scispace - formally typeset
Search or ask a question
Author

윤창열(Chang Yeol Yun)

Bio: 윤창열(Chang Yeol Yun) is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 28 citations.

Papers
More filters

Cited by
More filters
ReportDOI
30 Dec 2020

70 citations

Journal ArticleDOI
TL;DR: In this article, an improved modeling and analysis of the levelized cost of energy (LCOE) associated with photovoltaic (PV) power plants is presented, which considers the effective lifetime of various PV technologies rather than the usual use of the financial lifetime.

65 citations

ReportDOI
02 Jan 2020

53 citations

Journal ArticleDOI
TL;DR: A dynamic partitioning model of the multi-user multi-channel cognitive radio is used to cope with the vehicle-to-grid (V2G) communication issue and a two-stage V2G dispatch scheme is proposed for the wind-solar-EV VPP to maximize its overall daily profit.
Abstract: This article proposes an optimal coordinated scheduling of electric vehicles (EVs) for a virtual power plant (VPP) considering communication reliability. Recent advancements on wireless technologies offer flexible communication solutions with wide coverage and low-cost deployment for smart grid. Nevertheless, the imperfect communication may deteriorate the monitoring and controlling performance of distributed energy resources. An interactive approach is presented for combined optimization of dynamic spectrum allocation and EV scheduling in the VPP to coordinate charging/discharging strategies of massive and dispersed EVs. In the proposed approach, a dynamic partitioning model of the multi-user multi-channel cognitive radio is used to cope with the vehicle-to-grid (V2G) communication issue due to variable EV parking behaviors, and a two-stage V2G dispatch scheme is proposed for the wind-solar-EV VPP to maximize its overall daily profit. Furthermore, the effects of packet loss probability on the VPP scheduling performance and battery degradation cost are thoroughly analyzed and investigated. Comparative studies have been implemented to demonstrate the superior performance of the proposed methodology under various imperfect communication conditions.

41 citations

Journal ArticleDOI
TL;DR: A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy.
Abstract: Historical data offers a wealth of knowledge to the users. However, often restrictively mammoth that the information cannot be fully extracted, synthesized, and analyzed efficiently for an application such as the forecasting of variable generator outputs. Moreover, the accuracy of the prediction method is vital. Therefore, a trade-off between accuracy and efficacy is required for the data-driven energy forecasting method. It has been identified that the hybrid approach may outperform the individual technique in minimizing the error while challenging to synthesize. A hybrid deep learning-based method is proposed for the output prediction of the solar photovoltaic systems (i.e. proposed PV system) in Australia to obtain the trade-off between accuracy and efficacy. The historical dataset from 1990-2013 in Australian locations (e.g. North Queensland) are used to train the model. The model is developed using the combination of multivariate long and short-term memory (LSTM) and convolutional neural network (CNN). The proposed hybrid deep learning (LSTM-CNN) is compared with the existing neural network ensemble (NNE), random forest, statistical analysis, and artificial neural network (ANN) based techniques to assess the performance. The proposed model could be useful for generation planning and reserve estimation in power systems with high penetration of solar photovoltaics (PVs) or other renewable energy sources (RESs).

32 citations