scholarly journals Data-Driven EV Load Profiles Generation Using a Variational Auto-Encoder

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 849 ◽  
Author(s):  
Zhixin Pan ◽  
Jianming Wang ◽  
Wenlong Liao ◽  
Haiwen Chen ◽  
Dong Yuan ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Random Number ◽  
Temporal Correlation ◽  
Distribution Networks ◽  
Data Driven ◽  
Power Load ◽  
Data Driven Approach ◽  
Set Up ◽  
Charging Stations ◽  
Ev Charging

Although the penetration of electric vehicles (EVs) in distribution networks can improve the energy saving and emission reduction effects, its random and uncertain nature limits the ability of distribution networks to accept the load of EVs. To this end, establishing a load profile model of EV charging stations accurately and reasonably is of great significance to the planning, operation and scheduling of power system. Traditional generation methods for EV load profiles rely too much on experience, and need to set up a power load probability distribution in advance. In this paper, we propose a data-driven approach for load profiles of EV generation using a variational automatic encoder. Firstly, an encoder composed of deep convolution networks and a decoder composed of transposed convolution networks are trained using the original load profiles. Then, the new load profiles are obtained by decoding the random number which obeys a normal distribution. The simulation results show that EV load profiles generated by the deep convolution variational auto-encoder can not only retain the temporal correlation and probability distribution nature of the original load profiles, but also have a good restorative effect on the time distribution and fluctuation nature of the original power load.

scholarly journals A Data-Driven Approach for Optimizing the EV Charging Stations Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 118572-118592
Author(s):  
Yu Yang ◽  
Yongku Zhang ◽  
Xiangfu Meng
Keyword(s):  
Data Driven ◽  
Data Driven Approach ◽  
Charging Stations ◽  
Ev Charging

scholarly journals Controllability Evaluation of EV Charging Infrastructure Transformed from Gas Stations in Distribution Networks with Renewables

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1577
Author(s):  
Shuang Gao ◽  
Jianzhong Wu ◽  
Bin Xu
Keyword(s):  
Power Distribution ◽  
Queuing Theory ◽  
Distribution Networks ◽  
Temporal And Spatial Distribution ◽  
Charging Infrastructure ◽  
Optimal Dispatch ◽  
Gas Stations ◽  
Charging Stations ◽  
The Impact ◽  
Ev Charging

A considerable market share of electric vehicles (EVs) is expected in the near future, which leads to a transformation from gas stations to EV charging infrastructure for automobiles. EV charging stations will be integrated with the power grid to replace the fuel consumption at the gas stations for the same mobile needs. In order to evaluate the impact on distribution networks and the controllability of the charging load, the temporal and spatial distribution of the charging power is calculated by establishing mapping the relation between gas stations and charging facilities. Firstly, the arrival and parking period is quantified by applying queuing theory and defining membership function between EVs to parking lots. Secondly, the operational model of charging stations connected to the power distribution network is formulated, and the control variables and their boundaries are identified. Thirdly, an optimal control algorithm is proposed, which combines the configuration of charging stations and charging power regulation during the parking period of each individual EV. A two-stage hybrid optimization algorithm is developed to solve the reliability constrained optimal dispatch problem for EVs, with an EV aggregator installed at each charging station. Simulation results validate the proposed method in evaluating the controllability of EV charging infrastructure and the synergy effects between EV and renewable integration.

scholarly journals A data-driven approach for online aggregated load modeling through intelligent terminals

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771982599 ◽  
Author(s):  
Yi Tang ◽  
Liangliang Zhu ◽  
Jia Ning ◽  
Qi Wang
Keyword(s):  
Power System ◽  
Induction Motor ◽  
Power Distribution ◽  
Distribution Networks ◽  
Data Driven ◽  
Load Modeling ◽  
Load Model ◽  
Power Models ◽  
Data Driven Approach ◽  
Aggregated Load

Load model has significant impact on power system simulation. Current load modeling approaches are inadequate on revealing the accuracy and time-variation of load compositions. The application of wireless sensors dispersed in power distribution networks provides further opportunities for load modeling. In this article, a data-driven online aggregated load modeling approach is proposed systematically. First, all the electricity consumers are clustered according to big data of power consumption behaviors. In each cluster, typical users are designated to stand for the characteristics of the cluster, and intrusive measurement is adapted to capture these typical users’ time-varying information by employing wireless intelligent terminals, which can identify the composition of static load and induction motor load online. Second, the load models of other users in each cluster are assumed identical to typical users, including static impedance–current–power models and induction motor models. Finally, the composite load model is achieved by hierarchical aggregation and bottom-to-up stepwise equivalence. Simulations demonstrate that the load model built by proposed approach reflects higher accuracy and adaptability in power system.

scholarly journals Leak Localization in Water Distribution Networks Using Pressure and Data-Driven Classifier Approach

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 54 ◽  
Author(s):  
Congcong Sun ◽  
Benjamí Parellada ◽  
Vicenç Puig ◽  
Gabriela Cembrano
Keyword(s):  
Water Distribution ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Data Driven ◽  
Time Step ◽  
Water Losses ◽  
Localization Accuracy ◽  
Linear Discriminant ◽  
Data Driven Approach ◽  
Leak Localization

Leaks in water distribution networks (WDNs) are one of the main reasons for water loss during fluid transportation. Considering the worldwide problem of water scarcity, added to the challenges that a growing population brings, minimizing water losses through leak detection and localization, timely and efficiently using advanced techniques is an urgent humanitarian need. There are numerous methods being used to localize water leaks in WDNs through constructing hydraulic models or analyzing flow/pressure deviations between the observed data and the estimated values. However, from the application perspective, it is very practical to implement an approach which does not rely too much on measurements and complex models with reasonable computation demand. Under this context, this paper presents a novel method for leak localization which uses a data-driven approach based on limit pressure measurements in WDNs with two stages included: (1) Two different machine learning classifiers based on linear discriminant analysis (LDA) and neural networks (NNET) are developed to determine the probabilities of each node having a leak inside a WDN; (2) Bayesian temporal reasoning is applied afterwards to rescale the probabilities of each possible leak location at each time step after a leak is detected, with the aim of improving the localization accuracy. As an initial illustration, the hypothetical benchmark Hanoi district metered area (DMA) is used as the case study to test the performance of the proposed approach. Using the fitting accuracy and average topological distance (ATD) as performance indicators, the preliminary results reaches more than 80% accuracy in the best cases.

scholarly journals An Analysis of Electromagnetic Disturbances from an Electric Vehicle Charging Station

Energies ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 244
Author(s):  
Paweł Mazurek ◽  
Aleksander Chudy
Keyword(s):  
Power Quality ◽  
Electric Energy ◽  
Distribution Networks ◽  
Quality Parameters ◽  
Voltage Profile ◽  
Current Harmonics ◽  
Charging Station ◽  
Charging Stations ◽  
Electromagnetic Disturbances ◽  
Ev Charging

The electric vehicles (EVs) could potentially have a significant impact on power quality parameters and distribution networks as they are non-linear loads and their charging might result in tremendous power demand. When connected to the utility grid, a large number of EV charging stations from different manufacturers might create significant harmonic current emissions, impact the voltage profile, and eventually affect the power quality. Nevertheless, practical examples of disturbances from charging stations have not been made public. This paper aims to clarify the characteristics of conductive disturbances and levels of current harmonics generated by charging station and their severity on the quality of electric energy. The analysis was based on tests of a prototype station of an EV charging station integrated with a LED street light. The tests concern the determination of current harmonics and the values of conductive electromagnetic disturbances in the 150 kHz–30 MHz range. The test results of the prototype charger with observed exceedances of current harmonics (25th–39th range) and conducted interference exceedances are comprehensively described. After applying filtering circuits to the final version of the station, retesting in an accredited laboratory showed qualitative compliance.

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