POWER SMOOTHING METHOD FOR HYDROELECTRIC-PHOTOVOLTAIC-ENERGY STORAGE SYSTEM CONSIDERING OPERATING STATUS OF HYDROELECTRIC UNITS

Wang Ranxuan; Sun Zilong; Zhou Kun; Li Yuan

doi:10.19912/j.0254-0096.tynxb.2024-2154

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (4) : 61-73. DOI: 10.19912/j.0254-0096.tynxb.2024-2154

POWER SMOOTHING METHOD FOR HYDROELECTRIC-PHOTOVOLTAIC-ENERGY STORAGE SYSTEM CONSIDERING OPERATING STATUS OF HYDROELECTRIC UNITS

Wang Ranxuan, Sun Zilong, Zhou Kun, Li Yuan

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Abstract

In order to improve the reliability and stability of photovoltaic power generation systems and reduce the impact on grid power fluctuations, this paper proposes a power smoothing method for a hydroelectric-photovoltaic-energy storage complementary system considering the operating status of hydroelectric units. Based on the actual operating data of hydropower units, the feature indicators that characterize the performance of hydropower units are extracted. The improved CRITIC-TOPSIS method is used to obtain the initial evaluation of the operating status of hydropower units. The evaluation results are used as the label output of the Convolutional Neural Network （CNN）-Grey Wolf Optimization Support Vector Machine （GSVM） model to establish an online recognition model for the operating status of hydropower units based on CNN-GSVM. Using local weighted regression algorithm and wavelet decomposition algorithm, the photovoltaic power signal is decomposed into low-frequency grid connected power and high-frequency fluctuating power borne by different performance hydropower units and energy storage. A coordinated control strategy for the hydroelectric-photovoltaic-energy storage complementary system is formulated to achieve smooth control of photovoltaic power. The effectiveness of the proposed method is verified through simulation of actual operating data of the hydroelectric-photovoltaic-energy storage complementary system in a certain region. The simulation analysis results show that adjusting the output of the hydroelectric unit and energy storage system based on the real-time operating status of the hydroelectric unit not only ensures the smoothing of photovoltaic power fluctuations but also considers the safety of the hydroelectric unit.

Key words

convolutional neural networks / support vector machines / wavelet transforms / grey wolf algorithm / water-solar-storage complementarity / improving CRITIC-TOPSIS

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Wang Ranxuan, Sun Zilong, Zhou Kun, Li Yuan. POWER SMOOTHING METHOD FOR HYDROELECTRIC-PHOTOVOLTAIC-ENERGY STORAGE SYSTEM CONSIDERING OPERATING STATUS OF HYDROELECTRIC UNITS[J]. Acta Energiae Solaris Sinica. 2026, 47(4): 61-73 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2154

References

[1] 鲜于虎成, 黄显峰, 张艳青, 等. 考虑短期波动与弃电风险的水光互补系统中长期双层嵌套优化调度[J]. 电工技术学报, 2023, 38(21): 5781-5793.
XIANYU H C, HUANG X F, ZHANG Y Q, et al.Medium-and long-term double-layer nested optimal scheduling of hydro-PV complementary system considering short-term power fluctuation and curtailment risk[J]. Transactions of China Electrotechnical Society, 2023, 38(21): 5781-5793.
[2] 王聪, 赵杰, 李亮, 等. 一种嵌入改进海鸥算法的水光储鲁棒控制研究[J]. 电力系统保护与控制, 2023, 51(24): 143-153.
WANG C, ZHAO J, LI L, et al.Robust control of water and light storage embedded in an improved seagull optimization algorithm[J]. Power system protection and control, 2023, 51(24): 143-153.
[3] 张宇, 田亮. 基于PCA和改进TOPSIS法的火电机组运行综合评价[J]. 华北电力大学学报(自然科学版), 2023, 50(3): 110-116, 126.
ZHANG Y, TIAN L.Comprehensive evaluation of operation of thermal power units based on PCA and improved TOPSIS method[J]. Journal of North China Electric Power University(natural science edition), 2023, 50(3): 110-116, 126.
[4] 刘任改, 向文平, 喻永松, 等. 基于变权层次分析的水电机组运行状态评估[J]. 水电站机电技术, 2015, 38(4): 55-59.
LIU R G, XIANG W P, YU Y S, et al.Operation state evaluation of hydropower units based on variable weight analytic hierarchy process[J]. Mechanical & electrical technique of hydropower station, 2015, 38(4): 55-59.
[5] 秦小元, 胡伟, 刘仁杰, 等. 基于层次分析与改进模糊评价的水电机组检修效果综合评价[J]. 水电能源科学, 2024, 42(4): 183-187.
QIN X Y, HU W, LIU R J, et al.Comprehensive maintenance effect evaluation of hydroelectric generating unit based on hierarchical analysis process and improved fuzzy evaluation method[J]. Water resources and power, 2024, 42(4): 183-187.
[6] 吕志鹏, 吴鸣, 宋振浩, 等. 电能质量CRITIC-TOPSIS综合评价方法[J]. 电机与控制学报, 2020, 24(1): 137-144.
LYU Z P, WU M, SONG Z H, et al.Comprehensive evaluation of power quality on CRITIC-TOPSIS method[J]. Electric machines and control, 2020, 24(1): 137-144.
[7] 姜建国, 杨效岩, 毕洪波. 基于VMD-FE-CNN-BiLSTM的短期光伏发电功率预测[J]. 太阳能学报, 2024, 45(7): 462-473.
JIANG J G, YANG X Y, BI H B.Photovoltaic power forecasting method based on VMD-FE-CNN-BiLSTM[J]. Acta energiae solaris sinica, 2024, 45(7): 462-473.
[8] 贾岳鹏, 赵道利, 安学利, 等. 基于GAF-CNN的机组振动信号特征提取方法研究[J]. 大电机技术, 2024(3): 29-35.
JIA Y P, ZHAO D L, AN X L, et al.Research on feature extraction method of hydropower unit vibration signal based on GAF-CNN[J]. Large electric machine and hydraulic turbine, 2024(3): 29-35.
[9] 何葵东, 王卫玉, 金艳, 等. 基于CNN-SVM的水电机组智能故障诊断方法研究[J]. 水电能源科学, 2023, 41(4): 207-210, 215.
HE K D, WANG W Y, JIN Y, et al.Research on intelligent fault diagnosis method of hydroelectric generating unit based on CNN-SVM[J]. Water resources and power, 2023, 41(4): 207-210, 215.
[10] 闫群民, 刘语忱, 董新洲, 等. 基于CEEMDAN-HT的平抑光伏出力混合储能容量优化配置[J]. 电力系统保护与控制, 2022, 50(21): 43-53.
YAN Q M, LIU Y C, DONG X Z, et al.Hybrid energy storage capacity optimization configuration for smoothing PV output based on CEEMDAN-HT[J]. Power system protection and control, 2022, 50(21): 43-53.
[11] 陈裕, 张怡, 谢俊峰. 自适应滑动平均与小波包分解平抑风电波动[J]. 控制工程, 2021, 28(7): 1281-1288.
CHEN Y, ZHANG Y, XIE J F.Adaptive moving average and wavelet packet decomposition to smooth wind power fluctuation[J]. Control engineering of China, 2021, 28(7): 1281-1288.
[12] 贺彬, 任永峰, 贾伟青, 等. 储能MPC平抑分散式风电并网功率波动策略研究[J]. 太阳能学报, 2024, 45(6): 132-139.
HE B, REN Y F, JIA W Q, et al.Strategy of energy storage MPC to smooth grid-connected power fluctuation of distributed wind power[J]. Acta energiae solaris sinica, 2024, 45(6): 132-139.
[13] 汪茹康, 王健, 徐钢, 等. 基于SG-IDDBO-VMD的风光互补复合波动平抑策略[J]. 中国电机工程学报, 2025, 45(16): 6314-6325.
WANG R K, WANG J, XU G, et al.Strategy on wind-solar complementary composite fluctuation smoothing based on SG-IDDBO-VMD[J]. Proceedings of the CSEE, 2025, 45(16): 6314-6325.
[14] 王珏, 廖溢文, 韩文福, 等. 碳达峰背景下抽水蓄能-风电联合系统建模及有功功率控制特性研究[J]. 水利水电技术, 2021, 52(9): 172-181.
WANG J, LIAO Y W, HAN W F, et al.Modeling and active power control characteristics of pumped storage-wind hybrid power system in the context of peak carbon dioxide emission[J]. Water resources and hydropower engineering, 2021, 52(9): 172-181.
[15] CHEN L, WANG J, SUN Z, et al.Smoothing photovoltaic power fluctuations for cascade hydro-PV-pumped storage generation system based on a fuzzy CEEMDAN[J]. IEEE access, 2019, 7: 172718-172727.
[16] WU F, WANG J, SUN Z, et al.An optimal wavelet packets basis method for cascade hydro-PV-pumped storage generation systems to smooth photovoltaic power fluctuations[J]. Energies, 2019, 12(24): 4642
[17] 朱洁, 冯建刚, 高玉琴, 等. 基于BWM-CRITIC-TOPSIS的幸福河湖综合评价模型[J]. 水利水电科技进展, 2022, 42(6): 8-14, 20.
ZHU J, FENG J G, GAO Y Q, et al.Comprehensive evaluation model for happy rivers and lakes based on BWM-CRITIC-TOPSIS[J]. Advances in science and technology of water resources, 2022, 42(6): 8-14, 20.
[18] YE F, SUN J, WANG Y Y, et al.A novel method for the performance evaluation of institutionalized collaborative innovation using an improved G1-CRITIC comprehensive evaluation model[J]. Journal of innovation & knowledge, 2023, 8(1): 100289.
[19] ALGHASSAB M.Quantitative assessment of sustainable renewable energy through soft computing: fuzzy AHP-TOPSIS method[J]. Energy reports, 2022, 8: 12139-12152.
[20] MALEFAKI S, MARKATOS D, FILIPPATOS A, et al.A comparative analysis of multi-criteria decision-making methods and normalization techniques in holistic sustainability assessment for engineering applications[J]. Aerospace, 2025, 12(2): 100.
[21] 张逴, 李树成, 魏东, 等. 基于GS-SVM的光伏阵列积灰程度评估方法研究[J]. 太阳能学报, 2024, 45(11): 220-226.
ZHANG C, LI S C, WEI D, et al.Evaluation method of ash accumulation degree of photovoltaic array based on GS-SVM[J]. Acta energiae solaris sinica, 2024, 45(11): 220-226.
[22] 杨彤, 王卫玉, 张培, 等. 基于CEEMDAN和混合灰狼算法优化SVM的水电机组故障诊断方法[J]. 水电能源科学, 2022, 40(3): 195-198.
YANG T, WANG W Y, ZHANG P, et al.Fault diagnosis of hydropower unit based on CEEMDAN and hybrid grey wolf algorithm to optimize SVM[J]. Water resources and power, 2022, 40(3): 195-198.
[23] 刘洋, 程强, 史曜炜, 等. 基于注意力模块及1D-CNN的滚动轴承故障诊断[J]. 太阳能学报, 2022, 43(3): 462-468.
LIU Y, CHENG Q, SHI Y W, et al.Fault diagnosis of rolling bearings based on attention module and 1D-CNN[J]. Acta energiae solaris sinica, 2022, 43(3): 462-468.
[24] 罗超月岭, 郑韵馨, 徐帧雨, 等. 基于Borderline-SMOTE-IHT混合采样的改进GWO-SVM变压器故障诊断方法[J]. 智慧电力, 2023, 51(7): 108-114.
LUO C Y L, ZHENG Y X, XU Z Y, et al. Improved GWO-SVM transformer fault diagnosis method based on borderline-SMOTE-IHT mixed sampling[J]. Smart power, 2023, 51(7): 108-114.
[25] 荆东星, 陈杨晖, 全哲. 基于1D-CNN和SWLSTM的风电轴承故障诊断方法[J]. 机械强度, 2023, 45(6): 1309-1317.
JING D X, CHEN Y H, QUAN Z.Wind turbine rolling bearing fault diagnosis method based on 1D-CNN and SWLSTM[J]. Journal of mechanical strength, 2023, 45(6): 1309-1317.
[26] QIAN C, HE N, HE L L, et al.A weighted Gaussian process regression model based on improved local outlier factor and its application in state of health estimation of lithium-ion battery[J]. Engineering applications of artificial intelligence, 2024, 138: 109314.
[27] 刘光军, 张恒. 基于小波分解和GDI的动力电池故障诊断[J]. 电池, 2023, 53(2): 165-168.
LIU G J, ZHANG H.Fault diagnosis for power battery based on wavelet decomposition and GDI[J]. Battery bimonthly, 2023, 53(2): 165-168.