OPTIMAL SCHEDULING MODEL OF CASCADE HYDROPOWER STATIONS CONSIDERING UNCERTAINTY OF INCOMING WATER

Wang Ligang; Li Qun; Jin Yuan; Zhang Haibo

doi:10.19912/j.0254-0096.tynxb.2024-2074

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 360-366. DOI: 10.19912/j.0254-0096.tynxb.2024-2074

OPTIMAL SCHEDULING MODEL OF CASCADE HYDROPOWER STATIONS CONSIDERING UNCERTAINTY OF INCOMING WATER

Wang Ligang, Li Qun, Jin Yuan, Zhang Haibo

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Abstract

Aiming at the problem of output fluctuation of PV power station and the changeable situation of inflow interval of cascade hydropower stations, in order to cope with the uncertainty of photovoltaic power, this study considers the coordination relationship between PV units and hydropower units under the uncertainty of incoming water, and proposes an optimal scheduling model of cascade hydropower stations considering uncertainty of incoming water. Firstly, by analyzing the historical measured interval incoming water of cascade hydropower stations and the solar irradiance of PV power stations, based on the improved K-means clustering method, the typical scenarios of incoming water and PV uncertainty are established. Then, considering the power generation income and the penalty cost of power abandonment, the optimal scheduling model of cascade hydropower stations considering uncertainty of incoming water is proposed with the goal of maximizing the total operating income of cascade hydropower stations. Finally, the simulation results show that the optimal scheduling model of cascade hydropower stations proposed in this study can fully coordinate the schedulable resources, reduce the rate of water and light abandonment, and improve the overall operation economy of cascade hydropower stations.

Key words

hydraulic electrogenerating / uncertainty / photovoltaic / optimal / unit maintenance / cascade hydropower station

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Wang Ligang, Li Qun, Jin Yuan, Zhang Haibo. OPTIMAL SCHEDULING MODEL OF CASCADE HYDROPOWER STATIONS CONSIDERING UNCERTAINTY OF INCOMING WATER[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 360-366 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2074

References

[1] 魏子强, 温鹏, 梁志, 等. 计及需求响应比例的园区综合能源系统低碳经济调度方法[J]. 太阳能学报, 2023, 44(10): 38-45.
WEI Z Q, WEN P, LIANG Z, et al.Low carbon economic dispatching method of park integrated energy system considering proportion of demand response[J]. Acta energiae solaris sinica, 2023, 44(10): 38-45.
[2] 刘英培, 黄寅峰.考虑碳排权供求关系的多区域综合能源系统联合优化运行[J].电工技术学报, 2023, 38(13): 3459-3472.
LIU Y P, HUANG Y F, et al.Joint optimal operation of multi-regional integrated energy system considering the supply and demand of carbon emission rights[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3459-3472.
[3] 张秋艳, 谢俊, 潘学萍, 等.考虑动态频率响应的风光水互补发电短期优化调度模型[J]. 太阳能学报, 2023, 44(1): 516-524.
ZHANG Y Q, XIE J, PAN X P, et al.Short-term optimal scheduling model for wind-solar-hydro hybrid power generation system considering dynamic frequency response[J]. Acta energiae solaris sinica, 2023, 44(1): 516-524.
[4] 陈帝伊, 董文辉, 袁艺晨, 等. 轻量级感知网络学习下风水互补发电系统调节性能分析[J].太阳能学报, 2023, 44(10): 329-338.
CHEN D Y, DONG W H, YUAN Y C, et al.Adjustment performance analysis of wind-hydro hybrid power system under mini neural network reinforcement learning[J]. Acta energiae solaris sinica, 2023, 44(10): 329-338.
[5] 李咸善, 杨拯, 李飞, 等. 基于梯级水电调节的风光水联盟与区域电网联合运行优化调度策略[J]. 中国电机工程学报, 2023, 43(6): 2234-2247.
LI X S, YANG Z, LI F, et al.Optimization scheduling strategy for joint operation of wind-solar-water power alliance and regional power grid based on cascade hydropower regulation[J]. Proceedings of the CSEE, 2023, 43(6): 2234-2247.
[6] 傅质馨, 殷贵, 朱俊澎, 等. 基于EEMD和LSTM的水电机组劣化度预测方法研究[J]. 太阳能学报, 2022, 43(2): 75-81.
FU Z X, YIN G, ZHU J P, et al.Research on forecasting method of hydropower unit deterioration based on EEMD and LSTM[J]. Acta energiae solaris sinica, 2022, 43(2): 75-81.
[7] 黄显峰, 周文, 鲜于虎成, 等. 考虑源-荷协同风险的水光互补系统日前优化调度[J].水资源与水工程学报, 2024, 35(4): 119-126.
HUANG X F, ZHOU W, XIANYU H C, et al.Day-ahead optimal scheduling of Hydro-PV complementary system considering the risk of source-load collaboration[J]. Journal of water resources and water engineering, 2024, 35(4): 119-126.
[8] 闻昕, 秦济森, 谭乔凤, 等. 基于余留期效益函数的水光互补随机优化调度方法[J].水资源保护, 2023, 39(06): 23-31, 62.
WEN X, QIN J S, TAN Q F, et al.Research on stochastic optimal operation of hydro-photovoltaic complementary based on utility function of carryover stage[J]. Water resources protection, 2023, 39(06): 23-31, 62.
[9] 黄显峰, 鲜于虎成, 许昌, 等.考虑短期互补的水光发电系统中长期优化调度[J]. 水力发电学报, 2022, 41(11): 68-78.
HUANG X F, XIANYU H C, XU C, et al.Medium and long-term optimal scheduling of hydro-solar power system considering short-term complementary[J]. Journal of hydroelectric engineering, 2022, 41(11): 68-78.
[10] 姚福明, 廖逸犇, 史云翔, 等. 考虑水光互补系统参与电力辅助服务的短期优化调度[J].现代电力, 2023, 40(5): 706-714.
YAO F M, LIAO Y B, SHI Y X, et al.Short-term optimal dispatch of electric auxiliary services considering hydro-photovoltaic complementary system participation[J]. Modern electric power, 2023, 40(5): 706-714.
[11] YAO G, WU Y, HUANG X, et al.Clustering of typical wind power scenarios based on K-means clustering algorithm and improved artificial bee colony algorithm[J]. IEEE access, 2022, 10: 98752-98760.
[12] 王潇笛, 刘俊勇, 刘友波, 等. 采用自适应分段聚合近似的典型负荷曲线形态聚类算法[J]. 电力系统自动化,2019, 43(1): 110-118.
WANG X D,LIU J Y,LIU Y B,et al.Shape clustering algorithm of typical load curves based on adaptive piecewise aggregate approximation[J]. Automation of electric power systems, 2019, 43(1): 110-118.
[13] 宋冬然, 沈旭涛, 黄朝能, 等. 基于代理模型辅助改进标准粒子群算法的浮式风电场功率优化[J]. 中国电机工程学报, 2023, 43(增刊1): 217-228.
SONG D R, SHEN X T, HUANG Z N, et al.Power optimization of floating offshore wind farm based on surrogate-assisted standard particle swarm algorithm[J]. Proceedings of the CSEE, 2023, 43(Sup 1): 217-228.
[14] 申建建, 张楠男, 程春田, 等. 基于聚类分析和决策树的“一库多级”水电站日调度方法[J]. 中国电机工程学报, 2019, 39(3): 652-663.
SHEN J J, ZHANG N N, CHENG C T,et al.Method for daily operation of hydropower stations with one reservoir based on cluster analysis and decision tree technique[J]. Proceedings of the CSEE, 2019, 39(3): 652-663.