On Simulation of Power Systems and Microgrid Components with SystemC-AMS

• URL: http://arxiv.org/abs/2407.06217v1
• Date: Thu, 4 Jul 2024 22:42:29 GMT
• Title: On Simulation of Power Systems and Microgrid Components with SystemC-AMS
• Authors: Rahul Bhadani, Satyaki Banik, Hao Tu, Srdjan Lukic, Gabor Karsai,
• Abstract summary: Cyber-physical systems such as microgrids consist of interconnected components, localized power systems, and distributed energy resources. Power system converters and their control loops play an essential role in stabilizing grids and interfacing a microgrid with the main grid. This paper presents a faster method for simulating the electromagnetic transient response of microgrid components using SystemC-AMS.
• Score: 0.7689542442882423
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cyber-physical systems such as microgrids consist of interconnected components, localized power systems, and distributed energy resources with clearly defined electrical boundaries. They can function independently but can also work in tandem with the main grid. Power system converters and their control loops play an essential role in stabilizing grids and interfacing a microgrid with the main grid. The optimal selection of microgrid components for installation is expensive. Simulation of microgrids provides a cost-effective solution. However, when studying the electromagnetic transient response, their simulation is slow. Furthermore, software packages facilitating electromagnetic transient response may be prohibitively expensive. This paper presents a faster method for simulating the electromagnetic transient response of microgrid components using SystemC-AMS. We present a use case of a photovoltaic grid-following inverter with a phase-locked loop to track reference active and reactive power. Our results demonstrate that the simulation performed using SystemC-AMS is roughly three times faster than the benchmark simulation conducted using Simulink. Our implementation of a photovoltaic grid-following inverter equipped with a phase-locked loop for monitoring reference active and reactive power reveals that the simulation executed using SystemC-AMS is approximately three times faster than the benchmark simulation carried out using Simulink. Our implementation adopts a model-based design and produces a library of components that can be used to construct increasingly complex grid architectures. Additionally, the C-based nature allows for the integration of external libraries for added real-time capability and optimization functionality. We also present a use case for real-time simulation using a DC microgrid with a constant resistive load.

Related papers

• MICSim: A Modular Simulator for Mixed-signal Compute-in-Memory based AI Accelerator [10.65687190002229]
  This work introduces MICSim, an open-source, pre-circuit simulator designed for evaluation of chip-level software performance and hardware overhead of mixed-signal compute-in-memory (CIM) accelerators. MICSim features a modular design, allowing easy multi-level co-design and design space exploration.
  arXiv  Detail & Related papers  (2024-09-23T09:12:46Z)
• Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming [27.09017677987757]
  Multiple-input multiple-output (MIMO) systems play a key role in wireless communication technologies. We propose a power-oriented optimization algorithm for beamforming in modular hybrid systems. We show how power efficient beamforming can be encouraged by the learned, via boosting computation with low-resolution phase shifts.
  arXiv  Detail & Related papers  (2024-08-01T10:19:25Z)
• EM-GANSim: Real-time and Accurate EM Simulation Using Conditional GANs for 3D Indoor Scenes [55.2480439325792]
  We present a novel machine-learning (ML) approach (EM-GANSim) for real-time electromagnetic (EM) propagation. In practice, it can compute the signal strength in a few milliseconds on any location in 3D indoor environments.
  arXiv  Detail & Related papers  (2024-05-27T17:19:02Z)
• RigLSTM: Recurrent Independent Grid LSTM for Generalizable Sequence Learning [75.61681328968714]
  We propose recurrent independent Grid LSTM (RigLSTM) to exploit the underlying modular structure of the target task. Our model adopts cell selection, input feature selection, hidden state selection, and soft state updating to achieve a better generalization ability.
  arXiv  Detail & Related papers  (2023-11-03T07:40:06Z)
• Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN [8.980453507536017]
  Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods. We propose an alternative approach to the problem that leverages machine learning.
  arXiv  Detail & Related papers  (2023-06-23T16:45:46Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• A Dynamic Feedforward Control Strategy for Energy-efficient Building System Operation [59.56144813928478]
  In current control strategies and optimization algorithms, most of them rely on receiving information from real-time feedback. We propose an engineer-friendly control strategy framework that embeds dynamic prior knowledge from building system characteristics simultaneously for system control. We tested it in a case for heating system control with typical control strategies, which shows our framework owns a further energy-saving potential of 15%.
  arXiv  Detail & Related papers  (2023-01-23T09:07:07Z)
• Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side [6.129841305145217]
  A microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid.
  arXiv  Detail & Related papers  (2022-11-11T01:43:10Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Sim and Real: Better Together [47.14469055555684]
  We demonstrate how to learn simultaneously from both simulation and interaction with the real environment. We propose an algorithm for balancing the large number of samples from the high throughput but less accurate simulation. We analyze such multi-environment interaction theoretically, and provide convergence properties, through a novel theoretical replay buffer analysis.
  arXiv  Detail & Related papers  (2021-10-01T14:30:03Z)
• Fast and differentiable simulation of driven quantum systems [58.720142291102135]
  We introduce a semi-analytic method based on the Dyson expansion that allows us to time-evolve driven quantum systems much faster than standard numerical methods. We show results of the optimization of a two-qubit gate using transmon qubits in the circuit QED architecture.
  arXiv  Detail & Related papers  (2020-12-16T21:43:38Z)
• CSM-NN: Current Source Model Based Logic Circuit Simulation -- A Neural Network Approach [5.365198933008246]
  CSM-NN is a scalable simulation framework with optimized neural network structures and processing algorithms. Experiments show that CSM-NN reduces the simulation time by up to $6times$ compared to a state-of-the-art current source model based simulator running on a CPU. CSM-NN also provides high accuracy levels, with less than $2%$ error, compared to HSPICE.
  arXiv  Detail & Related papers  (2020-02-13T00:29:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.