考虑源荷不确定性的含先进绝热压缩空气储能综合能源型虚拟电厂两阶段随机鲁棒优化调度

韩爱明; 陈晓弢; 阿克清; 朱昱豪; 海昕; 陈来军

doi:10.19799/j.cnki.2095-4239.2026.0473

您当前的位置：

首页 >

文章列表页 >

考虑源荷不确定性的含先进绝热压缩空气储能综合能源型虚拟电厂两阶段随机鲁棒优化调度

储能系统与工程 | 更新时间：2026-09-18

- 考虑源荷不确定性的含先进绝热压缩空气储能综合能源型虚拟电厂两阶段随机鲁棒优化调度
- Two-stage stochastic-robust optimal scheduling of an integrated-energy virtual power plant with advanced adiabatic compressed air energy storage under source-load uncertainty
- 储能科学与技术 2026年15卷第9期页码：3692-3705
- 作者机构：
  
  青海大学能源与电气工程学院，青海西宁 810016
- 作者简介：
  
  韩爱明（2001—），男，硕士研究生，研究方向为压缩空气储能系统，E-mail：YS240858080516@qhu.edu.cn；
  陈晓弢，教授，研究方向为新型储能技术、综合能源系统优化调度，E-mail：chenxiaotao@qhu.edu.cn。
- 基金信息：
  
  国家自然科学基金联合基金项目(U22A20224)
- DOI：10.19799/j.cnki.2095-4239.2026.0473
  中图分类号： TK 02
- 收稿：2026-05-29，
  
  修回：2026-06-15，
  
  纸质出版：2026-09-28
- 稿件说明：
移动端阅览
韩爱明, 陈晓弢, 阿克清, 等. 考虑源荷不确定性的含先进绝热压缩空气储能综合能源型虚拟电厂两阶段随机鲁棒优化调度[J]. 储能科学与技术, 2026, 15(9): 3692-3705.

HAN Aiming, CHEN Xiaotao, A Keqing, et al. Two-stage stochastic-robust optimal scheduling of an integrated-energy virtual power plant with advanced adiabatic compressed air energy storage under source-load uncertainty[J]. Energy Storage Science and Technology, 2026, 15(9): 3692-3705.
韩爱明, 陈晓弢, 阿克清, 等. 考虑源荷不确定性的含先进绝热压缩空气储能综合能源型虚拟电厂两阶段随机鲁棒优化调度[J]. 储能科学与技术, 2026, 15(9): 3692-3705. DOI： 10.19799/j.cnki.2095-4239.2026.0473.

HAN Aiming, CHEN Xiaotao, A Keqing, et al. Two-stage stochastic-robust optimal scheduling of an integrated-energy virtual power plant with advanced adiabatic compressed air energy storage under source-load uncertainty[J]. Energy Storage Science and Technology, 2026, 15(9): 3692-3705. DOI： 10.19799/j.cnki.2095-4239.2026.0473.

摘要

针对高比例可再生能源接入下综合能源型虚拟电厂源荷双重不确定性问题，本方法构建了考虑电-热负荷随机波动和风光出力不利偏差的综合能源型虚拟电厂两阶段随机鲁棒优化模型。首先，采用拉丁超立方抽样与K-中心点（K-medoids）聚类生成电、热负荷典型场景，并以场景概率刻画荷侧随机性；其次，采用预算不确定集描述光伏和风电出力偏差，将源侧不确定性嵌入鲁棒优化框架；再次，以先进绝热压缩空气储能（advanced adiabatic compressed air energy storage，AA-CAES）作为综合能源型虚拟电厂的能量枢纽，建立含燃气轮机、电储能、储热罐及外部电网的电-热耦合功率平衡与状态演化模型；最后，利用列与约束生成（column-and-constraint generation，C&CG）算法将原问题分解为主问题和最坏场景子问题进行迭代求解。算例结果表明，在统一源荷测试场景下，当光伏和风电最坏下偏时段数分别为8和12时，该方法的平均总成本和测试最坏总成本较随机优化分别降低192.9元和513.1元，较同预算传统鲁棒优化分别降低59.9元和96.8元。其CVaR90总成本虽较同预算传统鲁棒优化高25.7元，但较随机优化降低507.5元。综合来看，本方法方法在平均运行经济性和极端场景抗风险能力方面表现更优，同时能够显著降低随机优化下的尾部风险，验证了所提两阶段随机鲁棒优化调度方法的有效性。

Abstract

To address the dual source-load uncertainties of integrated-energy virtual power plants (IEVPPs) under high penetration of renewable energy

this study develops a two-stage stochastic robust optimization model that accounts for IEVPP random electric and thermal load fluctuations alongside adverse photovoltaic and wind power output deviations. First

Latin hypercube sampling and K-medoids clustering are used to generate typical electric and thermal loads

after which load-side uncertainty is characterized via scenario probabilities. Second

a budgeted uncertainty set is adopted to describe photovoltaic and wind power output deviations

thereby incorporating source-side uncertainty into the robust optimization framework. Third

advanced adiabatic compressed air energy storage is utilized as the IEVPP energy hub

and an electric-thermal coupled power balance and state-evolution model is established

encompassing a gas turbine

electric energy storage

a thermal storage tank

and the external power grid. Finally

the column-and-constraint generation algorithm is employed to decompose the original problem into a master problem and a worst-case scenario subproblem for iterative solution.Case study results under unified source-load test scenarios indicate that setting the numbers of worst-case downward deviation periods for photovoltaic and wind power to 8 and 12

respectively

enables the proposed method to reduce the average total cost and worst-case total cost by 192.9 and 513.1 yuan

respectively (compared with stochastic optimization)

and by 59.9 and 96.8 yuan

respectively (compared with traditional robust optimization)

under the same uncertainty budget. Although its CVaR90 total cost is 25.7 yuan higher than that of traditional robust optimization under the same uncertainty budget

it achieves a substantial 507.5 yuan reduction compared with stochastic optimization. Overall

the proposed two-stage stochastic robust optimal scheduling method achieves better performance in terms of average operating economy and extreme-scenario risk resistance while significantly reducing the tail risk.

关键词

Keywords

references

张凯杰, 丁国锋, 闻铭, 等. 虚拟电厂的优化调度技术与市场机制设计综述[J]. 综合智慧能源, 2022, 44(2): 60-72.

ZHANG K J, DING G F, WEN M, et al. Review of optimal dispatching technology and market mechanism design for virtual power plants[J]. Integrated Intelligent Energy, 2022, 44(2): 60-72.

刘东, 樊强, 尤宏亮, 等. 泛在电力物联网下虚拟电厂的研究现状与展望[J]. 工程科学与技术, 2020, 52(4): 3-12. DOI:10.15961/j.jsuese.201901218.

LIU D, FAN Q, YOU H L, et al. Research status and trends of virtual power plants under electrical Internet of Things[J]. Journal of Sichuan University (Engineering Science Edition), 2020, 52(4): 3-12. DOI:10.15961/j.jsuese.201901218.

JU L W, TAN Z F, YUAN J Y, et al. A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind-photovoltaic-energy storage system considering the uncertainty and demand response[J]. Applied Energy, 2016, 171: 184-199. DOI:10.1016/j.apenergy.2016.03.020.

赵力航, 常伟光, 杨敏, 等. 电力市场环境下虚拟电厂两阶段能量经济优化调度[J]. 中国电力, 2022, 55(10): 14-22. DOI:10.11930/j.issn.1004-9649.202203042.

ZHAO L H, CHANG W G, YANG M, et al. Two-stage energy economic optimal dispatch of virtual power plant in deregulated electricity market[J]. Electric Power, 2022, 55(10): 14-22. DOI:10.11930/j.issn.1004-9649.202203042.

刘吉成, 冯淑贤, 宋亚楠, 等. 计及源荷不确定性的虚拟电厂优化调度模型[J]. 现代电力, 2025, 42(4): 799-809.

LIU J C, FENG S X, SONG Y N, et al. A scheduling model of virtual power plant considering source-load uncertainty[J]. Modern Electric Power, 2025, 42(4): 799-809.

蒋方帅, 曹俊波, 周志华, 等. 计及风险的分布式电源与储能虚拟电厂鲁棒优化调度[J]. 现代电力, 2023, 40(4): 570-576. DOI:10.19725/j.cnki.1007-2322.2021.0367.

JIANG F S, CAO J B, ZHOU Z H, et al. Robust optimal scheduling of distributed power and storage virtual power plants considering risks[J]. Modern Electric Power, 2023, 40(4): 570-576. DOI:10.19725/j.cnki.1007-2322.2021.0367.

赵冬梅, 王浩翔, 陶然. 计及风电-负荷不确定性的风-火-核-碳捕集多源协调优化调度[J]. 电工技术学报, 2022, 37(3): 707-718. DOI:10.19595/j.cnki.1000-6753.tces.201654.

ZHAO D M, WANG H X, TAO R. A multi-source coordinated optimal scheduling model considering wind-load uncertainty[J]. Transactions of China Electrotechnical Society, 2022, 37(3): 707-718. DOI:10.19595/j.cnki.1000-6753.tces.201654.

李勇, 姚天宇, 乔学博, 等. 基于联合时序场景和源网荷协同的分布式光伏与储能优化配置[J]. 电工技术学报, 2022, 37(13): 3289-3303. DOI:10.19595/j.cnki.1000-6753.tces.210712.

LI Y, YAO T Y, QIAO X B, et al. Optimal configuration of distributed photovoltaic and energy storage system based on joint sequential scenario and source-network-load coordination[J]. Transactions of China Electrotechnical Society, 2022, 37(13): 3289-3303. DOI:10.19595/j.cnki.1000-6753.tces.210712.

BERTSIMAS D, SIM M. The price of robustness[J]. Operations Research, 2004, 52(1): 35-53. DOI:10.1287/opre.1030.0065.

路清云. 基于新能源出力场景削减的多源协同控制策略[D]. 沈阳: 沈阳工业大学, 2023.

LU Q Y. Multi-source collaborative control strategy based on new energy output scenario reduction[D]. Shenyang: Shenyang University of Technology, 2023.

王华勇, 傅强, 徐昱, 等. 基于场景削减的网源协同规划研究[J]. 自动化仪表, 2021, 42(5): 69-74. DOI:10.16086/j.cnki.issn1000-0380.2020100002.

WANG H Y, FU Q, XU Y, et al. Research on generation and transmission expansion planning based on scenario reducing[J]. Process Automation Instrumentation, 2021, 42(5): 69-74. DOI:10.16086/j.cnki.issn1000-0380.2020100002.

ZENG B, ZHAO L. Solving two-stage robust optimization problems using a column-and-constraint generation method[J]. Operations Research Letters, 2013, 41(5): 457-461. DOI:10.1016/j.orl.2013.05.003.

尹斌鑫, 苗世洪, 李姚旺, 等. 考虑变寿命特性的先进绝热压缩空气储能电站容量规划模型[J]. 电工技术学报, 2020, 35(3): 612-622. DOI:10.19595/j.cnki.1000-6753.tces.190026.

YIN B X, MIAO S H, LI Y W, et al. A capacity planning model of advanced adiabatic compressed air energy storage plant considering lifetime varying characteristic[J]. Transactions of China Electrotechnical Society, 2020, 35(3): 612-622. DOI:10.19595/j.cnki.1000-6753.tces.190026.

李姚旺, 苗世洪, 罗星, 等. 含压缩空气储能电力系统日前-日内协调调度策略[J]. 中国电机工程学报, 2018, 38(10): 2849-2860. DOI:10.13334/j.0258-8013.pcsee.170777.

LI Y W, MIAO S H, LUO X, et al. Day-ahead and intra-day time scales coordinative dispatch strategy of power system with compressed air energy storage[J]. Proceedings of the CSEE, 2018, 38(10): 2849-2860. DOI:10.13334/j.0258-8013.pcsee. 170777.

闫文文, 文中, 李丹, 等. 计及含光热模块AA-CAES电站和碳捕集的综合能源系统低碳优化调度[J]. 电测与仪表, 2025, 62(6): 143-151. DOI:10.19753/j.issn1001-1390.2025.06.015.

YAN W W, WEN Z, LI D, et al. Low-carbon optimal scheduling of integrated energy system considering AA-CAES power plants and carbon capture with solar thermal modules[J]. Electrical Measurement & Instrumentation, 2025, 62(6): 143-151. DOI:10.19753/j.issn1001-1390.2025.06.015.

徐卫君, 张伟, 胡宇涛, 等. 先进绝热压缩空气储能多能流优化调度模型[J]. 电工技术学报, 2022, 37(23): 5944-5955. DOI:10.19595/j.cnki.1000-6753.tces.220772.

XU W J, ZHANG W, HU Y T, et al. Multi energy flow optimal scheduling model of advanced adiabatic compressed air energy storage[J]. Transactions of China Electrotechnical Society, 2022, 37(23): 5944-5955. DOI:10.19595/j.cnki.1000-6753.tces.220772.

BAI J Y, WEI W, CHEN L J, et al. Robust online operation of power systems with advanced adiabatic compressed air energy storage and renewable generation[J]. Journal of Energy Storage, 2022, 50: 104207. DOI:10.1016/j.est.2022.104207.

梅生伟, 李瑞, 陈来军, 等. 先进绝热压缩空气储能技术研究进展及展望[J]. 中国电机工程学报, 2018, 38(10): 2893-2907. DOI:10.13334/j.0258-8013.pcsee.172138.

MEI S W, LI R, CHEN L J, et al. An overview and outlook on advanced adiabatic compressed air energy storage technique[J]. Proceedings of the CSEE, 2018, 38(10): 2893-2907. DOI:10.13334/j.0258-8013.pcsee.172138.

蔡杰, 张世旭, 廖爽, 等. 考虑AA-CAES装置热电联储/供特性的微型综合能源系统优化运行策略[J]. 高电压技术, 2020, 46(2): 480-490.

CAI J, ZHANG S X, LIAO S, et al. Optimal operation strategy of micro-integrated energy system considering heat and power cogeneration characteristics of AA-CAES devices[J]. High Voltage Engineering, 2020, 46(2): 480-490.

徐青山, 李淋, 盛业宏, 等. 冷热电联供型多微网主动配电系统日前优化经济调度[J]. 电网技术, 2018, 42(6): 1726-1734. DOI:10.13335/j.1000-3673.pst.2018.0021.

XU Q S, LI L, SHENG Y H, et al. Day-ahead optimized economic dispatch of active distribution power system with combined cooling, heating and power-based microgrids[J]. Power System Technology, 2018, 42(6): 1726-1734. DOI:10.13335/j.1000-3673.pst.2018.0021.

汪洋叶, 赵力航, 常伟光, 等. 基于模型预测控制的虚拟电厂储能系统能量协同优化调控方法[J]. 智慧电力, 2021, 49(7): 16-22.

WANG Y Y, ZHAO L H, CHANG W G, et al. Model predictive control based energy collaborative optimization control method for energy storage system of virtual power plant[J]. Smart Power, 2021, 49(7): 16-22.

吴孟雪, 房方. 计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化[J]. 电工技术学报, 2023, 38(13): 3473-3485. DOI:10.19595/j.cnki.1000-6753.tces.220759.

WU M X, FANG F. Distributionally robust optimization of electricity-heat-hydrogen integrated energy system with wind and solar uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485. DOI:10.19595/j.cnki.1000-6753.tces.220759.

马恺珧. 不确定性环境下微电网优化调度研究[D]. 北京：中国科学院大学(中国科学院大学工程科学学院), 2024. DOI:10.27983/d.cnki.ggckx.2024.000103.

MA K Y. Research on optimal scheduling of microgrid under uncertainty[D]. Beijing:University of Chinese Academy of Sciences (School of Engineering Science), 2024. DOI:10.27983/d.cnki.ggckx.2024.000103.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

耦合光热发电储热-有机朗肯循环的先进绝热压缩空气储能系统热力学分析

计及先进绝热压缩空气储能系统热电协同的虚拟电厂多目标优化调度研究

不同PCM封装结构的相变水箱蓄放热性能研究

层状高熵氧化物的修饰改性策略、作用机理及储钠性能研究进展

基于物理参数辨识的燃料电池比例阀自适应标定筛选策略