Static Grid Research Articles

Methods to Improve Dynamic System Response of Power Compensators Using Supercapacitors in Low-Voltage Ride-Through (LVRT) Conditions

In this paper, a power compensator using supercapacitors in parallel to protect grid-connected devices connected to the distributed power supply in the case of a low-voltage ride-through (LVRT) situation in designed, and a grid-connected device control method with improved responsiveness is proposed. In the LVRT situation, the distributed generation power may boost the DC_link voltage, increasing the risk of destroying grid-connected devices. To prevent this, the power compensator designed in this study absorbs active power in a fault situation and stores it in the supercapacitor to suppress the DC_link voltage rise and efficiently use the power. In addition, we propose methods to improve the response of the grid reactive power through the reactive power compensation of the power compensator in LVRT situation. To this end, the power angle (θPW) was extracted through the formula, and the reactive power command, to be compensated by the power compensator, and the reactive power command, compensated by the grid-connected devices, were calculated according to the active power value. In this way, the grid power controlled by the power compensation device and the grid-connected devices was controlled by the active/reactive power of the same power angle and analyzed mathematically. Active power control and static grid support were performed in the normal state where the reduction rate of the normal value of the grid voltage was around 10%. However, when the grid voltage dropped by 10% to 100%, the reactive power control was appropriately performed with dynamic grid support by increasing the voltage from 10% to 20% or more. We conducted a simulation of the new and renewable energy grid-connected devices using the OPAL-RT-based Hardware-in-the Loop Simulation (HILS) system to control the proposed active/reactive power.

GeoDIN - Geoscience-Based Deep Interaction Networks for Predicting Flow Dynamics in Reservoir Simulation Models

Summary Network graphs represent a general language for describing complex systems and a framework for knowledge discovery. Graph learning is a new concept with applications emerging in biomedicine, pharmacology, smart mobility, and physical reasoning. When applied to petroleum systems, such as reservoir models, graphs provide unique differentiators for the abstraction of reservoir connectivity to facilitate “reservoir-centric” machine learning (ML) applications. In this paper, we demonstrate, for the first time, the application of geoscience-based deep interaction networks (GeoDIN) to learn complex physics relationships from 3D reservoir models for fast and accurate prediction of subsurface spatio-temporal flow dynamics. We build the network graph with embedded subsurface and physics representations and train the ML model to “act like the reservoir simulator.” We use a simulation benchmark model for two-phase incompressible flow, with approximately 1.1 million grid size, one central injector, and four corner producers. Static 3D grid properties include porosity and permeability. We use full-physics simulation output to construct the interaction network (IN) graph, where graph nodes objects (nodes) represent reservoir grid cells. We embed the feature vector combining pore, oil and water volumes, and pressure and relative permeability. The graph objects representing wells are connected with well completion factors. The producing wells have embedded oil and water production rates, while the objects representing injecting wells have embedded water injection rates. We represent graph relations (edges) with bidirectional transmissibility of the source cell. To preprocess the data for ML, we scale the graph object attributes using “min-max” normalization and we normalize the graph relation attributes using Box-Cox transformation. We train the GeoDIN framework to predict oil and water saturation dynamics in space and time. When benchmarked with full-physics simulation, the INs ran on two V100 graphics processing units and substantially accelerated the prediction phase compared to the physics-based simulator running on 70 Intel Xeon E5 CPU cores. On average, the error in GeoDIN predicted spatio-temporal distribution of oil saturation remains within 5% of full-physics simulation for 90% of model grid cells, while the error in water saturation remains within 2.5% of full-physics simulation. The spatio-temporal propagation of pressure is more sensitive to local embeddings of INs, which communicate on node-to-node information transfer. This results in a larger prediction error of the GeoDIN model when benchmarked to full-physics simulation. On average, the error distribution suggests that the great majority (90 to 95%) of grid cells fall within 10 to 30% error bound relative to full-physics simulation. The presented GeoDIN approach to network learning carries a game-changing potential for the prediction of subsurface flow dynamics. As the way forward, we will investigate the implementation of graph neural networks with automated feature learning, generalization, and scaleup.

Updraft dynamics and microphysics: on the added value of the cumulus thermal reference frame in simulations of aerosol–deep convection interactions

Abstract. One fundamental question about atmospheric moist convection processes that remains debated is whether, or under which conditions, a relevant variability in background aerosol concentrations may have a significant dynamical impact on convective clouds and their associated precipitation. Furthermore, current climate models must parameterize both the microphysical and the cumulus convection processes, but this is usually implemented separately, whereas in nature there is a strong coupling between them. As a first step to improve our understanding of these two problems, we investigate how aerosol concentrations modify key properties of updrafts in eight large-eddy-permitting regional simulations of a case study of scattered convection over Houston, Texas, in which convection is explicitly simulated and microphysical processes are parameterized. Dynamical and liquid-phase microphysical responses are investigated using the following two different reference frames: static cloudy updraft grid cells versus tracked cumulus thermals. In both frameworks, we observe the expected microphysical responses to higher aerosol concentrations, such as higher cloud number concentrations and lower rain number concentrations. In terms of the dynamical responses, both frameworks indicate weak impacts of varying aerosol concentrations relative to the noise between simulations over the observationally derived range of aerosol variability for this case study. On the other hand, results suggest that thermals are more selective than cloudy updraft grid cells in terms of sampling the most active convective air masses. For instance, vertical velocity from thermals is significantly higher at upper levels than when sampled from cloudy updraft grid points, and several microphysical variables have higher average values in the cumulus thermal framework than in the cloudy updraft framework. In addition, the thermal analysis is seen to add rich quantitative information about the rates and covariability of microphysical processes spatially and throughout tracked thermal lifecycles, which can serve as a stronger foundation for improving subgrid-scale parameterizations.

Parallel space-time adaptive numerical simulation of 3D cardiac electrophysiology

The bidomain equations form the state-of-the-art model of cardiac electrophysiology and describe normal or pathological propagation of the excitation wave through cardiac tissue. If based on mechanistic cell models and applied to anatomically realistic geometries, their discretization requires very fine resolutions both in space and time and renders the numerical solution an extremely challenging computational problem. In this paper, we address this challenge by combining space-time adaptive discretization with dynamic load balancing for parallel computing. We use multilevel finite element methods for the spatial adaptation of the mesh, embedded Rosenbrock time integration for an adaptive choice of step sizes, and non-overlapping domain decomposition for the parallelization. In order to further reduce the computational costs, we exploit the sparsity of local matrices during the assembly of global FEM matrices which ensure an optimal usage of memory. In numerical tests, we demonstrate the feasibility of our approach that a substantial reduction factor of computing time is achieved w.r.t the static grid computation and a good parallel efficiency is achieved.

Mixed Mesh Finite Volume Method for 1D Hyperbolic Systems with Application to Plug-Flow Heat Exchangers

We present a finite volume method formulated on a mixed Eulerian-Lagrangian mesh for highly advective 1D hyperbolic systems altogether with its application to plug-flow heat exchanger modeling/simulation. Advection of sharp moving fronts is an important problem in fluid dynamics, and even a simple transport equation cannot be solved precisely by having a finite number of nodes/elements/volumes. Finite volume methods are known to introduce numerical diffusion, and there exist a wide variety of schemes to minimize its occurrence; the most recent being adaptive grid methods such as moving mesh methods or adaptive mesh refinement methods. We present a solution method for a class of hyperbolic systems with one nonzero time-dependent characteristic velocity. This property allows us to rigorously define a finite volume method on a grid that is continuously moving by the characteristic velocity (Lagrangian grid) along a static Eulerian grid. The advective flux of the flowing field is, by this approach, removed from cell-to-cell interactions, and the ability to advect sharp fronts is therefore enhanced. The price to pay is a fixed velocity-dependent time sampling and a time delay in the solution. For these reasons, the method is best suited for systems with a dominating advection component. We illustrate the method’s properties on an illustrative advection-decay equation example and a 1D plug flow heat exchanger. Such heat exchanger model can then serve as a convection-accurate dynamic model in estimation and control algorithms for which it was developed.

A numerical CFD simulation method using static grid based on momentum compensation for screw pumps

Three-dimensional CFD numerical simulation is the most powerful tool for performance analysis of screw machines. This paper compares and analyzes two CFD numerical calculation methods based on moving and static grid, and proposes a static grid calculation method based on momentum compensation, which takes into account the wall velocity and fluid momentum on the rotor surface when the screw rotor rotates. That is, while the static grid is used, the rotation characteristics of the fluid domain between the rotors are considered. Compared with the commonly used static grid method, this method greatly improves the accuracy of the calculation, the calculation error of mass flow rate is reduced from 2.32% to 1.14%. At the same time, it is simple and easy to implement. It is also suitable for modelling screw machines with special-shaped rotors, which are still challenge for moving grids. The accuracy and reliability of the method are verified by comparison with the calculation results of the moving grid and the experiments.

Repairing Compressed Path Databases on Maps with Dynamic Changes

Single-agent pathfinding on grid maps can exploit online compiled knowledge produced offline and saved as a Compressed Path Database (CPD). Such a knowledge is distilled by performing repeated searches in a graph, where each node corresponds to a distinct grid cell, typically by algorithms such as Dijkstra's. All-pairs shortest paths (APSPs) are computed and the first move along a shortest path is persistently stored in the CPD. This way, an optimal move can efficiently be retrieved for any pair of source and target cells that is considered while the agent is navigating. However, a CPD supports a static grid, that is, a grid where each cell is permanently either traversable or non-traversable. Our work instead assumes that the cells in the map can undergo dynamic changes. Reasoning about the altered map would require a new CPD. As creating it from scratch is computationally expensive, we present techniques to repair an existing CPD. We prove that using our technique leads to correct and optimal solutions. Experiments demonstrate the benefits of our approach. When a single obstacle of a given size is added or removed, the repair costs often are a small fraction of a recomputation from scratch.

Design of power quality enhanced sustainable bidirectional electric vehicle charging station in distribution grid

Integration of electric vehicle charging stations with the distribution grid rises the grid current harmonics. In this article, a sustainable bidirectional electric vehicle charging station in the distribution grid is designed. The quality of the charging station is improved by employing an adaptive neuro-fuzzy controlled distribution static compensator. The proposed sustainable bidirectional electric vehicle charging station comprises a 3-phase bidirectional ac-dc converter, bidirectional chopper, distribution static compensator and a lithium-ion battery. The bidirectional converters facilitate power flow from grid to vehicle and vice-versa. The decoupled current and dc voltage control method has been implemented for a 3-phase bidirectional converter. A multi-step current control strategy is exploited to control the bidirectional chopper. Distribution static compensator alleviates the grid current harmonics. The proposed system performance is evaluated with two kinds of controllers such as proportional – integral and adaptive neuro-fuzzy controller based on the distribution static compensator dc-side voltage and grid current harmonics. The proposed system has been modelled in MATLAB/Simulink and validated through software in the loop setup comprising of dSPACE (DS1202) hardware. The validated results show that the adaptive neuro-fuzzy controller provides better performance.

Numerical Simulation of the Effect of Wall-Equiaxed Crystal Density on the Number of Columnar Crystals and the Thickness of an Equiaxed Crystal Layer for Al-4.7%Cu Alloy Ingot Based on 3D LBM-CA Method

In this paper, the lattice Boltzmann–cellular automata (LBM-CA) model with dynamic and static grids was used to study the growth of three-dimensional (3D) multidendrites under directional solidification with random preferred angles. In the static grid, the temperature field, flow field, and solute field during solidification were calculated by the LBM method, and in the dynamic grid, each dendrite evolution was calculated based on the CA method at its preferential crystallographic orientation. The coupling of LBM and CA was made by interpolation of the correlation quantities between the two sets of grids. The effects of wall-equiaxed crystal density on the number of columnar crystals and the thickness of the equiaxed crystal layer were studied by this model. The results showed that the density of the wall-equiaxed crystal has little effect on the number of columnar crystals and the thickness of the equiaxed crystal layer. When other conditions were the same, the lower the undercooling, the fewer the columnar crystals, and the thicker the equiaxed layer. In addition, the smaller the heat transfer coefficient, the lower the number of columnar grains, and the smaller the thickness of equiaxed grains.

Risk management assessment of mobile crowds: The case of the Black Nazarene procession in Manila, Philippines

The Philippines is a highly Christianized country where religious events, such as the Black Nazarene procession, are celebrated on a massive scale. The annual procession, characterized by devotees pushing the carriage of the Black Nazarene, in the city of Manila attracts thousands of devotees. The 2019 procession took 21.35 h to traverse a 6.94-km route at an average speed of 0.33 km per hour. The extreme size and density of attending crowds make injuries and fatalities a common occurrence. Despite the usual risks that similar events entail, limited studies have been conducted to characterize and evaluate big crowds. This study developed methods and policies that aim to improve risk management planning for the event. Using video graphics, a static grid analysis was performed to systematically evaluate and capture actual participants' density and speed. The aggressiveness, density, and group formation of devotees at varying road widths were found to significantly influence the procession's movement throughout its duration; at least 56 pedestrians are needed to push the carriage from behind for smoother forward movement. Finally, proper allocation of human resources by local government departments involved in event management was recommended to potentially minimize the incidence of related injuries and deaths.

SWARM: Adaptive Load Balancing in Distributed Streaming Systems for Big Spatial Data

The proliferation of GPS-enabled devices has led to the development of numerous location-based services. These services need to process massive amounts of streamed spatial data in real-time. The current scale of spatial data cannot be handled using centralized systems. This has led to the development of distributed spatial streaming systems. Existing systems are using static spatial partitioning to distribute the workload. In contrast, the real-time streamed spatial data follows non-uniform spatial distributions that are continuously changing over time. Distributed spatial streaming systems need to react to the changes in the distribution of spatial data and queries. This article introduces SWARM, a lightweight adaptivity protocol that continuously monitors the data and query workloads across the distributed processes of the spatial data streaming system and redistributes and rebalances the workloads as soon as performance bottlenecks get detected. SWARM is able to handle multiple query-execution and data-persistence models. A distributed streaming system can directly use SWARM to adaptively rebalance the system’s workload among its machines with minimal changes to the original code of the underlying spatial application. Extensive experimental evaluation using real and synthetic datasets illustrate that, on average, SWARM achieves 2 improvement in throughput over a static grid partitioning that is determined based on observing a limited history of the data and query workloads. Moreover, SWARM reduces execution latency on average 4 compared with the other technique.

Crowd Estimation of the Black Nazarene Procession in Manila, Philippines

Developments in crowd counting and estimation are currently in demand brought about by the increasing population and corresponding improvements in public safety planning. Various crowd counting methodologies are successful in addressing challenges and limitations, such as occlusions and wide density variations. However, none have tackled the risky dynamic scenario of the annual Black Nazarene Procession in Manila City, Philippines. Extreme densities are reached as participants follow a moving subject. Yearly reported crowd estimates vary greatly as estimation methods used for this event remain undisclosed, undefined, or unpublished. Considering the strengths of both detection-based and regression-based crowd counting methods, a novel pedestrian estimation method is proposed to appropriately provide an accurate pedestrian estimate. Using video graphics, a static grid analysis is performed to systematically capture and evaluate actual participant density. From the recorded pedestrian densities, functions were developed to estimate densities at varying distances ahead and behind the carriage. A pedestrian joining density function was established to account for devotees who merge with the crowd way ahead of the procession. The 2019 event involved a moving carriage faithfully followed by thousands of devotees as it traveled along a 6.94-km route for 21.35 hours. Employing a 95% confidence interval for the density function intercepts and coefficients, 176,086 to 484,215 active pedestrian devotees during the procession were estimated using the developed systematic pedestrian estimation method. The social value of a more accurate crowd estimation method lies in providing policymakers with reliable crowd estimates that will enable the authorities to deploy the appropriate number of security personnel for crowd management, as well as medical staff for emergency situations during mass gatherings of similar nature to the Black Nazarene Procession.

Adaptive Online-Learning Volt-Var Control for Smart Inverters Using Deep Reinforcement Learning

The increasing penetration of the power grid with renewable distributed generation causes significant voltage fluctuations. Providing reactive power helps balancing the voltage in the grid. This paper proposes a novel adaptive volt-var control algorithm on the basis of deep reinforcement learning. The learning agent is an online-learning deep deterministic policy gradient that is applicable under real-time conditions in smart inverters for reactive power management. The algorithm only uses input data from the grid connection point of the inverter itself; thus, no additional communication devices are needed and it can be applied individually to any inverter in the grid. The proposed volt-var control is successfully simulated at various grid connection points in a 21-bus low-voltage distribution test feeder. The resulting voltage behavior is analyzed and a systematic voltage reduction is observed both in a static grid environment and a dynamic environment. The proposed algorithm enables flexible adaption to changing environments through continuous exploration during the learning process and, thus, contributes to a decentralized, automated voltage control in future power grids.

Static grid carbon factors – Can we do better?

The average carbon intensity (gCO2e/kWh) of electricity provided by the UK National Grid is decreasing and becoming more time variable. This paper reviews the impact on energy calculations of using various levels of data resolution (half hourly, daily, monthly and annual) and of moving to region specific data. This analysis is in two parts, one focused on the potential impact on Part L assessments and the other on reported carbon emissions for existing buildings. Analysis demonstrated that an increase in calculated emissions of up to 12% is possible when using an emissions calculation methodology employing higher resolution grid carbon intensity data. Regional analysis indicated an even larger calculation discrepancy, with some regions annual emissions increasing by a factor of ten as compared to other regions. This paper proposes a path forward for the industry to improve the accuracy of analysis by using better data sources. The proposed change in calculation methodology is analogous to moving from using an annual average external temperature to using a CIBSE weather profile for a specific city or using a future weather file. Practical application: This paper aims to quantify the inaccuracy of a calculation methodology in common use in the industry and key to building regulations (specifically Building Regulations Part L – Conservation of Fuel and Power) – translating electricity consumption into carbon emissions. It proposes an alternative methodology which improves the accuracy of the calculation based on improved data inputs.

Harmonic analysis of AC/DC hybrid microgrid

A large number of nonlinear components exist in AC/DC hybrid microgrid, which will cause harmonic pollution and affect power quality. In this paper, according to the diversity, complexity, nonlinear and mutation characteristics of harmonics, the harmonic analysis of AC/DC hybrid microgrid is carried out by adding harmonic sources. The model diagram of AC/DC hybrid microgrid is built, and the mathematical parameters of harmonics are introduced. Finally, the simulation of microgrid model is constructed based on ETAP, and the harmonic characteristics of AC/DC hybrid microgrid are studied in three cases of static load and equivalent grid using the same harmonic source.

Static Grid Equivalent Models Based on Artificial Neural Networks

Static Grid Equivalent Models Based on Artificial Neural Networks

An Adaptive Hybrid Beamforming Approach for 5G-MIMO mmWave Wireless Cellular Networks

Hardware complexity reduction is a key concept towards the design and implementation of next generation broadband wireless networks. To this end, the goal of the study presented in this paper is to evaluate the performance of an adaptive hybrid analog-digital beamforming approach in fifth-generation (5G) massive multiple input multiple output (MIMO) millimeter wave (mmWave) wireless cellular orientations. In this context, generated beams are formed dynamically according to traffic demands, via an on-off analog activation of radiating elements per vertical antenna array, in order to serve active users requesting high data rate services without requiring any expensive and mechanical complex steering antenna system. Each vertical array, which constitutes a radiating element of a circular array configuration, has a dedicated radio frequency chain (digital part). The performance of our proposed approach is evaluated statistically, by executing a sufficient number of independent Monte Carlo simulations per MIMO configuration, via a developed system-level simulator incorporating the latest 5G-3GPP channel model. According to the presented results, the adaptive beamforming approach can improve various key performance indicators (KPIs) of the wireless orientation, such as total downlink transmission power and blocking probability. In particular, when studying/analyzing a MIMO configuration with 15 vertical antenna arrays and 10 radiating elements per array, then, depending on the tolerable amount of transmission overhead, the proposed adaptive algorithm can significantly reduce the number of active radiating antenna elements compared to the static grid of beams case. In the same context, when keeping the number of radiating elements constant, then the total downlink transmission power as well as the blocking probability can be significantly reduced. It is important to note that all the KPIs have been extracted when deploying the developed array configuration in complex cellular orientations (two tiers of cells around the central cell).

Improving image quality in fast, time-resolved micro-CT by weighted back projection

Time-resolved micro-CT is an increasingly powerful technique for studying dynamic processes in materials and structures. However, it is still difficult to study very fast processes with this technique, since fast scanning is typically associated with high image noise levels. We present weighted back projection, a technique applicable in iterative reconstruction methods using two types of prior knowledge: (1) a virtual starting volume resembling the sample, for example obtained from a scan before the dynamic process was initiated, and (2) knowledge on which regions in the sample are more likely to undergo the dynamic process. Therefore, processes on which this technique is applicable are preferably occurring within a static grid. Weighted back projection has the ability to handle small errors in the prior knowledge, while similar 4D micro-CT techniques require the prior knowledge to be exactly correct. It incorporates the prior knowledge within the reconstruction by using a weight volume, representing for each voxel its probability of undergoing the dynamic process. Qualitative analysis on a sparse subset of projection data from a real micro-CT experiment indicates that this method requires significantly fewer projection angles to converge to a correct volume. This can lead to an improved temporal resolution.

The effects of turbulence on the lean blowout mechanisms of bluff-body flames

The lean blowout mechanisms of premixed bluff-body flames are experimentally investigated at various turbulence intensities. Turbulence levels are varied using a novel turbulence generator, which combines static grid and fluidic jet impingement techniques. Three different turbulence levels are probed to study their effects on lean blowout. The three conditions span across the combustion regime diagram, from flamelets to broken reactions. For all three turbulence levels, the lean blowout process is induced through controlled fuel flow rate reduction. The transient blowout process is captured using three simultaneous high-speed diagnostic systems: particle image velocimetry (PIV), stereoscopic PIV (SPIV), and C2*/CH* species measurements. The two PIV systems are used to resolve the instantaneous velocity and vorticity fields, and the C2*/CH* species diagnostics allow for global equivalence ratios to be evaluated throughout the duration of blowout. The results show that the dynamics of lean blowout vary with turbulence intensity. At low turbulence levels, the flame experiences a global effect where the flame boundary interacts with the shear layer vorticity. This imparts high strain rates along the length of the flame, leading to blowout. As turbulence levels increase, the blowout mechanism becomes less dependent on flame and shear layer interactions and more driven by flame–turbulence interactions. At high turbulence conditions, flame-eddy interactions within the freestream augment flame stretching via increased flame straining and small-scale flame corrugations. Increased flame stretching disrupts the flame stabilization process, and ultimately results in blowout.

Development of methods for optimal assembly orificing design and application to a standing-wave Breed-and-Burn reactor

This work investigates different orificing strategies in sodium fast reactors and their application to a reference standing-wave Breed-and-Burn (B&B) core. Through analytic means, the standard method of static integral orificing is shown to be infeasible in the reference core. Using simplified physical models, a novel Mixed-Integer Linear Programming (MILP) formulation is proposed to design whole-core assembly-level orificing schemes according to constraints on various operating conditions, including peak and average temperatures, mass flowrates, flow velocities, and outlet temperature profiles. After highlighting the major assumptions employed and quantifying their impacts, the MILP formulation is used to assess two alternative methods of assembly orificing, namely static grid plate and variable orificing. These two methods of orificing are found to be suitable to B&B cores, and their sensitivities to various constraints are examined. Ultimately the use of variable orificing is found to be the most desirable from the perspectives of flexibility and ability to cope with difficult constraints, although it may be the most difficult of the three methods to implement in a realistic core design.