Control Strategy of the Pumped Storage Unit to Deal with the Fluctuation of Wind and Photovoltaic Power in Microgrid

Wu, Guangyi; Shao, Xiangxin; Jiang, Hong; Chen, Shaoxin; Zhou, Yibing; Xu, Hongyang

doi:10.3390/en13020415

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Pump storage units (PSUs) store excessive power during light load periods and convert hydro energy into electricity at peak load periods [1]. According to the hydropower status report [2], the installed capacity of PSUs reached 159.5 GW in 2020, accounting for 94% of the capacity of all energy storage facilities.…”

Section: Introductionmentioning

confidence: 99%

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

et al. 2022

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The harsh operating environment aggravates the degradation of pumped storage units (PSUs). Degradation trend prediction (DTP) provides important support for the condition-based maintenance of PSUs. However, the complexity of the performance degradation index (PDI) sequence poses a severe challenge of the reliability of DTP. Additionally, the accuracy of healthy model is often ignored, resulting in an unconvincing PDI. To solve these problems, a combined DTP model that integrates the maximal information coefficient (MIC), light gradient boosting machine (LGBM), variational mode decomposition (VMD) and gated recurrent unit (GRU) is proposed. Firstly, MIC-LGBM is utilized to generate a high-precision healthy model. MIC is applied to select the working parameters with the most relevance, then the LGBM is utilized to construct the healthy model. Afterwards, a performance degradation index (PDI) is generated based on the LGBM healthy model and monitoring data. Finally, the VMD-GRU prediction model is designed to achieve precise DTP under the complex PDI sequence. The proposed model is verified by applying it to a PSU located in Zhejiang province, China. The results reveal that the proposed model achieves the highest precision healthy model and the best prediction performance compared with other comparative models. The absolute average (|AVG|) and standard deviation (STD) of fitting errors are reduced to 0.0275 and 0.9245, and the RMSE, MAE, and R2 are 0.00395, 0.0032, and 0.9226 respectively, on average for two operating conditions.

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Section: Introductionmentioning

confidence: 99%

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

et al. 2022

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“…The PHES (Gao et al, 2018;Wu et al, 2020) technology is mature, with the advantages of flexible start-up, fast climbing and unloading, peak and valley regulation, frequency regulation, etc. It can well mitigate the adverse effects of distributed energy sources such as PV power generation and wind power generation on the power system and maintain the stable operation of the power system.…”

Section: Introductionmentioning

confidence: 99%

Research on the Internal Flow Characteristics of Pump Turbines for Smoothing the Output Fluctuation of the Wind–Photovoltaic Complementary System

Ren

Qiao²,

Wei³

et al. 2022

Front. Energy Res.

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Wind and photovoltaic (PV) power generation and other distributed energy sources are developing rapidly. But due to the influence of the environment and climate, the output is very unstable, which affects the power quality and power system stability. Pumped hydroelectric energy storage (PHES) systems are suitable as peaking power sources for wind and photovoltaic (Wind–PV) complementary systems because of their fast start–stop and long life. The mathematical models and operational characteristics of the three subsystems in the wind–PV–PHES complementary system are analyzed to improve the generation efficiency and access capacity of wind and PV power. The peaking characteristics of the PHES system are used to balance the maximum benefit and minimum output fluctuation of the wind–PV complementary system. The stable operation of the pump turbine is an important guarantee for the smooth output of the wind–PV complementary system. Three operating points are selected from the net load curve and converted to the pump turbine model parameters. The internal flow characteristics and laws of the pump turbine under different guide vane opening conditions are summarized through the analysis of the computational fluid dynamics (CFD) numerical simulation post-processing results. The study shows that the output of wind and PV power generation varies with the changes in wind speed and solar radiation, respectively. The output of the wind–PV complementary system still has large fluctuations, and the PHES system can effectively suppress the power fluctuation of the wind–PV complementary system and reduce the abandoned wind and light rate. CFD technology can accurately and efficiently characterize the internal flow characteristics of the pump turbine, which provides a basis for the design, optimization, and transformation of the pump turbine.

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Complementary operation of a small cascade hydropower station group and photovoltaic power stations

Zhu

Chen

et al. 2020

Clean Techn Environ Policy

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Control Strategy of the Pumped Storage Unit to Deal with the Fluctuation of Wind and Photovoltaic Power in Microgrid

Cited by 3 publications

References 26 publications

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

Degradation Trend Prediction of Pumped Storage Unit Based on MIC-LGBM and VMD-GRU Combined Model

Research on the Internal Flow Characteristics of Pump Turbines for Smoothing the Output Fluctuation of the Wind–Photovoltaic Complementary System

Complementary operation of a small cascade hydropower station group and photovoltaic power stations

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