Probability Evaluation of Excess Voltage in a Distribution Network with Uneven Charging Electric Vehicle Load

Chen, Wei; Zhang, Lei; Pei, Xiping

doi:10.1007/s42835-021-00839-w

Cited by 8 publications

(4 citation statements)

References 25 publications

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“…In this regard, the structure of LSTM cells is presented with its unrolled architecture, as shown in Figure 2. Furthermore, the calculation procedure at each time step of LSTM block is presented as the following formulations given in Equations ( 4)- (9).…”

Section: The Lstm Modelmentioning

confidence: 99%

“…Furthermore, the uncoordinated deployment of EV charging stations and the boost in charging power demand could impact the reliability of power grids. Many researchers have regarded the EV charging power demand as an additional burden on power grids [5][6][7][8][9][10]. They have focused on the adverse effect of EV charging power demand in the grids.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

Chang

Bae

Cha³

et al. 2021

Sustainability

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With the widespread use of electric vehicles, their charging power demand has increased and become a significant burden on power grids. The uncoordinated deployment of electric vehicle charging stations and the uncertainty surrounding charging behaviors can cause harmful impacts on power grids. The charging power demand during the fast charging process especially is severely fluctuating, because its charging duration is short and the rated power of the fast chargers is high. This paper presents a methodology to analyze and forecast the aggregated charging power demand from multiple fast-charging stations. Then, pattern of fast-charging power demand is analyzed to identify its irregular trend with the distribution of peak time and values. The forecasting model, based on long short-term memory neural network, is proposed in this paper to address the fluctuating of fast-charging power demand. The forecasting performance of the proposed model is validated in comparison with other deep learning approaches, using real-world datasets measured from fast-charging stations in Jeju Island, South Korea. The results show that the proposed model outperforms forecasting fast-charging power demand aggregated by multiple charging stations.

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Section: The Lstm Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

Chang

Bae

Cha³

et al. 2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…Considering the disadvantages of MCS with more samples and long calculation time, LHS is presented. LHS is used to simulate the EV charging loads, and the EV relevant parameters obey the general probability distribution [70]. In [71], a combination of LHS and K-means is used to model the uncertain scenarios of EV charging loads, and the EV relevant parameters obey a truncated Gaussian distribution.…”

Section: Radial Basis Function [45]mentioning

confidence: 99%

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Zhang

et al. 2022

Prot Control Mod Power Syst

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The development of distributed renewable energy, such as photovoltaic power and wind power generation, makes the energy system cleaner, and is of great significance in reducing carbon emissions. However, weather can affect distributed renewable energy power generation, and the uncertainty of output brings challenges to uncertainty planning for distributed renewable energy. Energy systems with high penetration of distributed renewable energy involve the high-dimensional, nonlinear dynamics of large-scale complex systems, and the optimal solution of the uncertainty model is a difficult problem. From the perspective of statistical machine learning, the theory of planning of distributed renewable energy systems under uncertainty is reviewed and some key technologies are put forward for applying advanced artificial intelligence to distributed renewable power uncertainty planning.

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“…As we all know, the power distribution system is a dynamic network, and the operation state of the power distribution network changes due to uncertain factors such as frequent load changes, fault trips, and equipment maintenance [3,4]. Under the planning of carbon peaking and carbon neutralization, more and more distributed energy sources are connected [5], which makes the distribution network larger, more complex in structure and diversified in operation modes. The operation mode of distribution network changes frequently, which brings new challenges to topology dynamic sensing and real-time identification.…”

Section: Introductionmentioning

confidence: 99%

Research on Topology Dynamic Perception and Line Parameter Identification of Distribution Network

Dai

et al. 2023

Preprint

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The absence of network topology and line parameter information data affects the condition monitoring, analysis and control of active distribution networks. In order to solve this problem, this paper proposes a method to identify the topology and line parameters of distribution network using the measured values obtained by intelligent meters and phasor measurement units. The initial model of topology and line parameter identification based on linear regression is solved by orthogonal triangular decomposition to obtain the initial values of topology and line, and the improved Newton Raphson (NR) is used to accurately identify the network topology and parameters. The proposed method is validated on IEEE 33 bus test system.

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Probability Evaluation of Excess Voltage in a Distribution Network with Uneven Charging Electric Vehicle Load

Cited by 8 publications

References 25 publications

Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

Aggregated Electric Vehicle Fast-Charging Power Demand Analysis and Forecast Based on LSTM Neural Network

Planning of distributed renewable energy systems under uncertainty based on statistical machine learning

Research on Topology Dynamic Perception and Line Parameter Identification of Distribution Network

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