引水条件下流域梯级水库优化调度技术

Optimal operation technology of cascade reservoirs in river basins under water diversion conditions

摘要

针对浑江流域梯级水库在跨流域引水背景下，供水与发电目标时空冲突、传统调度图难以动态响应多目标权衡的问题，提出耦合供水-发电目标的梯级水库优化调度模型及动态规划（dynamic programming，DP）联合逐步优化算法（pelican optimization algorithm，POA）的求解方法。通过优先级设定将多目标转化为有序单目标，结合丰、平、枯 3 种典型年来水及 4 种供水工况，构建已知供水过程、已知供水总量、传统调度图 3 种调度情景，以浑江流域桓仁、回龙山、太平哨梯级水库为研究对象，基于长序列数据开展模型应用验证，探究优化调度对缓解供发电冲突、提升资源利用效率的效果。结果显示：已知供水总量优化调度情景下，梯级总发电量较已知供水过程平均提升 0.85%，梯级最小出力平均提升 5.27%，枯水年工况 4 供水缺口从 3.21 亿 m³ 缩减至 1.47 亿 m³；与传统调度图相比，已知供水过程、已知供水总量优化调度使梯级保证出力分别提升 46.93%、52.49%，跨流域供水保证率分别提升 5.45%、6.00%，核心指标改善效果显著。研究成果可为相关区域梯级水库多目标协同调度提供技术支撑。

Abstract

The Hunjiang River basin's cascade hydropower plants faced significant temporal and spatial conflicts between water supply and power generation goals amid inter-basin water transfer. These conflicts frequently led to inefficient water resource allocation, with peak water supply demand unmet and power generation capacity underutilized during water-rich seasons. Meanwhile, traditional scheduling diagrams, which relied on fixed empirical parameters and historical operational data, made it difficult to respond dynamically to the complex trade-offs among multiple objectives (such as water supply security, power generation efficiency, and system stability) as hydrological conditions and water demand changed. These limitations further reduced the cascade hydropower system's overall utility. A cascade reservoir optimal scheduling model that couples water supply and power generation objectives was developed to address these issues, along with a solution technique that combines dynamic programming (DP) and the progressive optimality algorithm (POA). Three core objectives−minimizing the basin's own water shortage, minimizing inter-basin water shortage, and increasing cascade power generation−were converted into ordered single objectives through scientific priority setting. Combined with three typical inflow years (1971 wet, 1962 normal, and 1945 dry) and four water supply conditions, three scheduling scenarios were built: known daily water supply process, known total annual water supply volume, and traditional scheduling diagram (for comparison). The hydropower stations in Huanren, Huilongshan, and Taipingsha were selected as research objects, and the model’s accuracy was verified using long-series hydrological data spanning almost 50 years and detailed engineering data. Verification results demonstrated that under the optimal scheduling scenario with a known total water supply volume, compared with the scenario with a known water supply process, the annual total power generation of the cascade hydropower stations increased by an average of 0.85%, and the minimum output (a key indicator reflecting the system’s stable power supply capacity) rose by 5.27%, effectively improving the reliability of power supply for the regional power grid. The water supply gap decreased from 321 million m³ to 147 million m³ with a reduction rate of 54.21% for the dry year (1945), the most difficult condition for water supply, under working condition 4 (with the highest comprehensive water demand including domestic, agricultural, industrial, and inter-basin transfer needs). This significantly decreased dry-season water stress. Compared with the traditional scheduling diagram scenario, the two optimal scheduling scenarios (known water supply process and known total water supply volume) showed more outstanding performance: the cascade guaranteed output (the minimum power output stably provided by stations throughout the year) increased by 46.93% and 52.49%, respectively, and the inter-basin water supply guarantee rate (a critical index for fulfilling transfer commitments) increased by 5.45% and 6.00% respectively. The significant improvements in core indicators demonstrated the model's superiority and practical application value. The above research results not only provided important technical support for the multi-objective coordinated scheduling of cascade reservoirs in inter-basin water transfer project areas but also laid a solid foundation for the scientific operation and management of similar water-energy systems. In practical engineering applications, these results could help relevant management departments make more reasonable scheduling decisions, thereby improving the comprehensive utilization efficiency of water resources and hydropower resources. Specifically, they could reduce the waste of water resources caused by unreasonable scheduling, improve the utilization rate of hydropower energy, and ultimately promote the sustainable development of the regional water-energy-social economy system.

关键词

跨流域引水 / 梯级水库 / 优化调度模型 / 动态规划-逐步优化算法 (DP-POA) / 多目标协同

Key words

inter-basin water diversion / cascade reservoir / optimal operation model / dynamic programming-progressive optimality algorithm (DP-POA) / multi-objective coordination

参考文献

[1] 贾志峰, 刘鹏程, 马艳, 等. 我国水资源动态演变及开发利用现状分析[J]. 水电能源科学, 2023, 41(3): 27-30. DOI: 10.20040/j.cnki.1000-7709.2023.20220437.

[2] 孙琪, 侯坤, 袁天龙. 我国水资源综合利用评价指标研究[J/OL]. 水利发展研究, 2025-10-13. https://link.cnki.net/urlid/11.4655.TV.20250929.1908.008.

[3] 刘涛. 生态文明建设背景下水资源管理的重要性及实践举措分析[J]. 水资源保护, 2025, 41(4): 268-269.

[4] 黄硕俏, 吴泽宁, 狄丹阳. 水资源工业生产价值能值评估及空间分布[J]. 南水北调与水利科技, 2020, 18(1): 202-208. DOI: 10.13476/j.cnki.nsbdqk.2020.0021.

[5] 李跃红, 蒋晓辉, 张琳. 黄河流域水资源节约集约利用能力评价[J]. 南水北调与水利科技（中英文）, 2023, 21(4): 731-741. DOI: 10.13476/j.cnki.nsbdqk.2023.0071.

[6] 肖尧, 钟平安, 徐斌, 等. 基于区间犹豫模糊语言集的水资源多目标决策[J]. 南水北调与水利科技（中英文）, 2021, 19(1): 50-66. DOI: 10.13476/j.cnki.nsbdqk.2021.0005.

[7] 彭卓越, 李梦婷, 刘亚明, 等. 南水北调工程实施下受水区碳生态补偿标准研究: 以济南市为例[J]. 地理科学进展, 2025, 44(8): 1720-1731. DOI: 10.18306/dlkxjz.2025.08.012.

[8] Rebecca G D, Liu Ning, Caldwell P V, et al. Inter-basin surface water transfers database for public water supplies in conterminous United States, 1986-2015[J]. Scientific Data, 2023, 10(1): 255- 255. DOI: 10.1038/S41597-023-02148-5.

[9] Ming Bo, Zhong Huayu, Zhang Wei, et al. Deriving operating rules for inter-basin water transfer projects incorporating a scenario reduction strategy[J]. Journal of Hydrology, 2023, 624： 129854. DOI: 10.1016/J.JHYDROL.2023.129854.

[10] 方国华, 钟华昱, 闻昕, 等. 考虑湖泊互济互调运行的江苏省南水北调工程优化调度[J]. 水科学进展, 2023, 34(5): 776-787. DOI: 10.14042/j.cnki.32.1309.2023.05.012.

[11] 蒋佳怡, 徐斌, 岳浩, 等. 梯级水库群风光水多能互补多目标优化调度模型[J]. 中国农村水利水电, 2025(9): 141-148. DOI: 10.12396/znsd.241891.

[12] 张锴慧, 王好芳, 张泽玉, 等. 胶东地区跨流域调水优化配置研究[J]. 南水北调与水利科技, 2018, 16(2): 100-107. DOI: 10.13476/j.cnki.nsbdqk.2018.0045.

[13] 王秀杰, 马云鹏, 王玲, 等. 基于 NSGA-Ⅱ的南水北调东线一期北延应急工程水资源多目标优化配置[J]. 南水北调与水利科技（中英文）, 2024, 22(增刊 1): 84-93.

[14] 刘鸿鑫, 王丽梅, 吴承君. 南水北调西线工程调水对洮河梯级水库发电影响[J]. 南水北调与水利科技（中英文）, 2024, 22(6): 1091-1099. DOI: 10.13476/j.cnki.nsbdqk.2024.0108.

[15] 刘丹, 刘生杰, 廖君霞, 等. 我国典型大型跨流域调水工程的经济性再分析[J]. 南水北调与水利科技（中英文）, 2025, 23(5): 1047-1058. DOI: 10.13476/j.cnki.nsbdqk.2025.0105.

[16] Liu Fan, Huang Wentao, Ma Jingjing, et al. Optimal economic dispatch strategy for cascade hydropower stations considering electric energy and peak regulation markets[J]. Energies, 2025, 18(7): 1762- 1762. DOI: 10.3390/EN18071762.

[17] 王晔, 王威浩, 鲍淑君, 等. 黄河流域上游水库群多目标协同调度机制研究[J]. 南水北调与水利科技（中英文）, 2025, 23(6): 1358-1367. DOI: 10.13476/j.cnki.nsbdqk.2025.0135.

[18] 黄显峰, 王浩天, 高玉琴, 等. 基于改进 PSO 算法的水库群防洪优化调度[J]. 水利水电技术（中英文）, 2025, 56(10): 203-212. DOI: 10.13928/j.cnki.wrahe.2025.10.016.

[19] 徐杨, 吕昊, 刘帅, 等. Kriging 水动力学代理模型在水库群优化调度中的应用[J]. 长江科学院院报, 2024, 41(2): 7-13. DOI: 10.11988/ckyyb.20221303.

[20] 杨康. 考虑风、光出力不确定性的梯级水库群多尺度优化调度[D]. 西安: 西安理工大学, 2024. DOI: 10.27398/d.cnki.gxalu.2024.001448.

[21] 朱迪, 周研来, 陈华, 等. 考虑分级防洪目标的梯级水库汛控水位调度模型及应用[J]. 水利学报, 2023, 54(4): 414-425. DOI: 10.13243/j.cnki.slxb.20220360.

[22] 艾学山, 郭佳俊, 穆振宇, 等. 梯级水库群多目标优化调度模型及 CPF-DPSA 算法研究[J]. 水利学报, 2023, 54(1): 68-78. DOI: 10.13243/j.cnki.slxb.20210947.

[23] 林鹏飞, 游进军, 蒋云钟, 等. 面向双保证率的水库供水能力优化计算方法研究[J]. 水利学报, 2022, 53(5): 539-548. DOI: 10.13243/j.cnki.slxb.20200852.

[24] 董远恒, 徐斌, 张雨薇, 等. 跨流域调水多水源多目标水量调度模拟-优化双层耦合算法[J]. 南水北调与水利科技（中英文）, 2025, 23(1): 36-46. DOI: 10.13476/j.cnki.nsbdqk.2025.0004.

[25] 刘树锋, 王志华, 关帅, 等. 考虑各月生态流量变化约束的梯级小水电多目标优化调度研究[J]. 水利水电技术（中英文）, 2025, 56(11): 200-212. DOI: 10.13928/j.cnki.wrahe.2025.11.016.

[26] 包娟, 李想, 穆祥鹏, 等. 黄河上游玛尔挡-班多-羊曲梯级水电站联合调度效益分析[J]. 南水北调与水利科技（中英文）, 2025, 23(2): 231-241. DOI: 10.13476/j.cnki.nsbdqk.2025.0030.

[27] 许继军, 张晓琦, 周涛, 等. 引江补汉工程实施后的南水北调中线水源工程可调水量分析[J]. 南水北调与水利科技（中英文）, 2023, 21(4): 790-799. DOI: 10.13476/j.cnki.nsbdqk.2023.0077.

[28] 韩涵元. 考虑调蓄水库的引汉济渭工程联合优化调配研究[D]. 西安: 西安理工大学, 2024. DOI: 10.27398/d.cnki.gxalu.2024.001507.

[29] 雷其鸣, 牛庚, 桑学锋, 等. 基于改进强化学习算法的水库群多目标优化调度[J]. 南水北调与水利科技（中英文）, 2025, 23(5): 1185-1195. DOI: 10.13476/j.cnki.nsbdqk.2025.0118.

[30] 王旭, 郭旭宁, 雷晓辉, 等. 基于可行空间搜索遗传算法的梯级水库群调度规则[J]. 南水北调与水利科技, 2014, 12(4): 173-176. DOI: 10.13476/j.cnki.nsbdqk.2014.04.038.

[31] 翁硕, 林明财, 林建锋, 等. 基于多目标优化的敖江流域梯级水库水资源调度研究[J]. 水利水电科技进展, 2026, 46(2): 46-54.

[32] 张可, 孙艳, 王晓鹏, 等. 区间水优先的水库群引供水调度规则研究[J]. 水利水电技术（中英文）, 2024, 55(1): 124-133. DOI: 10.13928/j.cnki.wrahe.2024.01.011.

[33] 张明, 王随玲, 蒋志强, 等. 基于动态规划算法的额勒赛下游水库防洪优化调度研究[J]. 水电能源科学, 2021, 39(11): 127-131. DOI: 10.20040/j.cnki.1000-7709.2021.11.031.

[34] Chai Fuxin, Peng Feng, Zhang Hongping, et al. Stable improved dynamic programming method: An efficient and accurate method for optimization of reservoir flood control operation[J]. Water Resources Management, 2023, 37(14): 5635-5654. DOI: 10.1007/S11269-023-03622-W.

[35] 张宏喜, 安琪, 黎萍, 等. 基于动态规划算法的资源应急分配优化模型[J]. 电子设计工程, 2024, 32(10): 154-158. DOI: 10.14022/j.issn1674-6236.2024.10.032.

[36] 纪昌明, 李传刚, 刘晓勇, 等. 基于泛函分析思想的动态规划算法及其在水库调度中的应用研究[J]. 水利学报, 2016, 47(1): 1-9. DOI: 10.13243/j.cnki.slxb.20150453.

[37] 朱迪, 梅亚东, 许新发, 等. 复杂防洪系统优化调度的三层并行逐步优化算法[J]. 水利学报, 2020, 51(10): 1199-1211. DOI: 10.13243/j.cnki.slxb.20200148.

[38] 曾宏, 董新亮, 彭绪斌, 等. 考虑反调节水库作用的木里河梯级水库水库中长期优化调度研究[J]. 水电能源科学, 2022, 40(5): 87-90.

[39] Zhang Liang, Wang Wensong, He Yong, et al. Research on maintenance boundary variation of cascade hydropower units based on dynamic programming-progressive optimization algorithm[J]. E3S Web of Conferences, 2023, 423： 02006. DOI: 10.1051/E3SCONF/202342302006.

[40] Yuan Longhu, Liu Huaixiang, Lu Yongjun, et al. Multi-objective integrated decision method of cascade hydropower stations based on optimization algorithm and evaluation model[J]. Journal of Hydrology, 2024, 638： 131533. DOI: 10.1016/J.JHYDROL.2024.131533.

[41] 肖敬, 董增川, 罗晓丽, 等. 基于改进逐步优化算法的水库防洪优化调度[J]. 人民黄河, 2018, 40(10): 25-28. DOI: 10.3969/j.issn.1000-1379.2018.10.006.

[42] 张诚, 周建中, 王超, 等. 梯级水电站优化调度的变阶段逐步优化算法[J]. 水力发电学报, 2016, 35(4): 12-21.

基金资助

国家重点研发计划项目(2023YFC3209404)

引水条件下浑江流域梯级水电站优化调度系统开发项目(HYGS-SC-2019-125)