Switching Transition Analysis and Optimization for Bidirectional CLLC Resonant DC Transformer
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Engineering↗
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Results for “power system optimization”
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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Revisiting Theoretical Limits for One Degree-of-Freedom Wave Energy Converters
This work revisits the theoretical limits of one degree-of-freedom wave energy converters (WECs). This work considers the floating sphere used in the OES Task 10 WEC modeling and verification effort for analysis. Analytical equations are derived to determine bounds on displacement amplitude, time-averaged power (TAP), and power-take-off (PTO) force. A unique result found shows that the TAP absorbed by a WEC can be defined solely by the inertial properties and radiation hydrodynamic coefficients. In addition, a unique expression for the PTO force was derived that provides lower and upper bounds when resistive control is used to maximize power generation. For complex conjugate control, this same expression only provides a lower bound, as there is theoretically no upper bound. These bounds assist in comparing the performance of the floating sphere if it were to extract energy using surge or heave motion. The analysis shows because of differences in hydrodynamic coefficients for each oscillating mode, there are different frequency ranges that provide better power capture efficiency. The influence of a motion constraint on TAP while utilizing a nonideal power take-off is examined and found to reduce the losses associated with bidirectional energy flow. The expression to calculate TAP with a nonideal PTO is modified by the electrical conversion efficiency and the ratio of the PTO spring and damping coefficients. Furthermore, the PTO spring and damping coefficients were separated in the expression, allowing for limits to be set on the PTO coefficients to ensure net power generation.
16 TIDAL AND WAVE POWER↗
Selecting and Implementing Resilience Metrics in Existing Energy Sector Models [Slides]
Resilience is a topic receiving much attention in relation to energy systems, with particular attention being paid to the supply of electricity. As a result of the growing interest in energy sector resilience, research communities have proposed a plethora of candidate resilience indicators and metrics, most of which remain immature at different scales and segments within the energy system. A necessary focus of the research community lies in implementing, testing, and validating resilience metrics and analysis approaches in energy sector models, which will be invaluable for informing resilience planning and investment decisions. Recognizing these challenges that need to be addressed, we explore how to effectively integrate resilience considerations into energy sector models and tools. The overarching goal of the effort was to evaluate the data needs, methodologies, and outcomes - including consequences and/or changes in investment or operational decisions due to avoided consequences - based on resilience analysis in a range of existing tools. In particular, we selected five models originally built at NREL to explore non-resilience energy research questions to implement and exercise resilience metrics and analysis approaches. To demonstrate the importance of perspective, we selected models that represent different segments of the energy sector, geographic scales, and modeling approaches. A second important aspect of our effort was the development of generalized power interruption scenarios. These scenarios were intended to help establish a framework for simulating the effects of real-world threats in terms of their impacts on system components and, in turn, power interruption.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DLR Calculation Engine Interoperability Profile: A Project for the TOGETs Task Force
This document contains a detailed interoperability profile outlining how the components of the CIM can be used to implement DLR in support of the two use cases identified in the Use Case Document. An interoperability profile is defined by the Smart Electric Power Alliance (SEPA) as a document that “specifies standards-based requirements for interfaces or applications that are accepted by the user-community, testing authorities, and other stakeholders. It specifies configuration options within a standard’s requirements sufficient to deliver the desired level of interoperability and functionality.” To that end, this document is not in itself a standard, nor has it been approved by any formal body. Rather, it is an example of how to implement the CIM standard and has been reviewed by industry, including transmission operators and DLR vendors.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Neptunium mononitride as a target material for Pu-238 production
Deep space exploration requires specialized sources for both thermal and power applications. Radioactive decay heat of plutonium-238 (238Pu) provides these sources in the form of radioisotope thermoelectric generators (RTGs). The 238 Pu is produced via neutron capture reaction involving neptunium-237 ( 237 Np) target material. Continual optimization of 237 Np target materials and evaluation of potential alternative targets for production of 238 Pu RTGs are advantageous for meeting ongoing space power system resource requirements. Current production of 238 Pu for RTGs for the United States space program utilizes neptunium dioxide ( 237 NpO 2 ) targets; however, the use of neptunium mononitride ( 237 NpN) presents an opportunity to increase the mass of 237 Np per target compared to the dioxide form, as well as increase the thermal conductivity of the target. To assess the viability of a 237 NpN target material, the material chemistry must be thoroughly evaluated, including synthesis methods and dissolution and reprocessing schemes. This review presents a summary of synthesis pathways for 237 NpN based on published literature on actinide mononitrides. Specific literature on 237 NpN is limited, necessitating evaluation of other actinide systems to gather parallels. This suggests a need for additional experimental studies on 237 NpN. A particular limitation in the existing literature is a lack of information on the differences in material characteristics, such as morphology, particle size, and trace chemical impurities, as a function of synthesis method. These parameters may affect subsequent reactor performance or dissolution of irradiated targets. The evaluation of existing literature is presented with a focus on the efficacy of 237 NpN targets for 238 Pu production.
38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
Transforming Energy through Computational Science: Computing for Clean Energy
This fact sheet discusses the opportunity space for NREL's computational science capabilities to address national clean energy objectives. Achieving a carbon-free power sector by 2035 as a step towards a decarbonized U.S. energy economy in 2050 will require major advances in power generation, autonomous energy systems, transportation, and buildings/communities. Development of integrated modeling approaches for complex energy systems will be essential for deployment. Success requires developments in optimization and control theory, complemented by machine learning (ML) and artificial intelligence (AI), all of which in turn need targeted investments in breadth and scale of computing. This document defines an opportunity space where: embracing computing can link established research and development (R&D) to a decarbonization agenda; pursuing emerging approaches can accelerate the pace of technology advancement across the portfolio; and leading by example could reduce the carbon footprint of computing worldwide.
advanced computing↗
Dynamic Simulation of a Sub-Critical Coal Fired Power Plant
In order to address the demanding operating conditions for remaining coal-fired power plants, a dynamic model and a suite of tools have been developed for studying load cycling and to find optimization opportunities. A sub-critical steam cycle power plant was modeled in a flow-sheet modeling tool, APROS™. The model represented the firing system, economizer, evaporator, superheat, and reheat systems. Four loads from 100% TMCR to 25% TMCR were calibrated and tested such that low-to-high cycling could be studied. The model was run through various load cycles; one of which is presented here. This modeling is a prototype for general use in developing cutting edge controls products and for maximizing economic, low-emissions, and efficient operation of the existing coal power fleet.
Braun, Timothy↗
Use Probabilistic Forecasts in Reliable and Economic Electricity Market Scheduling and Operations
The value of probabilistic forecasts in electricity market operations is being increasingly recognized lately, however, the use is still limited. This study demonstrates two cases of using probabilistic forecasts in scheduling and operations of the California electricity market. We first develop a data-driven method to give weather-informed estimates of ramping reserves based on short-term probabilistic solar irradiance forecasts. By using forecasts across multiple sites, our proposed method presents advantages over the real-world baseline in terms of system reliability and economics. Our second case uses probabilistic hydro power forecasts in the scheduling of a hydro power plant under economic and environmental constraints. By optimizing the bids into both the day-ahead and real-time markets, our results demonstrate considerable economic benefits.
13 HYDRO ENERGY↗
Machine Learning for Fairness-Aware Load Shedding: A Real-Time Solution via Identifying Binding Constraints: Preprint
Timely and effective load shedding in power systems is critical for maintaining supply-demand balance and preventing cascading blackouts. To eliminate load shedding bias against specific regions in the system, optimization-based methods are uniquely positioned to help balance between economic and fairness considerations. However, the resulting optimization problem involves complex constraints, which can be time-consuming to solve and thus cannot meet the real-time requirements of load shedding. To tackle this challenge, in this paper we present an efficient machine learning algorithm to enable millisecond-level computation for the optimization-based load shedding problem. Numerical studies on both a 3-bus toy example and a realistic RTS-GMLC system have demonstrated the validity and efficiency of the proposed algorithm for delivering fairness-aware and real-time load shedding decisions.
97 MATHEMATICS AND COMPUTING↗
Development of Short-Term Forecasting Models Using Plant Asset Data and Feature Selection
Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 +- 0.014, 0.0026 +- 0, and 0.063 +- 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Synergistic data analytics for electromechanical oscillation in electric power systems
Accurate real-time estimation of the four electromechanical oscillation properties, i.e., dominant oscillation modes, mode shapes, participation factors, and coherent groups, is of great importance to assess and mitigate potential electromechanical oscillations in interconnected power systems. While eigenvalue analysis can realize such estimation, it requires precise linearized dynamic models and accurate parameters, which are highly difficult to obtain in practice. Data fusion-based modal estimation methods can extract the properties of electromechanical oscillations from measurement data without the power system model and parameters, but most of the time only one or two property assessments can be accomplished each time. To overcome this challenge, this paper presents a synergistic data analytics solution to characterize the dynamic behaviors of electromechanical oscillations from real-time measurement data. Here, the proposed method uses optimized variable projection, and it is capable of estimating all four electromechanical oscillation behavior properties from measured responses. Case studies are performed using the simulated measurement data of a 16-generator 68-bus test system and the field measurements collected by the PMUs deployed in the Yunnan Power Grid. The results demonstrate that the proposed synergistic data analytics solution can achieve satisfactory performance in capturing the properties of electromechanical oscillations from measurement data and exhibit strong robustness against measurement noise when compared with existing measurement-based methods.
42 ENGINEERING↗
A Field Enhancement Integration Design Featuring Misalignment Tolerance for Wireless EV Charging Using LCL Topology
This article proposes a magnetic integration design for EV wireless power transfer (WPT) systems, where the compensation and transmitting coils overlap one on top of the other to share the ferrite layer without any decoupling consideration. The magnetic field generated by both the compensation and transmitting coils are exploited to transfer power. To this end, a compensation method is proposed to enable magnetic field enhancement without any reactive power flow between the compensation and the transmitting coils, and to achieve input zero phase angle and constant current output. In addition, an efficient finite element analysis-based coil optimization algorithm is proposed to improve the coil misalignment tolerance on the horizontal plane, in which a reversely connected inner coil is used to stabilize the system output under misalignment conditions. Analytical and simulation results confirm transmission flux density enhancement and reduced leakage field characteristics of the proposed coil design. Finally, a scaled-down WPT prototype is built and tested to verify the performance and effectiveness of the design. The proposed design achieves 91.17% efficiency and misalignment tolerance up to 200 mm in any XY-direction while maintaining the power transfer and its efficiency. The results of this work provide insights into magnetic integration design of high-order compensation topologies featuring higher compactness, less ferrite usage, magnetic field enhancement, and misalignment tolerance for WPT systems.
42 ENGINEERING↗
Implementation of a feature selection algorithm in FARM to identify important state variables and time-invariant matrices
The FARM (Feasible Actuator Range Modifier) software module is a component of the RAVEN-based FORCE framework for analysis of Integrated Energy Systems (IES). FARM aids the HERON software module in the evaluation of the optimal dispatch for the different IES components. Set-point trajectories are required to meet limits on both production variables (i.e., the variables to be optimized such as the electrical power, the hydrogen production rate, etc.) and process variables tied to the service life of equipment (e.g., steam flowrate, vessel pressure, turbine firing temperature, etc.). To evaluate the feasibility of HERON generated set-points and to do so in an acceptable time, FARM employs reduced order models to represent the dynamic behavior of the systems to be dispatched. These surrogate models take the form of a linear dynamic system with sets of Linear Parameter Varying (LPV) matrices that are mapped to the system operating space. These matrices are derived from the trajectories of system state variables and system output variables during transients. The accuracy of LPV matrices depends on the selection of state variables. In previous reports, state variables were selected by adopting a complicated workflow requiring multiple software licenses and an advanced level of user expertise. In this report, a new workflow that automates the state variable selection process is presented. It significantly reduces the frequency of user interventions and does not require multiple software licenses. Each module in the new workflow is described in detail, and the input / output examples in each step of the workflow are provided. It was demonstrated that this workflow can greatly reduce the complexity of the state variable selection process, and that the updated FARM-Gamma and FARM-Delta validators can benefit from this workflow when solving the power dispatch problem of a representative IES test case. Finally, some code improvements that can further enhance the efficiency are suggested.
42 ENGINEERING↗
Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement
Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.
distributed energy resources↗
Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)
Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Development of an Optimal Variable-Pitch Controller for Floating Axial-Flow Marine Hydrokinetic Turbines
This article discusses the development of an optimal variable-pitch controller for floating, axial-flow marine turbines. Recently, OpenFAST, an open-source wind turbine modeling tool, has been extended to model marine turbines. A controller is necessary to simulate marine turbines for different load cases using OpenFAST, which greatly impacts the performance of the energy system. Previous studies have designed controllers using a linearized model of the marine turbine, which can be time-consuming and require the expertise of a control engineer. In this study, we use an automated approach that uses generic models of the marine turbine to identify the controller gains, which can expedite the process of designing a controller. Using an optimizer to identify the control system parameters can additionally improve the controller's performance. The optimal controller tuned using such an approach results in a 20% reduction in the tower-base damage equivalent loading and better tracking of the rated generator speed and power.
axial flow↗
Simulation-Based Design and Optimization of Waste Heat Recovery Systems
This fact sheet features an R&D project that will develop a modeling platform that can quantify the value of a district energy system and its potential for waste heat recovery. The new software analysis platform will evaluate and optimize district energy systems to better utilize low-temperature waste heat from nearby commercial and industrial buildings. The platform will help project developers and engineers easily quantify the potential value and cost savings of community energy systems for both producers and consumers of waste heat.
Combined heat and power, CHP, District energy, Was↗
Parallel derivative-free optimization for simulation-based design of behind-the-meter energy systems
In this work, the integrated design and dispatch of behind-the-meter or distributed resources (e.g. stationary battery storage and solar PV generation) is considered. A simulation-based framework is employed, generating high-fidelity results with closed-loop predictive control at a fine resolution, at the expense of high computational cost (several minutes to a few hours per design point). To address this challenge, parallel derivative-free design methods are considered. Four methods are compared, including state-of-the-art surrogate-based methods (Radial-Basis Functions and Gaussian processes) and sampling strategies, an evolutionary-based method, and a simple sequential grid refinement method. As a case study, two types of design problem with increasing complexity are considered, namely, the design of behind-the-meter resources (three design variables) and the inclusion of grid capacity (four design variables). The second yields a constrained design problem for which violations can only be determined after solving the computationally expensive simulation. For the three-dimensional case, all methods present a good performance, achieving a solution within 1% of the optimum after the first iteration, with the sequential grid refinement exhibiting the fastest convergence and achieving the best final objective value. This indicates that the parallel evaluation of multiple sampling points may be more important than the choice of method for small decision spaces. For the four-dimensional constrained case, the Genetic Algorithm presents the best tradeoff between performance and computational effort, while the rough objective function terrain generated by constraint violation penalties reduces the performance of surrogate-based methods. Contour plots with flat regions indicate flexibility in the optimal design and highlight the importance of characterizing the solution space.
24 POWER TRANSMISSION AND DISTRIBUTION↗