Large-Scale Integration of Renewable Power Sources into the Vietnamese Power System

Kies, Alexander; Schyska, Bruno U.; Viet, Dinh Thanh; Bremen, Lueder von; Heinemann, Detlev; Schramm, Stefan

doi:10.1016/j.egypro.2017.08.188

Cited by 32 publications

(16 citation statements)

References 21 publications

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“…This province is undergoing revitalization in terms of more sustainable future solar energy systems, which are partly funded by the World Bank. The province also meets the criteria set for the selection of the present study location for the future installation of solar measurement stations, i.e., it is solar-rich with terrain either flat or characterised by low obstructions, homogeneous landscape and land-usage (clearly represented by satellite pixels for validation), without large water bodies, mountains, dirt roads, industrial pollution, open-pit mining operations, or a danger of flooding [102,103]. Thus, the development of a solar forecasting model at multiple forecast horizons, especially in Bac-Ninh, is a justified research endeavour to support the United Nation's Sustainable Development Goal #7 related to the access to affordable renewable energies for all populations.…”

Section: Study Regionmentioning

confidence: 99%

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

et al. 2020

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This paper aims to develop the long short-term memory (LSTM) network modelling strategy based on deep learning principles, tailored for the very short-term, near-real-time global solar radiation (GSR) forecasting. To build the prescribed LSTM model, the partial autocorrelation function is applied to the high resolution, 1 min scaled solar radiation dataset that generates statistically significant lagged predictor variables describing the antecedent behaviour of GSR. The LSTM algorithm is adopted to capture the short- and the long-term dependencies within the GSR data series patterns to accurately predict the future GSR at 1, 5, 10, 15, and 30 min forecasting horizons. This objective model is benchmarked at a solar energy resource rich study site (Bac-Ninh, Vietnam) against the competing counterpart methods employing other deep learning, a statistical model, a single hidden layer and a machine learning-based model. The LSTM model generates satisfactory predictions at multiple-time step horizons, achieving a correlation coefficient exceeding 0.90, outperforming all of the counterparts. In accordance with robust statistical metrics and visual analysis of all tested data, the study ascertains the practicality of the proposed LSTM approach to generate reliable GSR forecasts. The Diebold–Mariano statistic test also shows LSTM outperforms the counterparts in most cases. The study confirms the practical utility of LSTM in renewable energy studies, and broadly in energy-monitoring devices tailored for other energy variables (e.g., hydro and wind energy).

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Section: Study Regionmentioning

confidence: 99%

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

et al. 2020

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“…Thus, before applying the solar tracking system in any region, an intensive investigation should be carried out. To our knowledge, an extensive investigation into the fabrication and operation of solar tracking systems in Vietnam and other Association of Southeast Asian Nations (ASEAN) has been rarely reported [33,34]. To understand the energy gain by the solar tracking system globally, an extensive study in a large area and countries is very important.…”

Section: Introductionmentioning

confidence: 99%

Grid-Connected Photovoltaic Systems with Single-Axis Sun Tracker: Case Study for Central Vietnam

Cuong

Nguyen

et al. 2020

Energies

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Recently, the demand for small grid-connected photovoltaic (PV) systems has been rapidly increasing; this is due to the reduction in the costs of grid-connected storage systems as compared to those of the stand-alone ones. Notably, the performance of the solar tracking system is not only depending on the types but also the region that they are set up and used. To understand how solar tracking systems work globally, we need to know their performance in each country and even the different parts of a country. In this study, two grid-connected PV systems with 250 W solar modules were used to investigate the efficient improvement of a single-axis sun tracking system in Central Vietnam. First, a mechanical tracking device with a linear actuator and a controller was designed and then its performance was comprehensively investigated with a grid-connected PV system. In addition to evaluating the energy gain by the tracking system, this study also considered the energy consumption of the linear actuator with its controller and a small grid-connected PV system; this has generally been omitted in previous works. Experimental results indicate that the total energy consumption of the tracking system was approximately 2–8% of the energy generated by the grid-connected PV system. The maximum overall energy generation was confirmed to have increased by up to 30.3% on a sunny day upon using the proposed tracking system; further, the net energy gain by using the sun tracker was ascertained to be 15.2% in average weather conditions. Based on the success of this work, we will keep experimenting with other parts of Vietnam. Also, we will collaborate with colleagues in other countries to create a guideline for understanding and using the solar tracker regionally and globally.

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“…Many researches are carried to handle the process of wind integration with the grid. Reference [13] investigates a renewable power system by jointly optimizing the expansion of renewable generation facilities and the transmission grid. It is proved that transmission can reduce cost of electricity when wind capacities and solar photovoltaics are installed separately.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model

2018

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The interaction between the gird and wind farms has significant impact on the power grid, therefore prediction of the interaction between gird and wind farms is of great significance. In this paper, a wind turbine-gird interaction prediction model based on long short term memory (LSTM) network under the TensorFlow framework is presented. First, the multivariate time series was screened by principal component analysis (PCA) to reduce the data dimensionality. Secondly, the LSTM network is used to model the nonlinear relationship between the selected sequence of wind turbine network interactions and the actual output sequence of the wind farms, it is proved that it has higher accuracy and applicability by comparison with single LSTM model, Autoregressive Integrated Moving Average (ARIMA) model and Back Propagation Neural Network (BPNN) model, the Mean Absolute Percentage Error (MAPE) is 0.617%, 0.703%, 1.397% and 3.127%, respectively. Finally, the Prony algorithm was used to analyze the predicted data of the wind turbine-grid interactions. Based on the actual data, it is found that the oscillation frequencies of the predicted data from PCA-LSTM model are basically the same as the oscillation frequencies of the actual data, thus the feasibility of the model proposed for analyzing interaction between grid and wind turbines is verified.

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Large-Scale Integration of Renewable Power Sources into the Vietnamese Power System

Cited by 32 publications

References 21 publications

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Grid-Connected Photovoltaic Systems with Single-Axis Sun Tracker: Case Study for Central Vietnam

Prediction of Wind Turbine-Grid Interaction Based on a Principal Component Analysis-Long Short Term Memory Model

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