Investigating the Impact of Electric Vehicles on the Voltage Profile of Distribution Networks

Soofi, Arash Farokhi; Manshadi, Saeed D.

doi:10.1109/isgt50606.2022.10041251

Cited by 7 publications

(3 citation statements)

References 17 publications

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“…These initial values typically are obtained using real-time measurements, historical data, or power system simulations. On the other side, the voltage magnitudes and phase angles in (5) are the predicted values after training the neural network models for a certain number of epochs.…”

Section: A Neural Network Modelsmentioning

confidence: 99%

“…The output layer consists of a set of nodes, each of which represents a measurement in the output vector X o in (5). That is, each of the output nodes is designed to predict a specific measurement (voltage magnitude, voltage phase angle, current magnitude, current phase angle, or line power loss).…”

Section: Radial Basis Function Network (Rbfn) Known As Kernel Functio...mentioning

confidence: 99%

“…PF is extensively used for electrical network analysis and prediction, potential problem identification, determination of the steady-state conditions of the systems, and optimizing the performance of the power grids [1], [2], [3], [4]. The resource mix of distribution systems has been changing due to the increasing penetration of distributed energy resources (DERs) such as wind turbines, solar panels, and energy storage devices [5], [6]. This transformation necessitates the development of new PF calculation methods to accurately model the uncertainties of these DERs and ensure an efficient and safe operation of the power grid.…”

Section: Introductionmentioning

confidence: 99%

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Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids

Tiwari,

Zideh,

Talreja

et al. 2024

IEEE Access

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Most power systems' approaches are currently tending towards stochastic and probabilistic methods due to the high variability of renewable sources and the stochastic nature of loads. Conventional power flow (PF) approaches such as forward-backward sweep (FBS) and Newton-Raphson require a high number of iterations to solve non-linear PF equations making them computationally very intensive. PF is the most important study performed by utility, required in all stages of the power system, especially in operations and planning. This paper discusses the applications of deep learning (DL) to predict PF solutions for three-phase unbalanced power distribution grids. Three deep neural networks (DNNs); Radial Basis Function Network (RBFnet), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN), are proposed in this paper to predict PF solutions. The strength of the proposed DNN models over the traditional iterative-based PF solvers is that these models can capture the nonlinear relationships in PF calculations to accurately predict the solutions. The PF problem is formulated as a multi-output regression model where two or more output values are predicted based on the inputs. The training and testing data are generated through the OpenDSS-MATLAB COM interface. These methods are completely data-driven where the training relies on reducing the mismatch at each node without the need for the knowledge of the system. The novelty of the proposed methodology is that the models can accurately predict the PF solutions for the unbalanced distribution grids with mutual coupling and are robust to different R/X ratios, topology changes as well as generation and load variability introduced by the integration of distributed energy resources (DERs) and electric vehicles (EVs). To test the efficacy of the DNN models, they are applied to IEEE 4-node and 123-node test cases, and the American Electric Power (AEP) feeder model. The PF results for RBFnet, MLP, and CNN models are discussed in this paper which demonstrate that all three DNN models provide highly accurate results in predicting PF solutions.INDEX TERMS Power flow analysis, deep neural networks, radial basis function networks, multi-layer perceptron, convolutional neural networks, unbalanced power distribution grids.The associate editor coordinating the review of this manuscript and approving it for publication was Ehab Elsayed Elattar .

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Section: A Neural Network Modelsmentioning

confidence: 99%

Section: Radial Basis Function Network (Rbfn) Known As Kernel Functio...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids

Tiwari,

Zideh,

Talreja

et al. 2024

IEEE Access

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Study on the impact of disorganized charging of scaled electric vehicles connected to the distribution network

Li,

Shu,

Xie

et al. 2024

J. Phys.: Conf. Ser.

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Aiming at the stochasticity of electric vehicle user charging, an electric vehicle time distribution model is established. Based on the IEEE33 distribution system, the Monte Carlo method is used to simulate the scaled electric vehicle disordered charging access to the distribution network and to study the change of the total system network loss and the mean value of the voltage specification when charging random loads access to different nodes. Next, the three-phase unbalanced metrics are observed at the EV access nodes. The results show that the charging load access to different distribution network nodes has an obvious impact on power quality. Finally, further EV grid optimization measures are given.

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Training A Deep Reinforcement Learning Agent for Microgrid Control using PSCAD Environment

Soofi

Bayani

Yazdanibiouki

et al. 2023

2023 IEEE PES Grid Edge Technologies Conference &Amp; Exposition (Grid Edge)

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Investigating the Impact of Electric Vehicles on the Voltage Profile of Distribution Networks

Cited by 7 publications

References 17 publications

Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids

Power Flow Analysis Using Deep Neural Networks in Three-Phase Unbalanced Smart Distribution Grids

Study on the impact of disorganized charging of scaled electric vehicles connected to the distribution network

Training A Deep Reinforcement Learning Agent for Microgrid Control using PSCAD Environment

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