenarios (seasons).
Nonlinear Multiobjective MPC-Based Optimal Operation of a High Consistency Refining System in Papermaking
As one of the most important unit in the papermaking industry, the high consistency (HC) refining system is confronted with challenges such as improving pulp quality, energy saving, and emissions reduction in its operation processes. Here in this correspondence, an optimal operation of HC refining system is presented using nonlinear multiobjective model predictive control strategies that aim at set-point tracking objective of pulp quality, economic objective, and specific energy (SE) consumption objective, respectively. First, a set of input and output data at different times are employed to construct the subprocess model of the state process model for the HC refining system, and then the Wiener-type model can be obtained through combining the mechanism model of Canadian Standard Freeness and the state process model that determines their structures based on Akaike information criterion. Second, the multiobjective optimization strategy that optimizes both the set-point tracking objective of pulp quality and SE consumption is proposed simultaneously, which uses NSGA-II approach to obtain the Pareto optimal set. Furthermore, targeting at the set-point tracking objective of pulp quality, economic objective, and SE consumption objective, the sequential quadratic programming method is utilized to produce the optimal predictive controllers. In conclusion, the simulation results demonstrate that the proposed methods can make the HC refining system provide a better performance of set-point tracking of pulp quality when these predictive controllers are employed. In addition, while the optimal predictive controllers orienting with comprehensive economic objective and SE consumption objective, it has been shown that they have significantly reduced the energy consumption.