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GridLAB-D is an agent? based distribution system simulation environment that allows fine-grained end-user models, including geospatial and network topology detail. GridLAB-D addresses the lack of runtime interaction by designing a flexible communication interface, Bus.py (pronounced bus-dot-pie), that uses Python to pass messages between one or more GridLAB-D instances and a Smart Grid simulator.
We present a ray tracing code to compute integrated cosmological observables on the fly in AMR N-body simulations. Unlike conventional ray tracing techniques, our code takes full advantage of the time and spatial resolution attained by the N-body simulation by computing the integrals along the line of sight on a cell-by-cell basis through the AMR simulation grid. Moroever, since it runs on the fly in the N-body run, our code can produce maps of the desired observables without storing large (or any) amounts of data for post-processing. We implemented our routines in the RAMSES N-body code and tested the implementation using an example of weak lensing simulation. We analyse basic statistics of lensing convergence maps and find good agreement with semi-analytical methods. The ray tracing methodology presented here can be used in several cosmological analysis such as Sunyaev-Zel'dovich and integrated Sachs-Wolfe effect studies as well as modified gravity. Our code can also be used in cross-checks of the more conventional methods, which can be important in tests of theory systematics in preparation for upcoming large scale structure surveys.
The Smart Grid is characterized by the proliferation of advanced digital controllers at all levels of its operational hierarchy from generation to end consumption. Such controllers within modern residential and commercial buildings enable grid operators to exercise fine-grained control over energy consumption through several emerging Buildings-to-Grid (B2G) applications. Though this capability promises significant benefits in terms of operational economics and improved reliability, cybersecurity weaknesses in the supporting infrastructure could be exploited to cause a detrimental effect and this necessitates focused research efforts on two fronts. First, the understanding of how cyber attacks in the B2G space could impact grid reliability and to what extent. Second, the development and validation of cyber-physical application-specific countermeasures that are complementary to traditional infrastructure cybersecurity mechanisms for enhanced cyber attack detection and mitigation. The PNNL B2G testbed is currently being developed to address these core research needs. Specifically, the B2G testbed combines high-fidelity buildings+grid simulators, industry-grade building automation and Supervisory Control and Data Acquisition (SCADA) systems in an integrated, realistic, and reconfigurable environment capable of supporting attack-impact-detection-mitigation experimentation. In this paper, we articulate the need for research testbeds to model various B2G applications broadly by looking at the end-to-end operational hierarchy of the Smart Grid. Finally, the paper not only describes the architecture of the B2G testbed in detail, but also addresses the broad spectrum of B2G resilience research it is capable of supporting based on the smart grid operational hierarchy identified earlier.
The electric grid is a highly complex, interconnected machine, and changing one part of the grid can have consequences elsewhere. Adding wind and solar affects the operation of the other power plants and adding high penetrations can induce cycling of fossil-fueled generators. Cycling leads to wear-and-tear costs and changes in emissions. Phase 2 of the Western Wind and Solar Integration Study(WWSIS-2) evaluated these costs and emissions and simulated grid operations for a year to investigate the detailed impact of wind and solar on the fossil-fueled fleet. This built on Phase 1, one of the largest wind and solar integration studies ever conducted, which examined operational impacts of high wind and solar penetrations in the West.
The electric grid is a highly complex, interconnected machine, and changing one part of the grid can have consequences elsewhere. Adding wind and solar affects the operation of the other power plants and adding high penetrations can induce cycling of fossil-fueled generators. Cycling leads to wear-and-tear costs and changes in emissions. Phase 2 of the Western Wind and Solar Integration Study (WWSIS-2) evaluated these costs and emissions and simulated grid operations for a year to investigate the detailed impact of wind and solar on the fossil-fueled fleet. This built on Phase 1, one of the largest wind and solar integration studies ever conducted, which examined operational impacts of high wind and solar penetrations in the West(GE Energy 2010).
The electric grid is a highly complex, interconnected machine, and changing one part of the grid can have consequences elsewhere. Adding wind and solar affects the operation of the other power plants and adding high penetrations can induce cycling of fossil-fueled generators. Cycling leads to wear-and-tear costs and changes in emissions. Phase 2 of the Western Wind and Solar Integration Study (WWSIS-2) evaluated these costs and emissions and simulated grid operations for a year to investigate the detailed impact of wind and solar on the fossil-fueled fleet. This built on Phase 1, one of the largest wind and solar integration studies ever conducted, which examined operational impacts of high wind and solar penetrations in the West(GE Energy 2010).
Dynamic, scalable, and interoperable control paradigms are required to enable efficient, secure, reliable, and resilient distribution grid operations with widespread grid integration of renewable energy resources. This paper presents our recent research on developing a novel, holistic, data-enhanced hierarchical control architecture that addresses the formidable challenges faced by emerging distribution grids with increasing penetrations of distributed energy resources. The proposed architecture integrates centralized monitoring and control with distributed grid-edge control and thus effectively deals with multi-spatiotemporal dynamics existing in the grid. Simulation results are provided to demonstrate the effectiveness of the proposed control architecture.
Dynamic, scalable and interoperable control paradigm is required to enable efficient, secure, reliable and resilient distribution grid operations with widespread grid integration of renewable energy resources. This paper presents our recent research work on developing a novel, holistic, data enhanced hierarchical control architecture that addresses the formidable challenges faced by emerging distribution grids with increasing penetrations of distributed energy resources. The proposed architecture integrates centralized monitoring and control together with distributed grid edge control, and thus effectively deals with multi-spatiotemporal dynamics existing in the grid. Simulation results are provided to demonstrate the effectiveness of the proposed control architecture.
Sub-optical-cycle dynamics of dense electron bunches in relativistic-intensity laser–solid interactions lead to the emission of high-order harmonics and attosecond light pulses. The capacity of particle-in-cell simulations to accurately model these dynamics is essential for the prediction of emission properties because the attosecond pulse intensity depends on the electron density distribution at the time of emission and on the temporal distribution of individual electron Lorentz-factors in an emitting electron bunch. Here, we show that in one-dimensional collisionless simulations, the peak density of the emitting electron bunch increases with the increase in the spatial resolution of the simulation grid. When collisions are added to the model, the peak electron density becomes independent of the spatial resolution. Collisions are shown to increase the spread of the peaks of Lorentz-factors of emitting electrons in time, especially in the regimes far from optimum generation conditions, thus leading to lower intensities of attosecond pulses as compared to those obtained in collisionless simulations.
The evolution of the power grid has brought increasing deployment of advance metering infrastructure, penetration of intelligent electronic devices, and integration of physical power system components with information and communications technologies. With the fast-expanding connectivity, cyber vulnerabilities arise due to the use of internet-based communication systems. These systems are targets of cyber-intrusions which attempt to disturb the normal power system functions. Traditional intrusion detection algorithms have been developed without an explicit model of the cyber components. In this paper, an algorithm to detect false data injections in the power system is proposed considering both cyber and physical models of the power system. The algorithm is based on an Adaptive Neuro Fuzzy Inference System (ANFIS) which collects information from state variables of the cyber-physical system to meet the performance requirements of the grid. Simulations of the proposed approach using the IEEE 13-bus test system validate the effectiveness of this artificial intelligence-based algorithm.
We perform particle-in-cell simulations of perpendicular nonrelativistic collisionless shocks to study electron heating and pre-acceleration for parameters that permit the extrapolation to the conditions at young supernova remnants. Our high-resolution large-scale numerical experiments sample a representative portion of the shock surface and demonstrate that the efficiency of electron injection is strongly modulated with the phase of the shock reformation. For plasmas with low and moderate temperature (plasma beta β{sub p}=5⋅10{sup −4} and β{sub p}=0.5), we explore the nonlinear shock structure and electron pre-acceleration for various orientations of the large-scale magnetic field with respect to the simulation plane, while keeping it at 90° to the shock normal. Ion reflection off of the shock leads to the formation of magnetic filaments in the shock ramp, resulting from Weibel-type instabilities, and electrostatic Buneman modes in the shock foot. In all of the cases under study, the latter provides first-stage electron energization through the shock-surfing acceleration mechanism. The subsequent energization strongly depends on the field orientation and proceeds through adiabatic or second-order Fermi acceleration processes for configurations with the out-of-plane and in-plane field components, respectively. For strictly out-of-plane field, the fraction of suprathermal electrons is much higher than for other configurations, because only in this case are the Buneman modes fully captured by the 2D simulation grid. Shocks in plasma with moderate β{sub p} provide more efficient pre-acceleration. The relevance of our results to the physics of fully 3D systems is discussed.
Battery energy storage systems (BESS), due to their tremendous range of uses and configurations, may assist PV integration in any number of ways by increasing power system flexibility. In this paper we describe results of a research project conducted by NREL and First Solar for developing controls and demonstrating many use cases for PV-BESS systems including (1) matching generation to loads through time shifting, reducing PV curtailment,; (2) promoting higher levels of PV penetrations by balancing the grid through ancillary services; (3) use of PV-BESS systems to provide wide-area stability services in the form of oscillations damping controls; and (r) use of gird forming BESS for enabling black start and islanded applications for PV-BESS systems. In the paper we will also describe the unique multi-MW test and validation platform developed at NREL for conducting research on fielded utility-scale PV-BESS systems. The platform consists of First Solar 0.43 MW PV power plant, NREL’s 1MW/1MW BESS, 2MW PV power supplies and 7MVA power electronic grid simulator interfaced with industrial plant controllers and power-hardware-in-the-loop (PHIL) capability.
Grid operators are increasingly turning to advanced grid-support functions in distributed energy resources (DER) to assist with distribution circuit voltage regulation, bulk system frequency control, and power system protection. The U.S. DER certification standard, Underwriters Laboratories (UL) 1741, was revised in September 2016 to add test procedures for multiple grid-support functions. Sandia National Laboratories, SunSpec Alliance, and growing community of collaborators have undertaken a multiyear effort to create an open-source system validation platform (SVP) that automates DER interconnection and interoperability test procedures by communicating with grid simulators, photovoltaic (PV) simulators, data acquisition systems, and interoperable equipment under test. However, the power hardware required for generating the test conditions may be untenable for many organizations. In this paper, we discuss development of the SVP testing capabilities for UL 1741 tests utilizing a controller hardware-in-the-loop testbed that precludes the need for power hardware using a 34.5 kW Austrian Institute of Technology smart grid controller. Finally, analysis of normal ramp rate, soft start ramp rate, specified power factor, volt-VAr, and frequency-watt advanced grid functions, and the effectiveness of the UL 1741 test protocols are included.
The fast computation which is critical for immersive engagement with and learning from energy simulations would be furthered by developing a general method for creating rapidly computed simplified versions of NREL's computation-intensive energy simulations. Created using machine learning techniques, these 'reduced form' simulations can provide statistically sound estimates of the results of the full simulations at a fraction of the computational cost with response times - typically less than one minute of wall-clock time - suitable for real-time human-in-the-loop design and analysis. Additionally, uncertainty quantification techniques can document the accuracy of the approximate models and their domain of validity. Approximation methods are applicable to a wide range of computational models, including supply-chain models, electric power grid simulations, and building models. These reduced-form representations cannot replace or re-implement existing simulations, but instead supplement them by enabling rapid scenario design and quality assurance for large sets of simulations. We present an overview of the framework and methods we have implemented for developing these reduced-form representations.
We investigate models for the photoionization of the widespread diffuse ionized gas (DIG) in galaxies. In particular, we address the long standing question of the penetration of Lyman continuum photons from sources close to the galactic midplane to large heights in the galactic halo. We find that recent hydrodynamical simulations of a supernova-driven interstellar medium (ISM) have low-density paths and voids that allow for ionizing photons from midplane OB stars to reach and ionize gas many kiloparsecs above the midplane. We find that ionizing fluxes throughout our simulation grids are larger than predicted by one-dimensional slab models, thus allowing for photoionization by O stars of low altitude neutral clouds in the Galaxy that are also detected in H{alpha}. In previous studies of such clouds, the photoionization scenario had been rejected and the H{alpha} had been attributed to enhanced cosmic ray ionization or scattered light from midplane H II regions. We do find that the emission measure distributions in our simulations are wider than those derived from H{alpha} observations in the Milky Way. In addition, the horizontally averaged height dependence of the gas density in the hydrodynamical models is lower than inferred in the Galaxy. These discrepancies are likely due to the absence of magnetic fields in the hydrodynamic simulations and we discuss how magnetohydrodynamic effects may reconcile models and observations. Nevertheless, we anticipate that the inclusion of magnetic fields in the dynamical simulations will not alter our primary finding that midplane OB stars are capable of producing high-altitude DIG in a realistic three-dimensional ISM.
Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial progress in developing parameterization (or closure) models for subgrid scale (SGS) processes using physical insights and mathematical approximations, they remain imperfect and can lead to inaccurate predictions. In recent years, machine learning has been successful in extracting complex patterns from high-resolution spatio-temporal data, leading to improved parameterization models, and ultimately better coarse grid prediction. However, the inability to satisfy known physics and poor generalization hinders the application of these models for real-world problems. In this work, we put forth a frame invariant closure approach to improve the accuracy and generalizability of deep learning-based subgrid scale closure models by embedding physical symmetries directly into the structure of the neural network. Specifically, we utilized specialized layers within the convolutional neural network in such a way that desired constraints are theoretically guaranteed without the need for any regularization terms. We demonstrate our framework for a two-dimensional decaying turbulence test case mostly characterized by the forward enstrophy cascade. We show that our frame invariant SGS model (i) accurately predicts the subgrid scale source term, (ii) respects the physical symmetries such as translation, Galilean, and rotation invariance, and (iii) is numerically stable when implemented in coarse-grid simulation with generalization to different initial conditions and Reynolds number. This work opens up a possibility of connecting physics-based theories and data-driven modeling paradigms, and thus represents a promising step towards the development of physically consistent data-driven turbulence closure models.
An interface with the OpenDSS distribution grid simulator, facilitating users in calibrating and validating models. Additionally, three distinct tools have been developed: PV Hosting Capacity: This tool allows users to determine the additional amount of photovoltaic (PV) generation that can be integrated into the system without breaching operational constraints. EV Hosting Capacity: This tool focuses on identifying the capacity for incorporating extra electric vehicle (EV) load into the system without surpassing operational limitations. Project Impact Analysis Tool: This tool is designed to assess and report all potential grid violations associated with a specific project, providing valuable insights into its impact on the distribution grid.
A near real-time physical transportation network routing system comprising: a traffic simulation computing grid and a dynamic traffic routing service computing grid. The traffic simulator produces traffic network travel time predictions for a physical transportation network using a traffic simulation model and common input data. The physical transportation network is divided into a multiple sections. Each section has a primary zone and a buffer zone. The traffic simulation computing grid includes multiple of traffic simulation computing nodes. The common input data includes static network characteristics, an origin-destination data table, dynamic traffic information data and historical traffic data. The dynamic traffic routing service computing grid includes multiple dynamic traffic routing computing nodes and generates traffic route(s) using the traffic network travel time predictions.