Microgrid Day-Ahead Scheduling Considering Neural Network based Battery Degradation Model

• URL: http://arxiv.org/abs/2202.12416v1
• Date: Thu, 24 Feb 2022 23:24:52 GMT
• Title: Microgrid Day-Ahead Scheduling Considering Neural Network based Battery Degradation Model
• Authors: Cunzhi Zhao, and Xingpeng Li
• Abstract summary: Battery energy storage system (BESS) can effectively mitigate the uncertainty of renewable generation. Main causes of LiB degradation are loss of Li-preventions, loss electrolyte, battery internal degradation. We propose a neural net-work based battery degradation (NNBD) model to quantify degradation with inputs of major degradation factors.
• Score: 0.42970700836450487
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Battery energy storage system (BESS) can effectively mitigate the uncertainty of variable renewable generation. Degradation is un-preventable for batteries such as the most popular Lithium-ion battery (LiB). The main causes of LiB degradation are loss of Li-ions, loss of electrolyte, and increase of internal resistance which are hard to model and predict. In this paper, we propose a data driven method to predict the battery degradation per a given scheduled battery operational profile. Particularly, a neural net-work based battery degradation (NNBD) model is proposed to quantify the battery degradation with inputs of major battery degradation factors. When incorporating the proposed NNBD model into microgrid day-ahead scheduling (MDS), we can estab-lish a battery degradation based MDS (BDMDS) model that can consider the equivalent battery degradation cost precisely. Since the proposed NNBD model is highly non-linear and non-convex, BDMDS would be very hard to solve. To address this issue, a neural network and optimization decoupled heuristic (NNODH) algorithm is proposed in this paper to effectively solve this neural network embedded optimization problem. Simulation results demonstrate that the proposed NNODH algorithm is able to ob-tain the optimal solution with lowest total cost including normal operation cost and battery degradation cost.

Related papers

• Learning the P2D Model for Lithium-Ion Batteries with SOH Detection [0.0]
  We show that a Convolutional Neural Network (CNN) surrogate model fit to accurately simulated data from a class of random driving cycles. We demonstrate that a CNN is an ideal choice for accurately capturing Lithium ion concentration profiles.
  arXiv  Detail & Related papers  (2025-02-19T23:17:30Z)
• Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing [53.77822620185878]
  We propose a synergistic methodology to concurrently optimize perovskite memristor fabrication and develop robust analog DNNs. We develop "BayesMulti", a training strategy utilizing BO-guided noise injection to improve the resistance of analog DNNs to memristor imperfections. Our integrated approach enables use of analog computing in much deeper and wider networks, achieving up to 100-fold improvements.
  arXiv  Detail & Related papers  (2024-12-03T19:20:08Z)
• A Comparison of Baseline Models and a Transformer Network for SOC Prediction in Lithium-Ion Batteries [0.0]
  The ability of a battery management system to accurately estimate the state of charge can help alleviate this problem. The paper compares different neural network-based models and common regression models for SOC estimation. Results of various experiments conducted on data obtained from natural driving cycles of the BMW i3 battery show that the decision tree outperformed all other models.
  arXiv  Detail & Related papers  (2024-10-22T14:27:43Z)
• Battery GraphNets : Relational Learning for Lithium-ion Batteries(LiBs) Life Estimation [0.0]
  We present the Battery GraphNets framework that jointly learns to incorporate a discrete dependency graph structure between battery parameters. The proposed method outperforms several popular methods by a significant margin on publicly available battery datasets and achieves SOTA performance.
  arXiv  Detail & Related papers  (2024-08-14T15:44:56Z)
• Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing [59.40731173370976]
  We investigate the application of energy-efficient brain-inspired machine learning models for on-board radio resource management. For relevant workloads, spiking neural networks (SNNs) implemented on Loihi 2 yield higher accuracy, while reducing power consumption by more than 100$times$ as compared to the CNN-based reference platform.
  arXiv  Detail & Related papers  (2023-08-22T03:13:57Z)
• NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes [50.00272243518593]
  Deep neural networks (DNNs) have become ubiquitous in machine learning, but their energy consumption remains problematically high. We have developed NeuralFuse, a novel add-on module that handles the energy-accuracy tradeoff in low-voltage regimes. At a 1% bit-error rate, NeuralFuse can reduce access energy by up to 24% while recovering accuracy by up to 57%.
  arXiv  Detail & Related papers  (2023-06-29T11:38:22Z)
• Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery [62.997667081978825]
  We propose an enhanced version of the physics-constrained deep neural network (PCDNN) approach to provide high-accuracy voltage predictions. The ePCDNN can accurately capture the voltage response throughout the charge--discharge cycle, including the tail region of the voltage discharge curve.
  arXiv  Detail & Related papers  (2022-03-03T19:56:24Z)
• Attention-based Deep Neural Networks for Battery Discharge Capacity Forecasting [2.8944480776764308]
  The capacity degeneration can be treated as the memory of the initial battery state of charge from the data point of view. The deep degradation network (DDN) is developed with the attention mechanism to measure similarity and predict battery capacity. The DDN model can extract the degeneration-related temporal patterns from the streaming sensor data and perform the battery capacity prediction efficiently online in real-time.
  arXiv  Detail & Related papers  (2022-02-14T14:16:25Z)
• Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning [5.096724740354125]
  We consider the problem of optimal charging/discharging of a bank of heterogenous battery units, driven by electricity generation and demand processes. The batteries in the battery bank may differ with respect to their capacities, ramp constraints, losses, as well as cycling costs. We propose a linear function approximation based Q-learning algorithm for learning the optimal solution.
  arXiv  Detail & Related papers  (2021-09-15T07:51:48Z)
• Physics-informed CoKriging model of a redox flow battery [68.8204255655161]
  Redox flow batteries (RFBs) offer the capability to store large amounts of energy cheaply and efficiently. There is a need for fast and accurate models of the charge-discharge curve of a RFB to potentially improve the battery capacity and performance. We develop a multifidelity model for predicting the charge-discharge curve of a RFB.
  arXiv  Detail & Related papers  (2021-06-17T00:49:55Z)
• Universal Battery Performance and Degradation Model for Electric Aircraft [52.77024349608834]
  Design, analysis, and operation of electric vertical takeoff and landing aircraft (eVTOLs) requires fast and accurate prediction of Li-ion battery performance. We generate a battery performance and thermal behavior dataset specific to eVTOL duty cycles. We use this dataset to develop a battery performance and degradation model (Cellfit) which employs physics-informed machine learning.
  arXiv  Detail & Related papers  (2020-07-06T16:10:54Z)

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.