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浙江大学学报(工学版)
浙江大学学报(工学版)  2024, Vol. 58 Issue (7): 1505-1515    DOI: 10.3785/j.issn.1008-973X.2024.07.020
机械工程、能源工程     
流域风光水电出力互补特性
李昕阳1(),刘为锋2,*(),郭旭宁2,李云玲2,朱非林3,钟平安3
1. 天津大学 建筑工程学院，天津 300072
2. 水利部水利水电规划设计总院 水战略研究一处，北京 100120
3. 河海大学 水文水资源学院，江苏 南京 210098
Complementary characteristics of wind-photovoltaic-hydropower output in basin
Xinyang LI1(),Weifeng LIU2,*(),Xuning GUO2,Yunling LI2,Feilin ZHU3,Ping’an ZHONG3
1. School of Civil Engineering, Tianjin University, Tianjin 300072, China
2. Water Strategy Research Unit One, MWR General Institute of Water Resources and Hydropower Planning and Design, Beijing 100120, China
3. College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
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摘要：

定量评估风光水能源出力互补特性对风光水电站规模布局以及互补优化调度具有重要意义，现有互补特性评价指标仅适用于评价两能源互补特性. 为此建立适合三能源互补特性评价的指标体系，结合历史气象资料，探明雅砻江流域风光水电出力长期互补特性. 针对风光水电出力受气象条件的显著影响和短期互补特性的复杂性，结合机器学习理论，提出风光水电出力短期典型日智能筛选模型，剖析不同时间计算步长对风光水电出力互补特性评价结果的差异. 计算结果表明，长期尺度风光能源出力对水电能源呈现电量补偿特性，使水电能源的发电效益提高3%；短期尺度水电能源出力对风光能源出力具有较好的电力补偿效益，水电补偿后风光弃电率减小了18个百分点，出力短缺率减小了28个百分点.
关键词： 风光水电出力;  互补性分析;  三能源互补性指标;  机器学习;  雅砻江流域    
Abstract:

The Quantitative assessment of the complementary characteristics of wind-photovoltaic-hydropower power output is important for the layout and optimized scheduling of wind-photovoltaic-hydropower systems. Existing indicators for evaluating complementary characteristics are only applicable to assessing the complementary characteristics between two energy sources. Indicators suitable for evaluating the complementary characteristics of three energy sources were established. Combining historical meteorological data, the long-term complementary characteristics of wind-photovoltaic-hydropower output in the Yalong River basin were revealed. In response to the significant impact of meteorological conditions on wind-photovoltaic-hydropower output and the complexity of short-term complementary characteristics, an intelligent screening model for short-term typical days of wind-photovoltaic-hydropower output was proposed, integrating with machine learning theory. The differences in evaluation results of wind-photovoltaic-hydropower output complementary characteristics under different time calculation steps were analyzed. The results show that at a long-term scale, wind and solar power output compensates for hydropower output, enhancing the hydroelectric energy generation efficiency by 3%. At a short-term scale, hydropower output demonstrated favorable electricity compensation benefits for wind and solar power output, with the wind and solar curtailment rate reduced by 18 percentage points and the output shortage rate reduced by 28 percentage points after hydropower compensation.
Key words: wind-photovoltaic-hydropower output    complementarity analysis    tri-energy complementarity index    machine learning    Yalong River basin
收稿日期: 2023-06-11 出版日期: 2024-07-01
CLC:  TP 393  
基金资助: 国家重点研发计划重点专项（2022YF3202300）；水利部水利青年拔尖人才项目；水利水电规划设计总院“揭榜挂帅”项目；中国博士后科学基金资助项目（2021M702313）；中央高校基本科研业务费专项资金资助项目（B240201123）.
通讯作者: 刘为锋     E-mail: 842212007@qq.com;weifliu@qq.com
作者简介: 李昕阳（1999—），女，博士生，从事水资源规划与战略研究及风光水多能互补优化调度研究. orcid.org/0009-0002-1750-1365. E-mail：842212007@qq.com
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引用本文:

李昕阳,刘为锋,郭旭宁,李云玲,朱非林,钟平安. 流域风光水电出力互补特性[J]. 浙江大学学报(工学版), 2024, 58(7): 1505-1515.

Xinyang LI,Weifeng LIU,Xuning GUO,Yunling LI,Feilin ZHU,Ping’an ZHONG. Complementary characteristics of wind-photovoltaic-hydropower output in basin. Journal of ZheJiang University (Engineering Science), 2024, 58(7): 1505-1515.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.07.020    或    https://www.zjujournals.com/eng/CN/Y2024/V58/I7/1505

图 1  两能源出力的互补性示意图
图 2  风光水电出力综合向量示意图
图 3  欧式距离计算示意图
图 4  动态时间弯曲距离算法的对齐方式和弯曲路径示意图
图 5  K-Means-DTW算法筛选典型日流程图
图 6  标准化后风光水的月平均出力
图 7  风光能源在长期尺度对水电能源提供电量补偿
图 8  确定最优聚类数
算法运行次数SSE
K-Means-EDK-Means-DTW
1756334
2769329
3749321
4761344
5763344
6752326
7762335
8752324
9746335
10760335
平均值757333
方差5360
表 1  不同算法的簇内误差平方和
图 9  基于K-Means-ED算法的典型日筛选
图 10  基于K-Means-DTW的典型日筛选
图 11  各个典型日风光水电出力过程
典型日${k_t}\left( {\boldsymbol{r}} \right)$互补等级
a0.703中等互补
b0.624弱互补
c0.522弱互补
d0.538弱互补
e0.764中等互补
f0.852较互补
g0.670中等互补
h0.776中等互补
表 2  各个典型日风光水电出力的综合互补系数
图 12  能源组合出力在小时尺度对综合互补系数的贡献
图 13  水电出力在短期尺度对风光出力提供电力补偿
典型日无水电补偿有水电补偿
${\eta _1}$${\eta _2}$${\eta _1}$${\eta _2}$
a028.2400
b0.9417.5600
c17.7716.720.490
d11.1814.9500
e13.9210.270.380
f12.141.7600
g13.9310.701.150
h19.017.230.700
表 3  在短期尺度有无水电补偿对风光出力的影响
计算时段${\boldsymbol{r}}$${L_p}\left( {\boldsymbol{r}} \right)$${k_t}\left( {\boldsymbol{r}} \right)$
长期$ - 0.336{{\boldsymbol{D}}_{{\mathrm{ws}}}} - 0.851{{\boldsymbol{D}}_{{\mathrm{wh}}}}+0.259{{\boldsymbol{D}}_{{\mathrm{sh}}}} $1.03600.873
短期$ - 0.026{{\boldsymbol{D}}_{{\mathrm{ws}}}} - 0.418{{\boldsymbol{D}}_{{\mathrm{wh}}}}+0.223{{\boldsymbol{D}}_{{\mathrm{sh}}}} $1.38950.716
表 4  不同计算时段的风光水电出力互补性
图 14  能源组合出力在不同计算时段对综合互补系数的贡献
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