Research on Evaluation Method of Renewable Energy Accommodation Capability Based on LSTM

Li, Chi; Huang, Yuehui; Li, Chun; Wang, Yuefeng; Dong, Chaoyu; Liang, Zhifeng

doi:10.1109/ei2.2018.8582200

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Then determine the number of principal components, variance contribution rate η i and cumulative variance contribution rate η ∑ (P), which are respectively [20]:…”

Section: Principal Component Analysismentioning

confidence: 99%

Deep Learning-Based Simulation of Surface Suspended Sediment Concentration in the Yangtze Estuary during Typhoon In-Fa

Ren,

Liu,

et al. 2023

Water

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Effectively simulating the variation in suspended sediment concentration (SSC) in estuaries during typhoons is significant for the water quality and ecological conditions of estuarine shoal wetlands and their adjacent coastal waters. During typhoons, SSC undergoes large variations due to the significant changes in meteorological and hydrological factors such as waves, wind speed, and precipitation, which increases the difficulty in simulating SSC. Therefore, in this study, we use an optimized Principal Component Analysis Long Short-Term Memory (PCA-LSTM) framework with an attention mechanism to simulate the SSC in the Yangtze Estuary during Typhoon In-Fa. First, we integrate data from different sources into a multi-source dataset. Second, we use the PCA to reduce the dimensionality of the multi-source data and eliminate redundant variables in the feature data. Third, we introduce an attention mechanism to optimize the long and short-term memory (LSTM) model. Finally, we use the differential evolution (DE) algorithm for hyperparameter selection and merge the feature data with the SSC data as the input of the optimized LSTM network to simulate SSC. The results showed that SSC’s fitting coefficients (R2) at four hydrological stations improved by 7.5%, 6.1%, 7.4%, and 7.8%, respectively, using the attention-based PCA-LSTM compared to the PCA-LSTM. Moreover, compared to the traditional LSTM model, the R2 was improved by 33.8%, 30.5%, 32.0%, and 28.6%, respectively, using the attention-based PCA-LSTM framework. The study indicates that the selection of input variables can affect the model results. Introducing an attention mechanism can effectively optimize the PCA-LSTM framework and improve the simulation accuracy, which helps simulate the non-linear process of SSC variation occurring during Typhoon In-Fa.

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“…Then determine the number of principal components, variance contribution rate η i and cumulative variance contribution rate η ∑ (P), which are respectively [20]:…”

Section: Principal Component Analysismentioning

confidence: 99%

Deep Learning-Based Simulation of Surface Suspended Sediment Concentration in the Yangtze Estuary during Typhoon In-Fa

Ren,

Liu,

et al. 2023

Water

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“…The above model-based method carries out simulation calculations for each time period; the solution is accurate, and the calculation results are relatively reliable. However, the disadvantages are complex modeling, a large amount of calculation, and limited applicable scenarios (Li et al, 2018). The uncertainty on both sides of the source and load leads to the complexity and diversity of power system operation scenarios (Wang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Multi-scenario renewable energy absorption capacity assessment method based on the attention-enhanced time convolutional network

Wu,

Zhou,

Zhang

et al. 2024

Front. Energy Res.

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As the penetration rate of renewable energy in modern power grids continues to increase, the assessment of renewable energy absorption capacity plays an increasingly important role in the planning and operation of power and energy systems. However, traditional methods for assessing renewable energy absorption capacity rely on complex mathematical modeling, resulting in low assessment efficiency. Assessment in a single scenario determined by the source-load curve is difficult because it fails to reflect the random fluctuation characteristics of the source-load, resulting in inaccurate assessment results. To address and solve the above challenges, this paper proposes a multi-scenario renewable energy absorption capacity assessment method based on an attention-enhanced time convolutional network (ATCN). First, a source-load scene set is generated based on a generative adversarial network (GAN) to accurately characterize the uncertainty on both sides of the source and load. Then, the dependence of historical time series information in multiple scenarios is fully mined using the attention mechanism and temporal convolution network (TCN). Finally, simulation and experimental verification are carried out using a provincial power grid located in southwest China. The results show that the method proposed in this article has higher evaluation accuracy and speed than the traditional model.

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“…In addition to the specific methods and influencing factors of solar energy accommodation assessment, indicator establishment of the solar energy accommodation capacity is another emphasis of the assessment process. Some researches try to establish representative indicators such as the actual total output of solar energy equipment [4,6,24], the installed capacity of solar energy equipment that can be accommodated by the systems [9,20], the ability of a given system to flexibly integrate fluctuating solar energy output [8,25] and the annual investment cost of solar energy equipment considering economy [6,8]. However, the researches mentioned above also do not consider the multiple forms of the solar energy accommodation, which brings about the problem of how to evaluate different energy carriers for the indicators establishment of the solar energy accommodation capacity.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the solar energy accommodation capability of the district integrated energy systems considering the transmission delay of the heating network

Wei

et al. 2021

International Journal of Electrical Power & Energy Systems

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Research on Evaluation Method of Renewable Energy Accommodation Capability Based on LSTM

Cited by 5 publications

References 3 publications

Deep Learning-Based Simulation of Surface Suspended Sediment Concentration in the Yangtze Estuary during Typhoon In-Fa

Deep Learning-Based Simulation of Surface Suspended Sediment Concentration in the Yangtze Estuary during Typhoon In-Fa

Multi-scenario renewable energy absorption capacity assessment method based on the attention-enhanced time convolutional network

Assessment of the solar energy accommodation capability of the district integrated energy systems considering the transmission delay of the heating network

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