基于动态分解组合预测模型的新政策下抽水蓄能收益测算

窦东; 樊伟

doi:10.19799/j.cnki.2095-4239.2026.0491

您当前的位置：

首页 >

文章列表页 >

基于动态分解组合预测模型的新政策下抽水蓄能收益测算

储能XXXX | 更新时间：2026-07-31

- 基于动态分解组合预测模型的新政策下抽水蓄能收益测算
- Benefit Calculation of Pumped Storage Under New Policy Based on Dynamic Decomposition Combined Forecasting Model
- 储能科学与技术 2026年页码：1-12
- 作者机构：
  
  1.内蒙古电力集团蒙电经济技术研究院有限责任公司，内蒙古自治区呼和浩特市 010020
  2.北京航空航天大学经济管理学院，北京 100191
- 作者简介：
  
  窦东（1994—），男，硕士研究生，经济师，从事电力技术经济、电力市场、储能研究，E-mail：1105965831@qq.com；
  樊伟，博士研究生，从事能源系统建模和电力系统规划研究，E-mail：2638413534@qq.com。
- 基金信息：
  
  国家自然科学基金青年科学基金项目(72404021)
- DOI：10.19799/j.cnki.2095-4239.2026.0491
  中图分类号： F 426.61
- 收稿：2026-06-09，
  
  修回：2026-07-03，
  
  网络首发：2026-07-30，
- 稿件说明：
移动端阅览
窦东, 樊伟. 基于动态分解组合预测模型的新政策下抽水蓄能收益测算[J]. 储能科学与技术, XXXX, XX(XX): 1-12.

DOU Dong, FAN Wei. Benefit Calculation of Pumped Storage Under New Policy Based on Dynamic Decomposition Combined Forecasting Model[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-12.
窦东, 樊伟. 基于动态分解组合预测模型的新政策下抽水蓄能收益测算[J]. 储能科学与技术, XXXX, XX(XX): 1-12. DOI： 10.19799/j.cnki.2095-4239.2026.0491.

DOU Dong, FAN Wei. Benefit Calculation of Pumped Storage Under New Policy Based on Dynamic Decomposition Combined Forecasting Model[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-12. DOI： 10.19799/j.cnki.2095-4239.2026.0491.

摘要

在电力系统新能源渗透率持续攀升的发展背景下，抽水蓄能是保障电网调峰、新能源消纳的关键储能载体。《关于完善发电侧容量电价机制的通知》新政落地重构了抽水蓄能原有盈利模式，在容量补偿基础上引导抽蓄全面参与电力现货、辅助服务市场拓宽收益来源。为量化新政落地后抽水蓄能现阶段与中长期收益变化规律，本文构建了融合动态分解电价预测技术的抽水蓄能全周期综合收益测算模型。首先依据新政计价规则建立省级统一容量补偿核算模型，以市场电价为驱动信号、全周期收益最优为目标搭建现货竞价优化运行模型，结合区域市场细则完善辅助服务收益核算公式，耦合形成多收益来源一体化经济评价体系。然后，结合分解算法与机器学习的优势，搭建基于动态分解的电力现货价格预测模型。最后，选取蒙西某1200MW装机抽水蓄能电站实证测算与敏感性分析。研究表明：现货市场模式可更加灵活地提升抽水蓄能电量收益空间，降低建设投资和提升电价预测准确率是提升收益能力的强敏感因素；测算预判未来5年蒙西区域现货电价呈逐年下行态势，抽水蓄能现货获利空间持续收窄，需通过优化调度策略、灵活参与多元市场对冲电价下行风险。

Abstract

Against the background of increasing penetration of renewable energy in power systems

pumped storage serves as a key flexible resource for peak regulation and new energy accommodation. The newly issued Notice on Improving the Capacity Pricing Mechanism on the Generation Side restructures the profit framework of pumped storage

enabling stations to gain extra revenue by participating in spot and ancillary service markets besides fixed capacity compensation. To quantify the short-term and medium-long term revenue of pumped storage under the new policy

this paper establishes an integrated revenue calculation framework combined with a dynamic decomposition price forecasting model. First

a provincial unified capacity compensation model is formulated in line with policy provisions; a spot market dispatch optimization model is constructed targeting maximum revenue guided by real-time electricity prices

together with an income calculation formula for ancillary services to form a comprehensive economic evaluation system. Second

a dynamic decomposition-based spot price forecasting model is developed by integrating decomposition algorithms and machine learning. Finally

a 1200 MW pumped storage station in Western Inner Mongolia is adopted for case calculation and sensitivity analysis. The results show that the spot market model can more flexibly expand the power generation revenue margin of pumped storage. Construction cost control and forecast accuracy of electricity prices are two dominant sensitive indicators for profitability. It is predicted that spot prices in Western Inner Mongolia will keep declining in the coming five years and the profit margin of pumped storage will shrink accordingly. Optimizing operational scheduling and diversifying market participation can effectively hedge the adverse impact of falling electricity prices.

关键词

Keywords

references

衣博文,许金华,范英.我国可再生能源配额制中长期目标的最优实现路径及对电力行业的影响分析[J].系统工程学报,2017,32(3):313-324.

YI Bowen, XU Jinhua, FAN Ying. Optimal realization path of medium and long-term objectives of renewable portfolio standard in China and its impact analysis on power industry[J]. Journal of Systems Engineering,2017,32(3):313-324.

国家发展改革委,国家能源局. 关于完善发电侧容量电价机制的通知[EB/OL]. [2026-01-30]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202601/t20260130_1403524.html.

国家发展改革委. 关于进一步完善抽水蓄能价格形成机制的意见[EB/OL]. [2021-05-07]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/202105/t20210507_1279341.html.

刘宗林. 新型电力系统下抽水蓄能电站经济性优化与可持续发展路径研究[J].水电与抽水蓄能,2025,11(5):94-98.

LIU Zonglin. Research on economic optimization and sustainable development path of pumped storage power station under new power system[J]. Hydropower and Pumped Storage,2025,11(5):94-98.

翟海燕,樊伟,张予燮,等. 抽水蓄能电站两部制电价机制研究——基于电量电价盈利与容量电价定价测算模型构建的分析[J].价格理论与实践,2022(9):178-182.

ZHAI Haiyan, FAN Wei, ZHANG Yuxie, et al. Research on two-part electricity price mechanism of pumped storage power station: Analysis based on construction of power price profit and capacity price calculation model[J]. Price:Theory & Practice,2022(9):178-182.

郑静,韩冬,周力. 抽水蓄能价格政策完善及市场化发展思考[J].水电与抽水蓄能,2025,11(1):38-43.

ZHENG Jing, HAN Dong, ZHOU Li. Thoughts on improving pumped storage price policy and market-oriented development[J]. Hydropower and Pumped Storage,2025,11(1):38-43.

王鹏,赵艺涵,张语珊,等. 面向受益对象的抽水蓄能容量成本分摊模型[J].广东电力,2025,38(5):42-53.

WANG Peng, ZHAO Yihan, ZHANG Yushan, et al. Capacity cost allocation model of pumped storage for beneficiaries[J]. Guangdong Electric Power,2025,38(5):42-53.

YAN Xin, GAO Bo, AN Chengzhi, et al. Coupled Capacity Allocation Mechanism for Distributed Hybrid Pumped Storage Resources[J]. IEEE Transactions on Smart Grid,2025.

刘金华,陈璐,刘凡骞,等. 风-光-抽水蓄能互补系统经济容量配置研究[J].供用电,2025,42(4):99-107.

LIU Jinhua, CHEN Lu, LIU Fanqian, et al. Research on economic capacity configuration of wind-solar-pumped storage complementary system[J]. Distribution & Utilization,2025,42(4):99-107.

李晓英,王静文,李慧娴,等. 现货联合市场抽水蓄能电站独立主体竞价策略[J].水利经济,2025,43(5):70-77.

LI Xiaoying, WANG Jingwen, LI Huixian, et al. Bidding strategy of independent pumped storage power station in joint spot market[J]. Journal of Water Economics,2025,43(5):70-77.

余晓鹏,李晓萌,张忠,等.抽蓄电站提供多时间尺度备用的优化策略[J].储能科学与技术,2022,11(2):573-582.

YU Xiaopeng, LI Xiaomeng, ZHANG Zhong, et al. Optimization strategy of pumped storage station providing multi-time scale reserve[J]. Energy Storage Science and Technology,2022,11(2):573-582.

樊伟,范英,谭忠富,等. 基于多层利益共享的虚拟电厂参与电碳市场分布鲁棒优化模型[J].系统工程理论与实践,2024,44(2):661-683.

FAN Wei, FAN Ying, TAN Zhongfu, et al. Distributed robust optimization model for virtual power plant participating in electricity-carbon market based on multi-layer benefit sharing[J]. Systems Engineering-Theory & Practice,2024,44(2):661-683.

丁红坚,辛诚,李沐阳,等. 考虑电力现货市场风险的抽水蓄能电站竞价策略[J].电力建设,2024,45(5):150-158.

DING Hongjian, XIN Cheng, LI Muyang, et al. Bidding strategy of pumped storage power station considering power spot market risk[J]. Electric Power Construction,2024,45(5):150-158.

李东伟,周子东,刘秋实,等. 基于电力市场化的抽水蓄能电站电价收益研究[J].人民长江,2024,55(3):243-248.

LI Dongwei, ZHOU Zidong, LIU Qiushi, et al. Research on electricity price income of pumped storage power station based on power marketization[J]. Yangtze River,2024,55(3):243-248.

马良,翟海燕,杨文婷,等. 现货市场下抽水蓄能电站电量收益及综合效益分析[J].水利水电技术(中英文),2023,54(S1):283-289.DOI:10.13928/j.cnki.wrahe.2023.S1.045.

MA Liang, ZHAI Haiyan, YANG Wenting, et al. Power generation revenue and comprehensive benefit analysis of pumped storage power station in spot market[J]. Water Resources and Hydropower Engineering,2023,54(S1):283-289.DOI:10.13928/j.cnki.wrahe.2023.S1.045.

余涛,刘小敏,俱鑫,等. 抽水蓄能电站参与电力现货市场竞价多日滚动优化运行方法[J].水力发电,2020,46(10):105-110.

YU Tao, LIU Xiaomin, JU Xin, et al. Multi-day rolling optimal operation method for pumped storage power station participating in power spot market bidding[J]. Water Power,2020,46(10):105-110.

俞耀文,陆宏智,赵勇,等. 电力中长期市场的双侧竞价机制设计[J].系统工程学报,2026,41(1):77-93.

YU Yaowen, LU Hongzhi, ZHAO Yong, et al. Design of two-sided bidding mechanism for medium and long-term power market[J]. Journal of Systems Engineering,2026,41(1):77-93.

ZHAI Haiyan, SUN Yang, TIAN Lin, et al. Optimization operation strategy of wind–pumped storage integrated system participation in spot market considering dual uncertainties[J]. Clean Energy,2024,8(6):175-186.

王雷,闫瑞涛,张凡,等.基于现货电能量市场与一次调频市场联合优化模型的独立储能项目经济性分析[J].储能科学与技术,2025,14(2):834-845.

WANG Lei, YAN Ruitao, ZHANG Fan, et al. Economic analysis of independent energy storage project based on joint optimization model of spot energy market and primary frequency regulation market[J]. Energy Storage Science and Technology,2025,14(2):834-845.

高天华,蒲勇健. 考虑价格风险管理的电力中长期竞价机制设计[J].系统工程学报,2026,41(1):15-27.

GAO Tianhua, PU Yongjian. Design of medium and long-term power bidding mechanism considering price risk management[J]. Journal of Systems Engineering,2026,41(1):15-27.

吴问足,王旭辉,卢苑,等. 基于分形理论的电力现货市场出清电价预测方法[J].中国电力,2025,58(9):183-193.

WU Wenzu, WANG Xuhui, LU Yuan, et al. Clearing electricity price forecasting method of power spot market based on fractal theory[J]. Electric Power,2025,58(9):183-193.

方逸飞,刘东,陈静,等. 考虑中长期市场因素的净现货竞价空间驱动的日前出清电价预测方法[J].中国电机工程学报,2026,46(2):506-519.

FANG Yifei, LIU Dong, CHEN Jing, et al. Day-ahead clearing price forecasting method driven by net spot bidding space considering medium and long-term market factors[J]. Proceedings of the CSEE,2026,46(2):506-519.

邵云姝,张琳,周乃康,等. 基于长短期记忆网络的电力市场价格预测研究[J].动力工程学报,2025,45(5):733-737.

SHAO Yunshu, ZHANG Lin, ZHOU Naikang, et al. Research on power market price prediction based on long short-term memory network[J]. Journal of Chinese Society of Power Engineering,2025,45(5):733-737.

艾雨,贾燕冰,韩肖清.基于相似日筛选与组合深度学习模型的日前电价预测方法[J].电网技术,2025,49(1):242-251.

AI Yu, JIA Yanbing, HAN Xiaoqing. Day-ahead electricity price forecasting method based on similar day screening and combined deep learning model[J]. Power System Technology,2025,49(1):242-251.

加鹤萍,郭宇辰,马乾鑫,等. 基于机器学习方法的现货电价预测研究综述[J].电力建设,2025,46(2):160-179.

JIA Heping, GUO Yuchen, MA Qian, et al. Review of spot electricity price forecasting based on machine learning methods[J]. Electric Power Construction,2025,46(2):160-179.

胥威汀,李婷,刘友波,等. 考虑电力市场交易影响的中长期负荷预测方法[J].电测与仪表,2017,54(2):100-104+108.

XU Weiting, LI Ting, LIU Youbo, et al. Medium and long term load forecasting method considering the influence of power market transactions[J]. Electrical Measurement & Instrumentation,2017,54(2):100-104+108.

陈晓红,欧阳长风,胡东滨,等.人工智能赋能电力系统管理的应用综述与展望[J].系统工程学报,2025,40(5):670-682.

CHEN Xiaohong, OUYANG Changfeng, HU Dongbin, et al. Application review and prospect of artificial intelligence empowering power system management[J]. Journal of Systems Engineering,2025,40(5):670-682.

RAHMAN M S, REZA H. Hybrid Deep Learning Approaches for Accurate Electricity Price Forecasting:A Day-Ahead US Energy Market Analysis with Renewable Energy[J]. Machine Learning and Knowledge Extraction,2025,7(4):120.

ZHUO Hao, WANG Yu, LI Shuai, et al. Enhancing Electricity Price Prediction Accuracy With an Attention Mechanism‐LSTM Hybrid Model[J]. Engineering Reports,2026,8(3):e70657.

樊伟,李旭东,王尧,等. 新型电力系统灵活性资源聚合两阶段调度优化模型[J].电力建设,2023,44(2):22-34.

FAN Wei, LI Xudong, WANG Yao, et al. Two-stage scheduling optimization model for flexible resource aggregation of new power system[J]. Electric Power Construction,2023,44(2):22-34.

王进,张粒子,赵志芳,等. 抽水蓄能电站市场化运行机制和日前市场出清模型[J].电力系统自动化,2023,47(12):145-153.

WANG Jin, ZHANG Lizi, ZHAO Zhifang, et al. Market-oriented operation mechanism and day-ahead market clearing model of pumped storage power station[J]. Automation of Electric Power Systems,2023,47(12):145-153.

柳洋,何永秀,李谟兴,等. 市场环境下抽水蓄能电站的价格市场衔接机制设计与效益评估[J].现代电力,2023,40(1):42-49.

LIU Yang, HE Yongxiu, LI Moxing, et al. Price market connection mechanism design and benefit evaluation of pumped storage power station under market environment[J]. Modern Electric Power,2023,40(1):42-49.

侯艺凡.«建设项目经济评价方法与参数»(第三版)修订建议[J].工程建设标准化,2026(3):79-90.

HOU Yifan. Revision suggestions for Economic Evaluation Methods and Parameters of Construction Projects (3rd Edition)[J]. Standardization of Engineering Construction,2026(3):79-90.

王博,袁佳鑫,叶雪,等.基于动态分解的电力现货价格预测建模方法研究[J].系统科学与数学,2025,45(8):2363-2375.

WANG Bo, YU Jiaxin, YE Xue, et al. Research on power spot price forecasting modeling method based on dynamic decomposition[J]. Journal of Systems Science and Mathematical Sciences,2025,45(8):2363-2375.

韩璟琳,冯喜春,胡平,等.基于Hyperopt-LightGBM的直流配电网短期负荷抗噪声预测[J].高电压技术,2024,50(11):4902-4911.

HAN Jinglin, FENG Xichun, HU Ping, et al. Anti-noise short-term load forecasting of DC distribution network based on Hyperopt-LightGBM[J]. High Voltage Engineering,2024,50(11):4902-4911.

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

重力储能技术研究进展及展望

能源转型下基于碳排放与新能源阶梯惩罚的抽水蓄能双层优化研究

基于完备集合经验模态分解的含抽蓄微电网混合储能容量优化配置

含高比例风光发电的电力系统中抽蓄电站的优化控制策略

储能电池对电力现货市场输电权交易的影响与应用