基于非参数核密度估计的风光水火储系统灵活性评估方法研究

doi:10.11930/j.issn.1004-9649.202508057

摘要/Abstract

摘要：

随着新能源渗透率持续提升，源荷双侧不确定性对电力系统稳定运行构成显著风险。为科学评估风光水火储多源耦合新型电力系统的灵活性，提出融合源荷双侧不确定性区间估计与随机生产模拟的协同分析框架。首先，通过非参数核密度估计生成新能源出力与负荷的置信区间，构建极端供需情景以量化不确定性。其次，结合分级调度策略，优先将风电、光伏和径流式水电等效为负值负荷，再考虑系统爬坡约束，利用改进的随机生产模拟算法安排火电机组出力。最后，调度库容式水电承接系统剩余负荷。当发生切负荷或弃新能源事件时，通过储能设备充放电进行调节。案例分析表明，非参数估计可有效表征源荷双侧不确定性；系统因爬坡能力不足引发的切负荷和弃新能源电量占比分别为14.8%和91.5%，即爬坡约束是影响系统稳定运行的重要因素；配置储能可显著提升系统调节能力，使系统失负荷概率和弃新能源概率分别降低8.6%和34.1%。

关键词: 等效电量函数法, 库容式水电, 非参数核密度估计, 灵活性评估, 新型电力系统

Abstract:

With the rising penetration of new energy, uncertainties on both generation and load sides pose significant risks to power system stability. To scientifically assess the flexibility of a novel multi-source coupled power system (integrating wind, solar, thermal, hydro and energy storage), a collaborative analysis framework is proposed, combining interval estimation of bilateral generation-load uncertainties with stochastic production simulation. First, non-parametric kernel density estimation generates confidence intervals for new energy output and load, and extreme supply-demand scenarios are constructed to quantify such uncertainties. Second, via a hierarchical dispatching strategy, wind, photovoltaic and run-of-river hydropower are prioritized as equivalent negative loads. Considering system ramping constraints, an improved stochastic production simulation algorithm schedules thermal power unit output. Finally, reservoir-type hydropower undertakes remaining system load. In case of load shedding or new energy curtailment, energy storage devices regulate the system through charging and discharging. Case studies show non-parametric estimation effectively characterizes bilateral generation-load uncertainties. The proportions of load shedding and new energy curtailment due to insufficient system ramping capacity are 14.8% and 91.5%, respectively, indicating ramping constraints are critical to system stability. Energy storage configuration significantly enhances regulation capacity, reducing the loss of load probability (LOLP) and new energy curtailment probability by 8.6% and 34.1%, respectively.

Key words: equivalent power function method, reservoir-type hydropower, nonparametric kernel density estimation, flexibility evaluation, new-type power system

米熠, 徐雪松, 杨一鸣, 邹鑫. 基于非参数核密度估计的风光水火储系统灵活性评估方法研究[J]. 中国电力, 2026, 59(4): 12-23.

MI Yi, XU Xuesong, YANG Yiming, ZOU Xin. Nonparametric kernel density estimation based wind-solar-hydro-thermal-storage system operational flexibility evaluation[J]. Electric Power, 2026, 59(4): 12-23.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202508057

https://www.electricpower.com.cn/CN/Y2026/V59/I4/12

图/表 14

参考文献 35

1	刘颖杰, 陈红坤, 田圆, 等. 基于投资组合高阶矩分析的电力系统灵活性评估[J]. 电力系统保护与控制, 2024, 52 (5): 116- 127.
	LIU Yingjie, CHEN Hongkun, TIAN Yuan, et al. Flexibility assessment of a power system based on higher-moment analysis of an investment portfolio[J]. Power System Protection and Control, 2024, 52 (5): 116- 127.
2	谢晓帆, 何小波, 刘波, 等. 高比例新能源下灵活性资源跨区博弈优化调度方法[J]. 电力科学与技术学报, 2025, 40 (6): 250- 259.
	XIE Xiaofan, HE Xiaobo, LIU Bo, et al. Optimal scheduling method of cross-regional game of flexible resources underhigh proportion of renewable energy[J]. Journal of Electric Power Science and Technology, 2025, 40 (6): 250- 259.
3	唐斐, 马秋波, 夏成璧, 等. 一种集中协调-实时分布控制的电网源-荷-储灵活性资源协调规划方法[J]. 电力信息与通信技术, 2025, 23 (11): 91- 96.
	TANG Fei, MA Qiubo, XIA Chengbi, et al. A centralized coordination real-time distributed control flexible resource coordination planning method for power grid source-load-storage[J]. Electric Power Information and Communication Technology, 2025, 23 (11): 91- 96.
4	窦嘉铭, 王小君, 司方远, 等. 基于深度强化学习的区域综合能源系统主动调节灵活性规则提取及可解释优化调度[J]. 电力系统保护与控制, 2025, 53 (23): 139- 151.
	DOU Jiaming, WANG Xiaojun, SI Fangyuan, et al. Extraction of active regulation flexibility rules and interpretable optimal scheduling forregional integrated energy systems based on deep reinforcement learning[J]. Power System Protection and Control, 2025, 53 (23): 139- 151.
5	许竞, 赵铁军, 高小刚, 等. 高比例新能源电力系统调节资源灵活性不足风险分析[J]. 中国电力, 2024, 57 (11): 129- 138.
	XU Jing, ZHAO Tiejun, GAO Xiaogang, et al. Risk analysis of insufficient flexibility from regulation resources in high proportion renewable energy power systems[J]. Electric Power, 2024, 57 (11): 129- 138.
6	任帅, 肖楚鹏, 梁新龙, 等. 计及虚拟电厂内需求侧灵活性资源的实时电价和V2G协调优化调度策略[J]. 电力信息与通信技术, 2024, 22 (8): 27- 36.
	REN Shuai, XIAO Chupeng, LIANG Xinlong, et al. The real-time electricity price and V2G coordinated optimal scheduling strategy considering demand-side flexibility resources in VPP[J]. Electric Power Information and Communication Technology, 2024, 22 (8): 27- 36.
7	孙东磊, 王宪, 孙毅, 等. 基于多面体不确定集合的电力系统灵活性量化评估方法[J]. 中国电力, 2024, 57 (9): 146- 155.
	SUN Donglei, WANG Xian, SUN Yi, et al. Polyhedral uncertainty set based power system flexibility quantitative assessment[J]. Electric Power, 2024, 57 (9): 146- 155.
8	闫群民, 任效煜, 宋潇, 等. 考虑源荷不确定性的电力系统灵活调度策略[J]. 电力工程技术, 2025, 44 (2): 172- 184.
	YAN Qunmin, REN Xiaoyu, SONG Xiao, et al. Flexible scheduling strategy for power systems considering source-load uncertainty[J]. Electric Power Engineering Technology, 2025, 44 (2): 172- 184.
9	HUANG M D, CHANG J X, GUO A J, et al. Cascade hydropower stations optimal dispatch considering flexible margin in renewable energy power system[J]. Energy, 2023, 285, 129375.
10	张宏宇, 印永华, 申洪, 等. 大规模风电接入后的系统调峰充裕性评估[J]. 中国电机工程学报, 2011, 31 (22): 26- 31.
	ZHANG Hongyu, YIN Yonghua, SHEN Hong, et al. Peak-load regulating adequacy evaluation associated with large-scale wind power integration[J]. Proceedings of the CSEE, 2011, 31 (22): 26- 31.
11	年兰雨. 基于随机生产模拟的多微网储能容量优化配置与运行控制[D]. 北京: 北京交通大学, 2021.
	NIAN Lanyu. Optimal configuration and operation control of multi-microgrid energy storage capacity based on stochastic production simulation[D]. Beijing: Beijing Jiaotong University, 2021.
12	张东辉, 康重庆, 卢洵, 等. 高比例新能源系统中储能配置规模论证[J]. 南方电网技术, 2022, 16 (4): 3- 11.
	ZHANG Donghui, KANG Chongqing, LU Xun, et al. Demonstration on the scale of energy storage deployment in high-proportion new energy power system[J]. Southern Power System Technology, 2022, 16 (4): 3- 11.
13	杨策, 孙伟卿, 韩冬. 考虑新能源消纳能力的电力系统灵活性评估方法[J]. 电网技术, 2023, 47 (1): 338- 346.
	YANG Ce, SUN Weiqing, HAN Dong. Power system flexibility evaluation considering renewable energy accommodation[J]. Power System Technology, 2023, 47 (1): 338- 346.
14	刘纯, 屈姬贤, 石文辉. 基于随机生产模拟的新能源消纳能力评估方法[J]. 中国电机工程学报, 2020, 40 (10): 3134- 3143.
	LIU Chun, QU Jixian, SHI Wenhui. Evaluating method of ability of accommodating renewable energy based on probabilistic production simulation[J]. Proceedings of the CSEE, 2020, 40 (10): 3134- 3143.
15	孙瑞娟, Gayan ABEYNAYAKE, 穆清, 等. 基于通用生成函数的海上风电集电系统可靠性与经济性评估[J]. 电力系统自动化, 2022, 46 (5): 159- 170.
	SUN Ruijuan, ABEYNAYAKE Gayan, MU Qing, et al. Reliability and economic evaluation of offshore wind power collection system based on universal generating function[J]. Automation of Electric Power Systems, 2022, 46 (5): 159- 170.
16	王洪涛, 刘旭, 陈之栩, 等. 低碳背景下基于改进通用生成函数法的随机生产模拟[J]. 电网技术, 2013, 37 (3): 597- 603.
	WANG Hongtao, LIU Xu, CHEN Zhixu, et al. Power system probabilistic production simulation based on improved universal generating function methods in low-carbon context[J]. Power System Technology, 2013, 37 (3): 597- 603.
17	罗捷, 袁康龙, 钟杰峰, 等. 考虑新能源接入对系统调峰性能影响的随机生产模拟算法[J]. 电力系统保护与控制, 2019, 47 (8): 180- 187.
	LUO Jie, YUAN Kanglong, ZHONG Jiefeng, et al. Probabilistic production simulation algorithm considering new energy's impact on regulation[J]. Power System Protection and Control, 2019, 47 (8): 180- 187.
18	赵书强, 索璕, 许朝阳, 等. 考虑断面约束的多能源电力系统时序性生产模拟[J]. 电力自动化设备, 2021, 41 (7): 1- 6.
	ZHAO Shuqiang, SUO Xun, XU Zhaoyang, et al. Time series production simulation of multi-energy power system considering section constraints[J]. Electric Power Automation Equipment, 2021, 41 (7): 1- 6.
19	姜淼, 高赐威, 苏卫华. 基于等效电量函数法的含DG配电网接线方式可靠性分析[J]. 电网技术, 2014, 38 (8): 2200- 2206.
	JIANG Miao, GAO Ciwei, SU Weihua. Equivalent energy function based reliability consideration of connection modes of distributed networks containing distributed generations[J]. Power System Technology, 2014, 38 (8): 2200- 2206.
20	胡晓飞, 林洁, 郭瑞鹏, 等. 水火发电系统随机生产模拟的两阶段定区间等效电量函数法[J]. 电力自动化设备, 2017, 37 (11): 169- 175.
	HU Xiaofei, LIN Jie, GUO Ruipeng, et al. A two-stage fixed-range equivalent energy function method for probabilistic production simulation on hydro-thermal generation system[J]. Electric Power Automation Equipment, 2017, 37 (11): 169- 175.
21	刘俊磊, 刘新苗, 娄源媛, 等. 考虑源荷储不确定性的新型电力系统随机生产模拟方法[J]. 南方电网技术, 2024, 18 (6): 36- 42.
	LIU Junlei, LIU Xinmiao, LOU Yuanyuan, et al. Stochastic production simulation method for new power system considering uncertainty of source network load and storage[J]. Southern Power System Technology, 2024, 18 (6): 36- 42.
22	郭旭阳, 谢开贵, 胡博, 等. 计入光伏发电的电力系统分时段随机生产模拟[J]. 电网技术, 2013, 37 (6): 1499- 1505.
	GUO Xuyang, XIE Kaigui, HU Bo, et al. A time-interval based probabilistic production simulation of power system with grid-connected photovoltaic generation[J]. Power System Technology, 2013, 37 (6): 1499- 1505.
23	江润叶. 风光接入下电力系统不确定性研究综述[J]. 海峡科学, 2022, (9): 32- 36.
24	唐君毅, 丁碧薇, 杨琪. 考虑风电预测区间的电力系统灵活性评估方法[J]. 电气传动, 2023, 53 (7): 49- 55.
	TANG Junyi, DING Biwei, YANG Qi. Power system flexibility evaluation method considering wind power prediction interval[J]. Electric Drive, 2023, 53 (7): 49- 55.
25	张祥. 华中地区风电发展对水电调度模式的影响[J]. 水电与新能源, 2016, 30 (6): 70- 74.
	ZHANG Xiang. Influence of wind power development on hydropower dispatching mode in Central China[J]. Hydropower and New Energy, 2016, 30 (6): 70- 74.
26	张玉敏. 基于不同核函数的概率密度函数估计比较研究[D]. 保定: 河北大学, 2010.
	ZHANG Yumin. The comparative study of probability density function estimation based on the different kernel functions[D]. Baoding: Hebei University, 2010.
27	杨锡运, 关文渊, 刘玉奇, 等. 基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J]. 中国电机工程学报, 2015, 35 (S1): 146- 153.
	YANG Xiyun, GUAN Wenyuan, LIU Yuqi, et al. Prediction intervals forecasts of wind power based on PSO-KELM[J]. Proceedings of the CSEE, 2015, 35 (S1): 146- 153.
28	JEON J, TAYLOR J W. Using conditional kernel density estimation for wind power density forecasting[J]. Journal of the American Statistical Association, 2012, 107 (497): 66- 79.
29	林玉娟. 考虑时序特性的电力系统随机生产模拟[D]. 合肥: 合肥工业大学, 2017.
	LIN Yujuan. Probabilistic production simulation of power system considering time sequence characteristics[D]. Hefei: Hefei University of Technology, 2017.
30	张俊涛, 程春田, 于申, 等. 水电支撑新型电力系统灵活性研究进展、挑战与展望[J]. 中国电机工程学报, 2024, 44 (10): 3862- 3884, I0010.
	ZHANG Juntao, CHENG Chuntian, YU Shen, et al. Progress, challenges and prospects of research on hydropower supporting the flexibility of new power systems[J]. Proceedings of the CSEE, 2024, 44 (10): 3862- 3884, I0010.
31	刘姜伟, 陈亦平, 肖云鹏, 等. 水电参与电力市场研究综述[J]. 中国电力, 2025, 58 (6): 156- 171.
	LIU Jiangwei, CHEN Yiping, XIAO Yunpeng, et al. Research overview of hydropower participation in electricity market[J]. Electric Power, 2025, 58 (6): 156- 171.
32	ZHANG J T, CHENG C T, YU S, et al. Sharing hydropower flexibility in interconnected power systems: a case study for the China Southern Power Grid[J]. Applied Energy, 2021, 288, 116645.
33	王冰, 王楠, 田政, 等. 美国电化学储能产业政策分析及对我国储能产业发展的启示与建议[J]. 分布式能源, 2020, 5 (3): 23- 28.
	WANG Bing, WANG Nan, TIAN Zheng, et al. Policy analysis of electrochemical energy storage industry in United States and its enlightenment and suggestions for development of China's energy storage industry[J]. Distributed Energy, 2020, 5 (3): 23- 28.
34	姚梦, 荆朝霞. 基于随机生产模拟的储能参与方式对比分析[J]. 电气自动化, 2024, 46 (5): 8- 10.
	YAO Meng, JING Zhaoxia. Comparative analysis of energy storage participation methods based on stochastic production simulation[J]. Electrical Automation, 2024, 46 (5): 8- 10.
35	SOLEIMANPOUR N, MOHAMMADI M. Probabilistic load flow by using nonparametric density estimators[J]. IEEE Transactions on Power Systems, 2013, 28 (4): 3747- 3755.

出力区间	向上爬坡率	向下爬坡率
$ \displaystyle\sum_{i=1}^{N}{P}_{\text{Gmin}}(i) $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{up}}\left(i\right) $	0
$ \left(\displaystyle\sum_{i=1}^{N}{P}_{\text{Gmin}}\left(i\right)\right.,\left.\displaystyle\sum_{i=2}^{N}{P}_{\text{Gmin}}\left(i\right)+{P}_{\text{Gmax}}\left(1\right)\right] $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{up}}\left(i\right) $	$ {r}_{\text{down}}\left(1\right) $
$ \vdots $	$ \vdots $	$ \vdots $
$ \left(\displaystyle\sum_{i=n}^{N}{P}_{\text{Gmin}}\left(i\right)+\displaystyle\sum_{i=n}^{N-1}{P}_{\text{Gmax}}\left(i\right),\right. $ $ \left.\displaystyle\sum_{i=n+1}^{N}{P}_{\text{Gmin}}\left(i\right)+\displaystyle\sum_{i=1}^{n}{P}_{\text{Gmax}}\left(i\right)\right] $	$ \displaystyle\sum_{i=n}^{N}{r}_{\text{up}}\left(i\right) $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{down}}\left(i\right) $
$ \vdots $	$ \vdots $	$ \vdots $
$ \left({P}_{\text{Gmin}}\left(N\right)+\displaystyle\sum_{i=1}^{N-1}{P}_{\text{Gmax}}\left(i\right),\right. \left.\displaystyle\sum_{i=1}^{N}{P}_{\text{Gmax}}\left(i\right)\right) $	$ {r}_{\text{up}}\left(N\right) $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{down}}\left(i\right) $
$ \displaystyle\sum_{i=1}^{N}{P}_{\text{Gmax}} $	0	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{down}}\left(i\right) $

出力区间	向上爬坡率	向下爬坡率
$ \displaystyle\sum_{i=1}^{N}{P}_{\text{Gmin}}(i) $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{up}}\left(i\right) $	0
$ \left(\displaystyle\sum_{i=1}^{N}{P}_{\text{Gmin}}\left(i\right)\right.,\left.\displaystyle\sum_{i=2}^{N}{P}_{\text{Gmin}}\left(i\right)+{P}_{\text{Gmax}}\left(1\right)\right] $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{up}}\left(i\right) $	$ {r}_{\text{down}}\left(1\right) $
$ \vdots $	$ \vdots $	$ \vdots $
$ \left(\displaystyle\sum_{i=n}^{N}{P}_{\text{Gmin}}\left(i\right)+\displaystyle\sum_{i=n}^{N-1}{P}_{\text{Gmax}}\left(i\right),\right. $ $ \left.\displaystyle\sum_{i=n+1}^{N}{P}_{\text{Gmin}}\left(i\right)+\displaystyle\sum_{i=1}^{n}{P}_{\text{Gmax}}\left(i\right)\right] $	$ \displaystyle\sum_{i=n}^{N}{r}_{\text{up}}\left(i\right) $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{down}}\left(i\right) $
$ \vdots $	$ \vdots $	$ \vdots $
$ \left({P}_{\text{Gmin}}\left(N\right)+\displaystyle\sum_{i=1}^{N-1}{P}_{\text{Gmax}}\left(i\right),\right. \left.\displaystyle\sum_{i=1}^{N}{P}_{\text{Gmax}}\left(i\right)\right) $	$ {r}_{\text{up}}\left(N\right) $	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{down}}\left(i\right) $
$ \displaystyle\sum_{i=1}^{N}{P}_{\text{Gmax}} $	0	$ \displaystyle\sum_{i=1}^{N}{r}_{\text{down}}\left(i\right) $

类型	台数	额定功率/ 万kW	最小技术出力/万kW	故障率/ %	爬坡速率/ ((万kW)·h^–1)
1	10	100	55.0	0.49	28.6
2	10	90	49.5	0.49	25.7
3	10	48	26.0	0.63	26.0
4	10	30	10.0	0.86	15.0

类型	台数	额定功率/ 万kW	最小技术出力/万kW	故障率/ %	爬坡速率/ ((万kW)·h^–1)
1	10	100	55.0	0.49	28.6
2	10	90	49.5	0.49	25.7
3	10	48	26.0	0.63	26.0
4	10	30	10.0	0.86	15.0

置信度/%	风电	光伏	径流式水电	负荷
85	80.40	82.80	83.20	85.70
90	84.30	88.60	87.70	88.80
95	89.00	93.70	92.20	94.70