/pdf/2019-cost-of-wind-energy-review-4w443c35kf.pdf

2019 Cost of Wind Energy Review

Photovoltaics energy: Improved modeling and analysis of the levelized cost of energy (LCOE) and grid parity – Egypt case study

/pdf/2018-cost-of-wind-energy-review-or5fuy3yui.pdf

2018 Cost of Wind Energy Review

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.

Optimal Coordination of Electric Vehicles for Virtual Power Plants With Dynamic Communication Spectrum Allocation

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).

/pdf/a-new-data-driven-long-term-solar-yield-analysis-model-of-4lnsavgs6x.pdf

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Papers

System Advisor Model(SAM)을 이용한 태양광발전 시스템 분석