Grid Simulation Research Articles

The Networked Energy Systems Emulation Center at the German Aerospace Center DLR – bridging the gap between digital simulation and real operation of energy grids

Abstract This paper describes the Networked Energy Systems Emulation Center – DLR-NESTEC – a platform for research on power grids of the future. The DLR-NESTEC consists of a large number of networked power electronic components with which real hardware can be emulated using a real-time simulation system. The grid networking is realized via cable emulators. The laboratory works with real physical power flows and has a connected load of 800 kVA. In addition to the emulators, real network components can be integrated into the laboratory. The sector coupling is addressed by the coupling of charging infrastructure of electric cars as well as the integration of electricity-controlled heating systems. In addition, hydrogen technologies can be integrated. The laboratory is controlled by a SCADA system coupled to high-resolution measurement equipment. With the help of DLR-NESTEC, various future questions regarding robust and safe operation sector-coupled energy networks can be addressed – for instance the operation of a grid structure with a high share of controllable loads without a superordinate control.

Climate adaptive rice planting strategies diverge across environmental gradients in the Indo-Gangetic Plains

Abstract The timing of rice planting has a profound influence on the productivity of the rice-wheat cropping pattern in the Indo-Gangetic Plains (IGP), a system that provides the foundation for food security in South Asia. Nevertheless, strategies for adaptive rice planting in a rapidly changing climate are not well established. In this ex-ante analysis, regional gridded crop model simulations are deployed to investigate the impact of different rice planting strategies on system level productivity, resilience, and environmental benefits. Our results suggest that synchronizing rice planting dates with the monsoon onset substantially outperforms farmer practice (+41%) and static state recommendations in the Eastern IGP. However, planting long-duration rice with the monsoon onset is ineffective in the Northwestern IGP since the later arrival of the monsoon increases the probability of cold damage to rice and terminal heat stress in wheat. Here, fixed planting dates (+12.5%) or planting medium duration varieties at monsoon onset (+18%) performed best. We conclude that resilient and productive rice planting strategies must account for interannual weather variability and divergent climate conditions across sub-regions in the IGP.

Secure Data Transmission and Trustworthiness Judgement Approaches Against Cyber-Physical Attacks in an Integrated Data-Driven Framework

Threats of cyberattacks have penetrated from disclosing critical user information to destroying/manipulating industrial control systems. Study on data security during network transmission has raised increasing attention in the systems and control community, which is found very necessary and timely in the context of Industry 4.0. In most existing approaches, the protection of the transmitted data from eavesdropping attacks and the detection of malicious integrity attacks are usually carried out separately. In this study, an integrated data-driven framework applicable at the control level is proposed to deal with secure transmission and attack detection simultaneously. In the framework, a secure correlation-based encryption/decryption approach and a trustworthiness judgement approach are proposed. Comprehensive discussions are made regarding the analysis of the sensitivity to attacks, the introduced time delay, and the design degree-of-free. Executable algorithms are presented, corresponding to which hardware is modularized and can work standalone independent from the configuration of the monitoring and control systems or any third-party authentication agencies. Evaluation results on a simulated two-area frequency-load control power grid system are provided to show the effectiveness and performance of the proposed approaches.

Electromechanical transient simulation model of low frequency flexible transmission system based on modular multilevel matrix converter

Low frequency AC transmission has broad prospects in the future power system. How to establish its electromechanical transient simulation model and provide basic research means for its adaptability analysis after connecting to the large power grid is an urgent problem to be solved. Based on the existing electromechanical transient simulation methods, the mathematical model of modular multilevel matrix converter (M3C) is simplified, and an electromechanical transient simulation model composed of virtual DC circuit and equivalent AC circuit is proposed. On this basis, the power and voltage controller is designed according to the power decoupling characteristics of M3C. Further considering the operation requirements of the offshore wind power transmission system through low frequency AC transmission, an island control is designed to make M3C have the ability of passive control. Numerical examples verify the effectiveness of the proposed simulation model, which can effectively respond to short-circuit faults and power loss. This model can better meet the calculation requirements of large power grid simulation.

A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks

Large sets of matter density simulations are becoming increasingly important in large-scale structure cosmology. Matter power spectra emulators, such as the Euclid Emulator and CosmicEmu, are trained on simulations to correct the non-linear part of the power spectrum. Map-based analyses retrieve additional non-Gaussian information from the density field, whether through human-designed statistics such as peak counts, or via machine learning methods such as convolutional neural networks. The simulations required for these methods are very resource-intensive, both in terms of computing time and storage. This creates a computational bottleneck for future cosmological analyses, as well as an entry barrier for testing new, innovative ideas in the area of cosmological information retrieval. Map-level density field emulators, based on deep generative models, have recently been proposed to address these challenges. In this work, we present a novel mass map emulator of the KiDS-1000 survey footprint, which generates noise-free spherical maps in a fraction of a second. It takes a set of cosmological parameters (Ω M , σ 8) as input and produces a consistent set of 5 maps, corresponding to the KiDS-1000 tomographic redshift bins. To construct the emulator, we use a conditional generative adversarial network architecture and the spherical convolutional neural network DeepSphere, and train it on N-body-simulated mass maps. We compare its performance using an array of quantitative comparison metrics: angular power spectra Cℓ , pixel/peaks distributions, Cℓ correlation matrices, and Structural Similarity Index. Overall, the average agreement on these summary statistics is <10% for the cosmologies at the centre of the simulation grid, and degrades slightly on grid edges. However, the quality of the generated maps is worse at high negative κ values or large scale, which can significantly affect summaries sensitive to such observables. Finally, we perform a mock cosmological parameter estimation using the emulator and the original simulation set. We find good agreement in these constraints, for both likelihood and likelihood-free approaches. The emulator is available at tfhub.dev/cosmo-group-ethz/models/kids-cgan.

Investigating the Possibilities for Simulation of the Interconnected Electric Power and Communication Infrastructures

In recent years, we have seen an increasing dependency between different urban critical infrastructures, which raises the demands on their analysis and cybersecurity. Current open-source solutions do not provide simulations of interconnected data networks and power grids, which are essential for analysing of both infrastructures weak parts and reducing the risk of emerging threats. The main purpose of this paper is to describe the design of a simulation platform that provides this type of simulation to customers using the virtual user interface. The paper describes the development of a complex model of interconnected power and data infrastructures. Created virtualization platform that consists of several open-source tools is described in detail. Outputs of this paper can be used for analysis of critical infrastructures at the level of urban networks.

Comparative Impact of Three Practical Electric Vehicle Charging Scheduling Schemes on Low Voltage Distribution Grids

This paper benchmarks the performance of three practical electric vehicle (EV) charging scheduling methods relative to uncontrolled charging (UNC) in low-voltage (LV) distribution grids. The charging methods compared are the voltage droop method (VDM), price-signal-based method (PSM) and average rate method (ARM). Trade-offs associated with the grid performance, charging demand fulfilment and economic benefits are explored for three different grid types and four increasing levels of EV penetration for summer and winter. This study was carried out using grid simulations of six existing Dutch distribution grids, and the EV charging demand was generated based on 1.5 M EV charging sessions; therefore, the findings of this research are relevant for actual case studies. The results suggest that the PSM can be a preferred strategy for achieving a charging cost reduction of 6–11% when the grid performance is not a bottleneck for the given EV penetration. However, it can lead to an increased peak loading of the grid under certain operational conditions, resulting in a charging energy deficiency ratio of 4–8%. The VDM should be preferred if user information on the parking time and energy demand is not consistently available, and if the mitigation of grid congestion is critical. However, both unfinished charging events and charging costs increase with the VDM. The ARM provides the best balance in the trade-offs associated with the mitigation of grid congestion and price reduction, as well as charging completion. This research provides a perception of how to select the most appropriate practical charging strategy based on the given system requirements. The outcome of this study can also serve as a benchmark for advanced smart charging algorithm evaluation in the future.

Near-ultraviolet continuum modeling of the 1985 April 12 great flare of AD Leo

White-light stellar flares are now reported by the thousands in long-baseline, high-precision, broad-band photometry from missions like Kepler, K2, and TESS. These observations are crucial inputs for assessments of biosignatures in exoplanetary atmospheres and surface ultraviolet radiation dosages for habitable-zone planets around low-mass stars. A limitation of these assessments, however, is the lack of near-ultraviolet spectral observations of stellar flares. To motivate further empirical investigation, we use a grid of radiative-hydrodynamic simulations with an updated treatment of the pressure broadening of hydrogen lines to predict the λ ≈ 1800 − 3300 Å continuum flux during the rise and peak phases of a well-studied superflare from the dM3e star AD Leo. These predictions are based on semi-empirical superpositions of radiative flux spectra consisting of a high-flux electron beam simulation with a large, low-energy cutoff (≳ 85 keV) and a lower-flux electron beam simulation with a smaller, low-energy cutoff (≲ 40 keV). The two-component models comprehensively explain the hydrogen Balmer line broadening, the optical continuum color temperature, the Balmer jump strength, and the far-ultraviolet continuum strength and shape in the rise/peak phase of this flare. We use spatially resolved analyses of solar flare data from the Interface Region Imaging Spectrograph, combined with the results of previous radiative-hydrodynamic modeling of the 2014 March 29 X1 solar flare (SOL20140329T17:48), to interpret the two-component electron beam model as representing the spatial superposition of bright kernels and fainter ribbons over a larger area.

Simplified Inverter-Based Generation Model for Protection and Short-Term Stability Studies in Electromagnetic Transient Resolution

The new dynamics of inverter-based generation (IBG) and the lack of accurate models jeopardize the safety of power systems. Additionally, most of the detailed models for electromagnetic transients (EMT) from manufacturers are black boxes. For this reason, the objective of this work was to propose a generic IBG model in EMT resolution suitable for short-circuit, protections, and short-term voltage stability studies. For this purpose, the model considered different functionalities, such as synchronization in case of unbalanced voltage sags, flexible negative sequence current injection, peak current limitation, dynamic reactive power control, and fault ride-through (FRT). This model allowed grid operators to meet the challenge of performing accurate simulations with high integration of renewable resources in their energy matrix. The model was also based on a voltage-controlled current source in the ATP/EMTP software. The short-circuit response was evaluated in a simulation scenario for different fault events presenting a reliable behavior due to the consideration of technical and regulatory requirements and constraints of IBGs. Finally, the proposed model has a fast initialization and is suitable for simulations with large time steps, making it valuable for EMT simulations in large grids.

Electron tracks simulation in water: Performance comparison between GPU CPU and the EUMED grid installation

PurposeWe explored different technologies to minimize simulation time of the Monte-Carlo method for track generation following the Geant4-DNA processes for electrons in water. MethodsA GPU software tool is developed for electron track simulations. A similar CPU version is also developed using the same collision models. CPU simulations were carried out on a single user desktop computer and on the computing grid France Grilles using 10 and 100 computing nodes. Computing time results for CPU, GPU, and grid simulations are compared with those using Geant4-DNA processes. ResultsThe CPU simulations better performs when the number of electrons is less than 104 with 100 eV initial energy, this number decreases as the energy increases. The GPU simulations gives better results when the number of electrons is more than 104 with initial energy of 100 eV, this number decreases to 103 for electrons with 10KeV and increases back with higher energy. The use of the grid introduces an additional queuing time which slows down the overall simulation performance. Thus, the Grid gives better performance when the number of electrons is over 105 with initial energy of 10KeV, and this number decreases as the energy increases. ConclusionsThe CPU is best suited for small numbers of primary incident electrons. The GPU is best suited when the number of primary incident particles occupies sufficient resources on GPU card in order to get an important computing power. The grid is best suited for simulations with high number of primary incident electrons with high initial energy.

Distributed utility‐based real‐time power flow optimization in ICT‐enabled low voltage distribution grids

AbstractLow‐Voltage (LV) distribution grids are facing a rapid increase of connected Photovoltaic (PV) power plants as well as flexible consumers like Battery‐Electric Vehicle (BEV) chargers and Heat Pumps (HPs). The coordinated operation of these generation, storage and consumption units, referred to as Distributed Energy Resources (DERs), is regarded as a key requirement to maximize the benefits of renewable generation without violating, for example, voltage limits. Therefore, an operation management scheme was proposed in previous work that optimizes the power flows in LV grids in real‐time, where optimality is expressed as a maximization of the utility that DER owners experience from power consumption or injection, respectively. In this contribution, this method is extended by: (1) detailing a time‐varying utility‐model to express customer needs, (2) introducing a distributed implementation enhancing the robustness to failures, (3) developing a testbed using a real‐time digital power grid simulator and a communication network emulator, and (4) integrating a real‐time information flooding protocol. The performance is evaluated in different simulation scenarios, showing that the proposed method is able to cooperatively utilize the flexible units in order to fulfil the DER owners' needs even in the event of controller failures and constrained communication.

Coordinated scheme for expansion planning of transmission system with active distribution system flexibility provision

The extensive access of distributed renewable energy at the level of distribution network makes the distribution grid have the ability of active regulation similar to the transmission grid, and the power exchange between transmission and distribution systems becomes more flexible bidirectional. In order to make full use of the complementary collaboration ability between the distribution and transmission systems, this paper proposes a flexible planning method of transmission system coordinated with distribution system for the future scene of high proportion renewable energy. This collaborative optimization framework is a double-layer structure: The upper layer goes on the expansion planning of transmission system, and the lower layer goes on the operation simulation of distribution grid. The upper-level model transfers the marginal price of nodes to the lower-level model, and the lower-level model feeds back the purchase and sale agreement to the upper model. The linear transformation of the model is deployed using piecewise linearization and big-M method. We use the strong duality theory to transform the original double problem into an equivalent single-layer form, and then the effective solution of the commercial application is realized. Aiming at the high proportion renewable energy in transmission and distribution systems, the performance of the formulated problem is validated by comparative analyses of examples.

Review of cloth modeling and simulation for virtual fitting

Virtual fitting technology is widely used in many fields, such as e-commerce, garment computer-aided design, and film and game production. Cloth modeling and simulation play an important role in virtual fitting. The realism and simulation speed directly affect the user experience and visual appearance. This paper first reviews the history of cloth modeling and simulation methods. Then, it focuses on four perspectives: yarn-structure modeling of the cloth modeling level; multi-resolution grid simulation; the relation between human posture and cloth deformation; and collision problems of the cloth simulation level. Yarn-structure modeling considers a unit cell that incorporates the physical characteristics of the fabric and the fabric structure. A multi-resolution grid can integrate techniques from different research fields and may achieve a breakthrough in cloth modeling. Generative adversarial networks (GANs) have potential for dealing with the relation between human posture and cloth deformation by comparing several typical algorithms with GANs. Solving the collision and friction problem depends on choosing the right envelope box for refining the human body surface. Finally, the paper concludes with an analysis of future research trends that could improve the fidelity and speed of a simulation. The paper may serve as a reference for research into cloth modeling and simulation for virtual fitting.

Aggregated models of active distribution networks for stability studies of large transmission systems

The paper presents a new framework to quantify the amount of dynamic grid support that should be provided from converter-based energy resources replacing generation from synchronous machines. A tuning approach for local stability issues in active distribution networks (ADN) is presented, followed by an aggregation procedure to derive adequate low-order ADN models. The resulting models from benchmark systems with varying shares of grid-forming converter-based generation up to 100% of the distribution grid load are published along with the paper. They allow an adequate representation of ADNs for the assessment of voltage and frequency performance in large-scale transmission grid simulations. A quantitative study of the full continental ENTSO-E system investigates a large disturbance in the form of a system split for multiple development paths of converters replacing synchronous machines. It is found that about 10%–20% of the new converters should have grid-support capabilities to maintain the dynamic power system performance at today’s level. • A method for tuning active distribution networks (ADN) with grid-forming converters. • An aggregation procedure for low order models of the ADN response to disturbances. • ADN dynamic model suite for transmission grid studies (published with the paper). • A quantitative study of the ENTSO-E system during a large disturbance (system split). • Recommendation for the future level of control contributions from ADN.

Load Balancing Based on Firefly and Ant Colony Optimization Algorithms for Parallel Computing.

With the wide application of computational fluid dynamics in various fields and the continuous growth of the complexity of the problem and the scale of the computational grid, large-scale parallel computing came into being and became an indispensable means to solve this problem. In the numerical simulation of multi-block grids, the mapping strategy from grid block to processor is an important factor affecting the efficiency of load balancing and communication overhead. The multi-level graph partitioning algorithm is an important algorithm that introduces graph network dynamic programming to solve the load-balancing problem. This paper proposed a firefly-ant compound optimization (FaCO) algorithm for the weighted fusion of two optimization rules of the firefly and ant colony algorithm. For the graph, results after multi-level graph partitioning are transformed into a traveling salesman problem (TSP). This algorithm is used to optimize the load distribution of the solution, and finally, the rough graph segmentation is projected to obtain the most original segmentation optimization results. Although firefly algorithm (FA) and ant colony optimization (ACO), as swarm intelligence algorithms, are widely used to solve TSP problems, for the problems for which swarm intelligence algorithms easily fall into local optimization and low search accuracy, the improvement of the FaCO algorithm adjusts the weight of iterative location selection and updates the location. Experimental results on publicly available datasets such as the Oliver30 dataset and the eil51 dataset demonstrated the effectiveness of the FaCO algorithm. It is also significantly better than the commonly used firefly algorithm and other algorithms in terms of the search results and efficiency and achieves better results in optimizing the load-balancing problem of parallel computing.

Double-layer AGC frequency regulation control method considering operating economic cost and energy storage SOC consistency

Aiming at the problem of power grid frequency regulation caused by the large-scale grid connection of new energy, this paper proposes a double-layer automatic generation control (AGC) frequency regulation control method that considers the operating economic cost and the consistency of the state of charge (SOC) of the energy storage. At the regional control level, an economically optimized dynamic frequency regulation responsibility distribution between the unit and the energy storage is realized, and the idle time of energy storage is fully used for SOC management to effectively suppress the fluctuation of the energy storage SOC. At the energy storage station level, the cooperative control algorithm is used to ensure that the output and SOC of each energy storage unit are consistent, respectively. The effectiveness of the method is verified by establishing the dynamic model of the unit-storage combined frequency regulation of the regional power grid for simulation and comparison experiments. The method proposed in this paper considers the influence of different disturbance conditions on the AGC frequency regulation responsibility distribution between the unit and the energy storage and fully uses the frequency regulation characteristics of the energy storage's rapid and flexible response. It effectively improves the service life of energy storage and the comprehensive operation efficiency of the system while optimizing the frequency regulation operation cost, improving the frequency regulation performance, and increasing the frequency regulation compensation income.

Voronoi Meshing to Accurately Capture Geological Structure in Subsurface Simulations

Mesh generation lies at the interface of geological modeling and reservoir simulation. Highly skewed or very small grid cells may be necessary to accurately capture the geometry of geological features, but the resulting poorly scaled or small grid cells can have a substantial negative impact on simulator accuracy and speed. One way to minimize numerical errors caused by gridding complex structures is to simulate on high-quality Voronoi meshes, which reduce grid orientation effects in fluid flow. This work presents a complete methodology to create Voronoi simulation grids, model fluid flow in complex geological systems, and visualize the results. A recently developed Voronoi meshing method that can automatically generate provably good unstructured meshes that conform to input surfaces creating closed volumes is used. Initially an analytical benchmark simulation is presented to validate the quality of the meshes and simulation results and demonstrate the superiority of simulation results using Voronoi meshes over flexed-hexahedral meshes on a domain with internal features. Next, meshes are created for test structures representing four of the most common geological features in the subsurface: layering, pinch-out, an interior lens that tapers to zero thickness on all sides and a fault with offset. Two benchmark flow simulations are run for each test structure. Finally, a realistic geological example for CO_2 injection into an anticline is simulated. Three realizations of the Voronoi mesh at the same resolution are generated for the simulations. Each mesh is highly refined near the injection wells and coarse in areas of less interest. These three meshes are used to model the CO_2 plume in the subsurface as it migrates to the top of the structure and then fills downward. Simulations on the meshes with randomly generated elements inside the input volumes each give slightly different fingering patterns for the viscous-unstable buoyant gas flow. The results presented in this work show a promising step towards utilizing fully automated Voronoi meshing for subsurface flow simulations in complex geology.

Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations

Binary stars undergo a variety of interactions and evolutionary phases, critical for predicting and explaining observations. Binary population synthesis with full simulation of stellar structure and evolution is computationally expensive, requiring a large number of mass-transfer sequences. The recently developed binary population synthesis code POSYDON incorporates grids of MESA binary star simulations that are interpolated to model large-scale populations of massive binaries. The traditional method of computing a high-density rectilinear grid of simulations is not scalable for higher-dimension grids, accounting for a range of metallicities, rotation, and eccentricity. We present a new active learning algorithm, psy-cris, which uses machine learning in the data-gathering process to adaptively and iteratively target simulations to run, resulting in a custom, high-performance training set. We test psy-cris on a toy problem and find the resulting training sets require fewer simulations for accurate classification and regression than either regular or randomly sampled grids. We further apply psy-cris to the target problem of building a dynamic grid of MESA simulations, and we demonstrate that, even without fine tuning, a simulation set of only ∼1/4 the size of a rectilinear grid is sufficient to achieve the same classification accuracy. We anticipate further gains when algorithmic parameters are optimized for the targeted application. We find that optimizing for classification only may lead to performance losses in regression, and vice versa. Lowering the computational cost of producing grids will enable new population synthesis codes such as POSYDON to cover more input parameters while preserving interpolation accuracies.

Do High-spin High-mass X-Ray Binaries Contribute to the Population of Merging Binary Black Holes?

Gravitational-wave observations of binary black hole (BBH) systems point to black hole spin magnitudes being relatively low. These measurements appear in tension with high spin measurements for high-mass X-ray binaries (HMXBs). We use grids of MESA simulations combined with the rapid population-synthesis code COSMIC to examine the origin of these two binary populations. It has been suggested that Case-A mass transfer while both stars are on the main sequence can form high-spin BHs in HMXBs. Assuming this formation channel, we show that depending on the critical mass ratios for the stability of mass transfer, 48%–100% of these Case-A HMXBs merge during the common-envelope phase and up to 42% result in binaries too wide to merge within a Hubble time. Both MESA and COSMIC show that high-spin HMXBs formed through Case-A mass transfer can only form merging BBHs within a small parameter space where mass transfer can lead to enough orbital shrinkage to merge within a Hubble time. We find that only up to 11% of these Case-A HMXBs result in BBH mergers, and at most 20% of BBH mergers came from Case-A HMXBs. Therefore, it is not surprising that these two spin distributions are observed to be different.

A parallel discrete unified gas kinetic scheme on unstructured grid for inviscid high-speed compressible flow simulation

The discrete unified gas kinetic scheme (DUGKS) is a recently devised approach to simulate multiscale flows based on the kinetic models, which also shows distinct features for continuum flows. Most of the existing DUGKS are sequential or based on structured grids, thus limiting their scope of application in engineering. In this paper, a parallel DUGKS for inviscid high-speed compressible flows on unstructured grids is proposed. In the framework of the DUGKS, the gradients of the distribution functions are calculated by a least-square method. To parallelize the method, a graph-based partitioning method is employed to guarantee the load balancing and minimize the communication among processors. The method is validated by several benchmark problems, i.e., a two-dimensional (2D) Riemann problem, 2D subsonic flows passing two benchmark airfoils, a 2D regular shock reflection problem, 2D supersonic flows (Mach numbers are 3 and 5) around a cylinder, an explosion in a three-dimensional (3D) box, a 3D subsonic flow around the Office National d'Etudes et de Recherches Aérospatiales M6 wing, a 3D hypersonic flow (Mach number is 10) around a hemisphere, and a supersonic flow over the Northrop YF-17 fighter model. The numerical results show good agreement with the published results, and the present method is robust for a wide range of Mach numbers, from subsonic to hypersonic. The parallel performance results show that the proposed method is highly parallel scalable, where an almost linear scalability with 93% parallel efficiency is achieved for a 3D problem with over 55 × 106 tetrahedrons on a supercomputer with up to 4800 processors.