- 个人中心
- 退出登录
EN Reader LoginJournal Login
- Home
- Journals
- Academic News
- EP News
- ELibrary
- Preprint
- Contact Us
Multi index fusion reliability evaluation method for virtual power plants considering the dynamic characteristics of energy storage
- Vol. 59, Issue 1, Pages: 33-43(2026)
- 作者机构：
1. 国网浙江省电力有限公司,浙江,杭州,310016
2. 国网浙江省电力有限公司营销服务中心,浙江,杭州,310016
3. 河海大学 电气与动力工程学院,江苏,南京,211100
- 作者简介：
- 基金信息：
DOI：10.11930/j.issn.1004-9649.202505077
CLC：-
Published Online：13 January 2026，
Published：13 January 2026
- 稿件说明：
移动端阅览
王辉东, 倪琳娜, 芦鹏飞, 程颖, 吴英俊. 考虑储能动态特性的虚拟电厂多指标融合可靠性评估方法[J]. 中国电力, 2026, 59(1): 33-43.
王辉东, 倪琳娜, 芦鹏飞, et al. Multi index fusion reliability evaluation method for virtual power plants considering the dynamic characteristics of energy storage[J]. 2026, 59(1): 33-43.
王辉东, 倪琳娜, 芦鹏飞, 程颖, 吴英俊. 考虑储能动态特性的虚拟电厂多指标融合可靠性评估方法[J]. 中国电力, 2026, 59(1): 33-43. DOI： 10.11930/j.issn.1004-9649.202505077.
王辉东, 倪琳娜, 芦鹏飞, et al. Multi index fusion reliability evaluation method for virtual power plants considering the dynamic characteristics of energy storage[J]. 2026, 59(1): 33-43. DOI： 10.11930/j.issn.1004-9649.202505077.
摘要
虚拟电厂（virtual power plant，VPP）作为聚合“源-储-荷”多元资源的核心枢纽，其可靠性评估对电力系统稳定运行与可再生能源消纳至关重要。当前评估方法存在两点局限：1）采用静态储能模型，难以反映设备动态特性；2）评估指标无法量化供电过程中的功率输出稳定性。为此，提出一种考虑储能动态特性的虚拟电厂多指标融合可靠性评估方法。首先，基于储能设备在充放电过程中的容量衰减与效率退化特性，构建动态储能模型；其次，整合风电、光伏、可调负荷及电网交易等多元能源，构建体现“源-储-荷”协同运行的虚拟电厂模型；最后，围绕供电稳定性与可再生能源消纳效率，提出平均输出功率稳定性（mean VPP export，MVPPE）和可再生能源未利用量（expected renewable energy not used，ERENU）指标，融合传统电力不足概率、期望缺供电量，建立考虑储能动态特性的虚拟电厂可靠性评估体系。仿真结果表明，该方法较传统静态储能模型可使MVPPE提升14.5%、ERENU降低88.7%，能更真实反映虚拟电厂的可靠性水平，为其规划、运行与优化提供科学依据。
Abstract
As a core hub for aggregating multi-source "generation-storage-load" resources
the reliability assessment of Virtual Power Plants (VPPs) is crucial for the stable operation of power systems and the absorption of renewable energy. Current assessment methods have two limitations: first
they adopt static energy storage models
which struggle to reflect the dynamic characteristics of devices; second
the evaluation indicators fail to quantify the power output stability during the power supply process. To address this
a multi-indicator integrated reliability assessment method for virtual power plants considering the dynamic characteristics of energy storage is proposed. Firstly
a dynamic energy storage model is constructed based on the capacity degradation and efficiency deterioration characteristics of energy storage devices during charging and discharging processes. Secondly
multiple energy sources such as wind power
photovoltaic power
adjustable loads
and grid transactions are integrated to build a virtual power plant model that reflects the collaborative operation of "generation-storage-load". Finally
focusing on power supply stability and renewable energy absorption efficiency
two indicators—Mean VPP Export Power Stability (MVPPE) and Expected Renewable Energy Not Used (ERENU)—are proposed. Combined with traditional indicators including Loss of Load Probability (LOLP) and Expected Energy Not Supplied (EENS)
a reliability assessment system for virtual power plants considering the dynamic characteristics of energy storage is established. Simulation results show that compared with the traditional static energy storage model
this method increases MVPPE by 14.5% and reduces ERENU by 88.7%
which can more truly reflect the reliability level of VPPs and provide a scientific basis for their planning
operation
and optimization.
关键词
Keywords
references
1刘刚, 田志浩, 黄颖峰, 等. 考虑虚拟电厂的电力系统一致性经济调度方法[J]. 中国电力, 2025, 58 (6): 105- 111.
LIU Gang, TIAN Zhihao, HUANG Yingfeng, et al. Consistency economic dispatch method for power systems considering virtual power plants[J]. Electric Power, 2025, 58 (6): 105- 111.
2魏春晖, 单林森, 胡大栋, 等. 面向需求响应的园区虚拟电厂优化调度策略[J]. 中国电力, 2025, 58 (6): 112- 121.
WEI Chunhui, SHAN Linsen, HU Dadong, et al. Optimal scheduling strategy of park-level virtual power plant for demand response[J]. Electric Power, 2025, 58 (6): 112- 121.
3王宇绅, 陈皓勇, 黄宇翔, 等. 多重不确定性下的虚拟电厂参与电能量和需求响应市场鲁棒优化调度策略[J]. 发电技术, 2024, 45 (6): 1173- 1185.
WANG Yushen, CHEN Haoyong, HUANG Yuxiang, et al. Robust optimal scheduling strategy for virtual power plant participation in electric energy and demand response markets under multiple uncertainties[J]. Power Generation Technology, 2024, 45 (6): 1173- 1185.
4李佳蔚, 张冠宇. 大规模分布式新能源接入对省级电网稳定性影响[J]. 中国电力, 2024, 57 (6): 174- 180.
LI Jiawei, ZHANG Guanyu. Impact of large scale distributed new energy access on provincial power grid stability[J]. Electric Power, 2024, 57 (6): 174- 180.
5段梅梅, 程含渺, 方凯杰, 等. 基于分时电碳耦合定价的虚拟电厂主从博弈策略[J]. 电测与仪表, 2024, 61 (11): 1- 11.
DUAN Meimei, CHENG Hanmiao, FANG Kaijie, et al. Research on Stackelberg game strategy for virtual power plant considering time-of-use electricity and carbon coupled price[J]. Electrical Measurement & Instrumentation, 2024, 61 (11): 1- 11.
6许星原, 陈皓勇, 黄宇翔, 等. 虚拟电厂市场化交易中的挑战、策略与关键技术[J]. 发电技术, 2023, 44 (6): 745- 757.
XU Xingyuan, CHEN Haoyong, HUANG Yuxiang, et al. Challenges, strategies and key technologies for virtual power plants in market trading[J]. Power Generation Technology, 2023, 44 (6): 745- 757.
7钟永洁, 汤成俊, 王紫东, 等. 我国虚拟电厂的发展演进和关键技术及难点分析[J]. 浙江电力, 2025, 44 (2): 13- 31.
ZHONG Yongjie, TANG Chengjun, WANG Zidong, et al. Analysis of the evolution, key technologies, and challenges of virtual power plants in China[J]. Zhejiang Electric Power, 2025, 44 (2): 13- 31.
9刘铠诚, 贾晓强, 何桂雄, 等. 计及虚拟储能的冷热电综合能源系统优化调度方法[J]. 浙江电力, 2024, 43 (12): 38- 48.
LIU Kaicheng, JIA Xiaoqiang, HE Guixiong, et al. An optimal scheduling method for IES considering virtual energy storage[J]. Zhejiang Electric Power, 2024, 43 (12): 38- 48.
10王尚斌, 苑丽伟, 方莹, 等. 计及新能源波动的分布式储能分层控制策略[J]. 山东电力技术, 2024, 51 (9): 74- 84.
WANG Shangbin, YUAN Liwei, FANG Ying, et al. Distributed energy storage layered control strategy considering renewable energy fluctuations[J]. Shandong Electric Power, 2024, 51 (9): 74- 84.
11葛磊蛟, 郑轶文, 李小平, 等. 高比例分布式光伏接入下配电网多类型储能优化配置技术综述[J]. 浙江电力, 2025, 44 (5): 1- 11.
GE Leijiao, ZHENG Yiwen, LI Xiaoping, et al. A review of optimal allocation technologies for multi-type energy storage in distribu-tion networks with high penetration of DPV[J]. Zhejiang Electric Power, 2025, 44 (5): 1- 11.
12丁杨, 唐培全, 张文奎, 等. 考虑涉网一次调频考核要求面向风储联合系统收益优化的储能容量配置[J]. 山东电力技术, 2025, 52 (2): 46- 54.
DING Yang, TANG Peiquan, ZHANG Wenkui, et al. Optimal allocation of energy storage for profit optimization in wind-storage systems considering primary frequency regulation examinations[J]. Shandong Electric Power, 2025, 52 (2): 46- 54.
13李志浩, 刘鑫, 倪筹帷, 等. 基于场景集的虚拟电厂中储能系统优化配置研究[J]. 电气传动, 2021, 51 (16): 65- 71.
LI Zhihao, LIU Xin, NI Chouwei, et al. Research on optimal configuration of energy storage system in virtual power plant based on scene set[J]. Electric Drive, 2021, 51 (16): 65- 71.
14唐福斌. 基于混合储能的虚拟电厂热电联合优化调度[D]. 北京: 华北电力大学, 2021.
TANG Fubin. Optimal scheduling of combined heat and power of virtual power plant based on hybrid energy storage[D]. Beijing: North China Electric Power University, 2021.
16吴仁泽, 周文亮, 汤士明. 虚拟电厂中的储能技术应用[J]. 电子技术, 2024, 53 (11): 212- 213.
WU Renze, ZHOU Wenliang, TANG Shiming. Application of energy storage technology in virtual power plants[J]. Electronic Technology, 2024, 53 (11): 212- 213.
17杨珂, 王栋, 李达, 等. 虚拟电厂网络安全风险评估指标体系构建及量化计算[J]. 中国电力, 2024, 57 (8): 130- 137.
YANG Ke, WANG Dong, LI Da, et al. Network security risk assessment index system and calculation for virtual power plant[J]. Electric Power, 2024, 57 (8): 130- 137.
18胡嘉诚, 张宁, 曹雨桐, 等. 分布式能源接入下虚拟电厂负荷优化调度决策[J]. 综合智慧能源, 2025, 47 (1): 62- 69.
HU Jiacheng, ZHANG Ning, CAO Yutong, et al. Load optimization scheduling decision for virtual power plants with distributed energy accessed[J]. Integrated Intelligent Energy, 2025, 47 (1): 62- 69.
19王丽晓, 李嘉琪, 晏二保, 等. 资源聚合型虚拟电厂电-冷-柔耦合需求响应策略[J]. 广东电力, 2024, 37 (12): 119- 128.
WANG Lixiao, LI Jiaqi, YAN Erbao, et al. Electric cooling and flexibility joint demand response strategies for resources aggregated virtual power plants[J]. Guangdong Electric Power, 2024, 37 (12): 119- 128.
20谭洪, 杨彪, 张延旭, 等. 虚拟电厂设计与运营[M]. 北京: 中国电力出版社, 2025.
21王桂兰, 卓怀宇, 卢建刚, 等. 计及信誉值的虚拟电厂多主体合作博弈交易方法[J]. 广东电力, 2024, 37 (8): 26- 34.
WANG Guilan, ZHUO Huaiyu, LU Jiangang, et al. Multi-agent cooperative game trading method for virtual power plants considering reputation value[J]. Guangdong Electric Power, 2024, 37 (8): 26- 34.
22李明扬, 董哲. 含分布式新能源和需求响应负荷的虚拟电厂定价机制及优化调度[J]. 综合智慧能源, 2024, 46 (10): 12- 17.
LI Mingyang, DONG Zhe. Pricing mechanism and optimal scheduling of virtual power plants containing distributed renewable energy and demand response loads[J]. Integrated Intelligent Energy, 2024, 46 (10): 12- 17.
23葛少云, 王浩鸣. 基于系统状态转移抽样的含分布式电源配电网可靠性评估[J]. 电力系统自动化, 2013, 37 (2): 28- 35.
GE Shaoyun, WANG Haoming. Reliability evaluation of distribution networks including distributed generations based on system state transition sampling[J]. Automation of Electric Power Systems, 2013, 37 (2): 28- 35.
24石敏, 周步祥, 张冰, 等. 计及分布式电源不确定性的配电网系统可靠性评估[J]. 电力系统及其自动化学报, 2017, 29 (12): 114- 120.
SHI Min, ZHOU Buxiang, ZHANG Bing, et al. Reliability assessment of distribution network system considering the output uncertainties of distributed generations[J]. Proceedings of the CSU-EPSA, 2017, 29 (12): 114- 120.
25龚剑波. 基于GO法的计及分布式电源的配电系统可靠性研究[D]. 杭州: 浙江大学, 2012.
GONG Jianbo. Distribution system reliability research based on GO methodology considering the impacts of distributed generation[D]. Hangzhou: Zhejiang University, 2012.
26王蕾. 分布式电源建模及其对配电网可靠性的影响研究[D]. 合肥: 合肥工业大学, 2015.
WANG Lei. Research on modeling of distributed generation and effect on distribution system reliability[D]. Hefei: Hefei University of Technology, 2015.
27陶若冰, 徐红丽. 虚拟电厂供电可靠性管理策略研究及评估[J]. 电力需求侧管理, 2022, 24 (4): 36- 41.
TAO Ruobing, XU Hongli. Power supply reliability management strategies for virtual power plant[J]. Power Demand Side Management, 2022, 24 (4): 36- 41.
28蒋明轩, 李更丰, 卞艺衡, 等. 计及气/热惯性和延时性的综合能源系统可靠性评估方法[J]. 电力自动化设备, 2025, 45 (5): 19- 30, 39.
JIANG Mingxuan, LI Gengfeng, BIAN Yiheng, et al. Reliability evaluation method of integrated energy system considering gas/thermal inertia and time delay[J]. Electric Power Automation Equipment, 2025, 45 (5): 19- 30, 39.
30JAMSHIDI S, POURHOSSEIN K, ASADI M. Size estimation of wind/solar hybrid renewable energy systems without detailed wind and irradiation data: a feasibility study[J]. Energy Conversion and Management, 2021, 234, 113905.
31刘晓艳, 王珏, 姚铁锤, 等. 基于卫星遥感的超短期分布式光伏功率预测[J]. 电工技术学报, 2022, 37 (7): 1800- 1809.
LIU Xiaoyan, WANG Jue, YAO Tiechui, et al. Ultra short-term distributed photovoltaic power prediction based on satellite remote sensing[J]. Transactions of China Electrotechnical Society, 2022, 37 (7): 1800- 1809.
32周洋, 黄德志, 李培栋, 等. 考虑平衡端点相位不对称及光伏接入的低压配电网三相潮流模型[J]. 中国电力, 2024, 57 (10): 190- 198.
ZHOU Yang, HUANG Dezhi, LI Peidong, et al. A three-phase power flow model for low-voltage distribution networks considering balanced bus phase asymmetry and photovoltaic access[J]. Electric Power, 2024, 57 (10): 190- 198.
33杨雄, 李娟, 姜云龙, 等. 基于负荷准线重构的配电网需求侧资源集群优化调度方法[J]. 中国电力, 2025, 58 (10): 163- 170.
YANG Xiong, LI Juan, JIANG Yunlong, et al. An optimal scheduling method for demand-side resource clustering in distribution networks based on customer directrix load reconfiguration[J]. Electric Power, 2025, 58 (10): 163- 170.
34杨彪, 颜伟, 莫静山. 考虑源荷功率随机性和相关性的主导节点选择与无功分区方法[J]. 电力系统自动化, 2021, 45 (11): 61- 67.
YANG Biao, YAN Wei, MO Jingshan. Pilot-bus selection and network partitioning method considering randomness and correlation of source-load power[J]. Automation of Electric Power Systems, 2021, 45 (11): 61- 67.
35周煜, 吴海燕, 齐俊丽, 等. 源荷储一体化园区资源优化配置及其参与现货交易报价策略[J]. 湖南电力, 2024, 44 (3): 126- 135.
ZHOU Yu, WU Haiyan, QI Junli, et al. Optimized allocation of source-load-storage-integrated park resource and its offer strategy of participation in spot trading[J]. Hunan Electric Power, 2024, 44 (3): 126- 135.
36郭琦, 陈孟晓, 余佳微, 等. 考虑负荷灵活调节潜力的分布式能源系统能量管理策略[J]. 中国电力, 2025, 58 (8): 60- 68.
GUO Qi, CHEN Mengxiao, YU Jiawei, et al. Energy management strategy for distributed energy systems considering the regulation capability from flexible loads[J]. Electric Power, 2025, 58 (8): 60- 68.
37崔先迤, 曾伟杰, 崔泽政, 等. 考虑氢负荷需求的风光氢热联产型虚拟电厂日前调度优化运行策略[J]. 湖南电力, 2024, 44 (6): 1- 9.
CUI Xianyi, ZENG Weijie, CUI Zezheng, et al. Day-head scheduling optimal operation strategy for wind-solar-hydrogen-heat cogeneration virtual power plants considering hydrogen load requirements[J]. Hunan Electric Power, 2024, 44 (6): 1- 9.
0
Views
0
下载量
0
CSCD
- Meta-XMLDownload
- BibTeXDownload
- EndnoteDownload
- RefMan Download
- NoteExpressDownload
- NoteFirstDownload
Publicity Resources
Related Articles
Related Author
Related Institution
AI问答- Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19
京公网安备11010802024621 - It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
- Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.