新型电力系统中气象服务的现状、挑战及未来发展方向

邓力源; 王皓怀; 王凌梓; 沈海波

doi:10.16516/j.ceec.2025-248

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

新型电力系统中气象服务的现状、挑战及未来发展方向

极端气候事件下的能源与交通韧性 | 更新时间：2026-10-14

- 新型电力系统中气象服务的现状、挑战及未来发展方向
- Meteorological Services in the New Power System: Status, Challenges, and Future Directions
- 南方能源建设 2026年13卷第3期页码：1-19
- 作者机构：
  
  中国南方电力调度控制中心,广东,广州,510663
- 作者简介：
  
  王凌梓，ORCID: 0009-0008-6426-292X1991-，女，高级工程师，硕士，主要从事电网气象业务工作（e-mail）wanglz2@csg.cn
- 基金信息：
  
  南方电网公司软课题项目“能源安全与气象预警协同发展研究”（000000RT25061015）
- DOI：10.16516/j.ceec.2025-248
  中图分类号： TM7;P49
- 纸质出版：2026
- 稿件说明：
移动端阅览
邓力源, 王皓怀, 王凌梓, 等. 新型电力系统中气象服务的现状、挑战及未来发展方向[J]. 南方能源建设, 2026, 13(3): 1-19.

.
邓力源, 王皓怀, 王凌梓, 等. 新型电力系统中气象服务的现状、挑战及未来发展方向[J]. 南方能源建设, 2026, 13(3): 1-19. DOI： 10.16516/j.ceec.2025-248.

. DOI： 10.16516/j.ceec.2025-248.

摘要

目的

随着新型电力系统建设的不断深入，气象服务广泛渗透至源荷预测、运行调度、灾害预警、电力交易等电网运行的不同环节，亟待通过系统性梳理电力气象服务的应用现状与问题，明确其未来发展方向。

方法

文章通过文献综述与系统性分析，重点梳理电力气象监测、电力气象灾害预警、电力负荷预测与新能源功率预测等环节的应用现状及电力气象服务现存问题，结合前沿发展趋势对关键问题进行剖析，对未来电力气象服务进行展望。

结果

研究发现，随着新型电力系统的不断发展，电力气象服务呈现多元性的特征，并已成为提升电力系统安全、效率和智能化水平的关键支撑。然而，现阶段仍存在监测网络不完善、预报预警水平不足、数据不适配和电力市场服务模式单一等问题，制约了电力气象服务水平。

结论

未来电力气象服务应朝着更精细化、智能化、一体化的方向发展，通过电力和气象的深度融合应用，实现更精细化的气象监测和更精准的气象预报，提供更完善的能源保供服务，建立更健全的服务机制，为新型电力系统建设提供重要支撑。

Abstract

Objective

Driven by the goals of carbon peaking and carbon neutrality

the traditional power grid operation mode has undergone significant changes

gradually evolving into a new power system characterized by renewable energy as the main source

multi-energy complementarity

and coordinated interaction among generation

grid

load

and storage. In this context

the operation of the power grid is closely related to meteorological conditions. Meteorological services provide essential support for electric

ity demand and generation forecasting

operational dispatch

disaster early warning

and electricity market management. Therefore

it is urgent to systematically review the application status and challenges of power meteorological services and to clarify their future development directions.

Method

This study adopted literature review and systematic analysis approaches

focusing on the current applications of meteorological services in the power system

including meteorological monitoring

disaster early warning

load forecasting

and renewable energy power prediction. The existing problems of power meteorological services were summarized

and key issues were analyzed in light of frontier trends to provide prospects for future development.

Result

Research indicates that meteorological services have been increasingly integrated into multiple stages of the power system

including planning

operational dispatch

and disaster management

demonstrating diversified functionalities. These services have become critical for enhancing the reliability

efficiency

and intelligence of power operations. Nonetheless

challenges remain

such as incomplete monitoring networks

limited forecasting accuracy

data incompatibility

and restricted service models for electricity markets

which constrain overall performance.

Conclusion

This study proposes that future power meteorological services should focus on enhancing service capability and effectiveness

moving towards greater refinement

intelligence

and integration. Key priorities focus on advancing the deep integration of power systems and meteorology to refine meteorological monitoring

enhance forecasting accuracy

strengthen data interoperability

and improve energy supply assurance services. Establishing a more comprehensive service mechanism will provide critical support for the construction of the new power system.

关键词

Keywords

references

IPCC. Climate change 2022: mitigation of climate change [M]. Cambridge: Cambridge University Press, 2022. DOI: 10.1017/9781009157926.

XIN B A. New power system and new energy system [M]. Beijing: China Electric Power Press, 2023.

LI F R, YANG Y H, YIN Z D, et al. Development and application of UPQC to adapt to the access of high proportion of distributed energy resource [J]. Power electronics, 2024, 58(11): 72-76, 80. DOI: 10.20222/j.cnki.cn61-1124/tm.2024.11.014.

LI B B, FAN Y L, LI C, et al. Construction and application of low-carbon dispatching system based on "power meteorological one map" [J]. Automation application, 2024, 65(17): 108-110, 113. DOI: 10.19769/j.zdhy.2024.17.029.

LI J, XU T Q, LI Y, et al. Profitability study of multi-market coupled integrated renewable energy generation projects [J]. Power system protection and control, 2024, 52(6): 65-76. DOI: 10.19783/j.cnki.pspc.231210.

LIU J Z, WANG Q H, HU Y, et al. Connotation, characteristics and key technologies of new power systems [J]. New type power systems, 2023, 1(1): 49-65.

WANG W S, LIU C, QIN S Y, et al. Renewable energy grid connection and dispatch operation technology series [M]. Beijing: China Electric Power Press, 2020: 4.

ZHAO Y H, PENG G, YUE B P, et al. Research on correlation between wind-induced vibration of transmission line with span distribution and micrometeorological data [J]. Inner Mongolia electric power, 2025, 43(2): 70-76. DOI: 10.19929/j.cnki.nmgdljs.2025.0024.

LU J Z, PI X Y, ZHU S G, et al. Challenges and prospects in prevention and control of typical external disasters power generation and network equipments in the new type power systems [J]. New type power systems, 2024, 2(1): 36-51. DOI: 10.20121/j.2097-2784.ntps.230044.

JIANG F, LIN Z Y, WANG W Y, et al. Optimal Bagging ensemble ultra short term multi-energy load forecasting considering least average envelope entropy load decomposition [J]. Proceedings of the CSEE, 2024, 44(5): 1777-1788. DOI: 10.13334/j.0258-8013.pcsee.223470.

BIE Z H, LI G F. Risk assessment and resilience improvement of new power systems in extreme weather [J]. Journal of global energy interconnection, 2024, 7(1): 1-2. DOI: 10.19705/j.cnki.issn2096-5125.2024.01.001.

GAO H J, GUO M H, LIU J Y, et al. Power supply challenges and prospects in new power system from Sichuan electricity curtailment events caused by high-temperature drought weather [J]. Proceedings of the CSEE, 2023, 43(12): 4517-4537. DOI: 10.13334/j.0258-8013.pcsee.222971.

GUO J X, LI R K, CAI P L, et al. Risk in solar energy: spatio-temporal instability and extreme low-light events in China [J]. Applied energy, 2024, 359: 122749. DOI: 10.1016/j.apenergy.2024.122749.

LIU F, WANG X M, SUN F B, et al. Potential impact of global stilling on wind energy production in China [J]. Energy, 2023, 263: 125727. DOI: 10.1016/j.energy.2022.125727.

LI B J, FU X B, SHI K P, et al. Research on ultra short term combination prediction of wind power based on adversarial neural network [J]. Power electronics, 2025, 59(10): 89-95. DOI: 10.20222/j.cnki.cn61-1124/tm.20250704.008.

LI J L, YANG L S, LONG H Y. Climatic impacts on energy consumption: intensive and extensive margins [J]. Energy economics, 2018, 71: 332-343. DOI: 10.1016/j.eneco.2018.03.010.

WANG W S, XIE H B, YANG Z, et al. An introductory analysis of electric meteorology: basic concepts and research directions [J]. Proceedings of the CSEE, 2024, 44(18): 7440-7452. DOI: 10.13334/j.0258-8013.pcsee.241858.

PERERA A T D, HONG T Z. Vulnerability and resilience of urban energy ecosystems to extreme climate events: a systematic review and perspectives [J]. Renewable and sustainable energy reviews, 2023, 173: 113038. DOI: 10.1016/j.rser.2022.113038.

GONÇALVES A C R, COSTOYA X, NIETO R, et al. Extreme weather events on energy systems: a comprehensive review on impacts, mitigation, and adaptation measures [J]. Sustainable energy research, 2024, 11(1): 4. DOI: 10.1186/s40807-023-00097-6.

XU S D, WANG J, LI M Y, et al. Distribution network fault recovery strategy considering active control of an MESS in extreme weather [J]. Power system protection and control, 2024, 52(12): 45-57. DOI: 10.19783/j.cnki.pspc.231606.

HAN F J, WANG X H, QIAO J, et al. Review on artificial intelligence based load forecasting research for the new-type power system [J]. Proceedings of the CSEE, 2023, 43(22): 8569-8591. DOI: 10.13334/j.0258-8013.pcsee.221560.

SHI J, LI B J, LI Z S, et al. Research on multi-level load forecasting method for power system based on improved LSTM [J]. Power demand side management, 2025, 27(2): 62-67. DOI: 10.3969/j.issn.1009-1831.2025.02.010.

LI Z, YE L, DAI B H, et al. Ultra-short-term wind power prediction method based on IDSCNN-AM-LSTM combination neural network [J]. High voltage engineering, 2022, 48(6): 2117-2127. DOI: 10.13336/j.1003-6520.hve.20210557.

CHEN Z H, HE L W. Meteorological research and early warning services for wind turbine icing in wind farms [J]. Inner Mongolia electric power, 2025, 43(2): 3-11. DOI: 10.19929/j.cnki.nmgdljs.2025.0016.

HOU H, WAN Y, WANG Z G, et al. Wind and photovoltaic generation prediction bi-level model based on uncertainty scenarios under typhoon disaster [J]. Electric power construction, 2025, 46(3): 146-154. DOI: 10.12204/j.issn.1000-7229.2025.03.012.

WANG Z Y, LIU G Y, TANG Y C, et al. Collaborative optimization of "six elements" in modern power systems [J]. Distribution utilization, 2025, 42(7): 3-11, 22. DOI: 10.19421/j.cnki.1006-6357.2025.07.001.

ZHU Y C, CHEN C, HUANG L, et al. Current situation, challenges and countermeasures of meteorological service for carbon market construction [J]. Climate change research, 2025, 21(4): 555-564. DOI: 10.12006/j.issn.1673-1719.2024.310.

YUAN T J, ZHANG W D. A review of research on source-storage-load coordinated planning for new power system [J]. Proceedings of the CSEE, 2025, 45(17): 6664-6682. DOI: 10.13334/j.0258-8013.pcsee.240612.

LUO Y, YIN L L, FENG L M, et al. A review of CFD technology in wind resource assessment and future prospects [J]. Development innovation of machinery electrical products, 2025, 38(2): 167-170, 174. DOI: 10.3969/j.issn.1002-6673.2025.02.046.

ZHU F X, HUANG R L, GU P, et al. Research on wind energy resource assessment and power generation calculation of wind farms under complex terrain conditions [J]. China rural water and hydropower, 2025(12): 200-205. DOI: 10.12396/znsd.2500100.

HAN P, LIU T, WEI X, et al. Simulation and research on frequency modulation and voltage regulation characteristics of photovoltaic power station [J]. Power electronics, 2025, 59(3): 74-77. DOI: 10.20222/j.cnki.cn61-1124/tm.2025.03.011.

ZHUANG W B, QI C, WANG J, et al. Dynamic ice process estimation model of transmission line based on micrometeorological monitoring [J]. Power system protection and control, 2019, 47(14): 87-94. DOI: 10.7667/PSPC20191411.

LI L, LIU G J, ZHANG L, et al. Simulation of intelligent risk assessment method for extreme meteorological disasters on transmission lines [J]. Computer simulation, 2025, 42(6): 144-147, 158. DOI: 10.3969/j.issn.1006-9348.2025.06.024.

LENHARD R W. An indirect method for estimating the weight of glaze on wires [J]. Bulletin of the American meteorological society, 1955, 36(1): 1-5. DOI: 10.1175/1520-0477-36.1.1.

GOODWIN III E J, MOZER J D, DIGIOIA JR A M, et al. Predicting ice and snow loads for transmission line design [C]// Anon. Proceedings of the First International Workshop on Atmospheric Icing of Structures, Hannover, Germany, May 31, 1982. Cold Regions Research and Engineering Laboratory, 1983: 267-273.

CHAÎNÉ P M, CASTONGUAY G. New approach to radial ice thickness concept applied to bundle like conductors [R]. Toronto, Canada: Environment Canada, 1974.

MAKKONEN L. Heat transfer and icing of a rough cylinder [J]. Cold regions science and technology, 1985, 10(2): 105-116. DOI: 10.1016/0165-232X(85)90022-9.

XU Y X. Research on meteorological disaster warning and online monitoring networking method in transmission channel [D]. Baoding: North China Electric Power University, 2022. DOI: 10.27139/d.cnki.ghbdu.2022.000351.

ZHOU H L, ZHANG L Q, DONG X G, et al. Prediction of ice accretion on Qinling transmission lines integrating image recognition and numerical simulation [J]. Southern energy construction, 2026(1): 182-190. DOI: 10.16516/j.ceec.2025-073.

HAO Y P, ZHANG W X, LIANG W, et al. Mask R-CNN image recognition and segmentation of icing conductors in transmission lines [J]. Guangdong electric power, 2022, 35(2): 1-10. DOI: 10.3969/j.issn.1007-290X.2022.002.001.

ZHANG M, ZHAO G F, WANG L L, et al. Wind-induced coupling vibration effects of high-voltage transmission tower-line systems [J]. Shock and vibration, 2017, 2017: 1205976. DOI: 10.1155/2017/1205976.

YU J B, ZHUO Y, ZHANG J Y, et al. Analysis of wind vibration response and dynamic instability of large crossing transmission tower line system between islands [J]. Shandong electric power, 2024, 51(1): 1-10, 23. DOI: 10.20097/j.cnki.issn1007-9904.2024.01.001.

ZHU C Z, JIANG K H, JIANG W D, et al. Overview of fault prediction technology of power grid transmission equipment under typhoon disaster [J]. High voltage apparatus, 2025, 61(5): 10-19. DOI: 10.13296/j.1001-1609.hva.2025.05.002.

CHEN Z Q. Research on key technology of risk assessment of wind-bias tripping of transmission lines under typhoon condition [D]. Guangzhou: South China University of Technology, 2024. DOI: 10.27151/d.cnki.ghnlu.2024.004621.

HUANG S Y, MO S Z. Evaluation and analysis of meteorological service benefits in the electric power industry [C]//Proceedings of the 26th Annual Meeting of the Chinese Meteorological Society, Sub-Session on Meteorological Disasters and Social Harmony, Hangzhou, China, October 14, 2009. Beijing: Meteorological Disasters and Services Committee, China Meteorological Society, Forecasting and Disaster Reduction Department, China Meteorological Administration, National Meteorological Center, Public Meteorological Service Center, China Meteorological Administration, 2009: 154-159.

LI Z. Research on meteorological disaster risk management system of UHV transmission lines [D]. Baoding: North China Electric Power University, 2022. DOI: 10.27139/d.cnki.ghbdu.2022.000176.

LIU L Y, YANG X T, LONG C, et al. Study on the relationship between residential cooling load and temperature under extreme weather of summer [C]// Anon. 2023 Panda Forum on Power and Energy(PandaFPE), Chengdu, China, April 27-30, 2023. New York: IEEE, 2023: 2043-2048. DOI: 10.1109/PandaFPE57779.2023.10141456.

GAO C W, LI Q Y, SU W H, et al. Temperature correction model research considering temperature cumulative effect in short-term load forecasting [J]. Transactions of China electrotechnical society, 2015, 30(4): 242-248. DOI: 10.19595/j.cnki.1000-6753.tces.2015.04.030.

LI Y, YU J W, CAI X N, et al. Characteristics of electric loads in Nanjing and relationship between extreme loads and meteorological conditions in summer [J]. Meteorological science and technology, 2021, 49(4): 637-646. DOI: 10.19517/j.1671-6345.20200259.

WANG J D, DU C. Short-term load prediction model based on Attention-BiLSTM neural network and meteorological data correction [J]. Electric power automation equipment, 2022, 42(4): 172-177, 224. DOI: 10.16081/j.epae.202112017.

HU Y S, XIA X, DING Y, et al. A multivariate regression load forecasting algorithm based on factor and tendency analysis feedback [J]. Power demand side management, 2018, 20(6): 22-25, 35. DOI: 10.3969/j.issn.1009-1831.2018.06.006.

DAI M M, WANG K, LI Q, et al. Short-term load forecasting method based on weather classification and convolutional neural network [J]. Power demand side management, 2023, 25(3): 93-98. DOI: 10.3969/j.issn.1009-1831.2023.03.015.

SHEN F, LIU S R, XU X Y, et al. Multi-load short-term joint forecasting of integrated energy system based on multi-scale feature extraction [J]. High voltage engineering, 2024, 50(7): 2918-2930. DOI: 10.13336/j.1003-6520.hve.20240619.

GENG G C, HE Y. Short-term power load forecasting based on VMD-TCN-Attention mechanism [J]. Intelligent computer and applications, 2024, 14(7): 94-99. DOI: 10.20169/j.issn.2095-2163.240714.

LAI Z F. Research on photovoltaic and wind power forecasting based on spatial and temporal dependencies [D]. Hefei: University of Science and Technology of China, 2024. DOI: 10.27517/d.cnki.gzkju.2024.000431.

ZHOU Y, YUAN H B, ZHENG F Y, et al. Short-term prediction method and device for power regional load: 202011083600.0 [P]. 2021-01-15.

ZHANG C, LIU K, CUI H, et al. Regional power load prediction method considering meteorological factors: 202411748609.7 [P]. 2025-04-01.

TAN J, LI M, ZHANG J C, et al. Power distribution network load long-term prediction method and system based on big data: 202510365903.8 [P]. 2025-08-22.

YANG M, LI M L, WANG B, et al. Review on technologies and applications of numerical weather prediction for new power system [J]. Automation of electric power systems, 2025, 49(17): 1-20. DOI: 10.7500/AEPS20240227001.

GONG Y F, LU Z X, QIAO Y, et al. An overview of photovoltaic energy system output forecasting technology [J]. Automation of electric power systems, 2016, 40(4): 140-151. DOI: 10.7500/AEPS20150711003.

VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need [C]// Anon. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, December 4-9, 2017. Red Hook: Curran Associates Inc. , 2017: 6000-6010.

ZHOU H Y, ZHANG S H, PENG J Q, et al. Informer: Beyond efficient transformer for long sequence time-series forecasting [C]//Proceedings of the 35th AAAI Conference on Artificial Intelligence, Online, February 2-9, 2021. Palo Alto: AAAI Press, 2021: 11106-11115. DOI: 10.1609/aaai.v35i12.17325.

WU H X, XU J H, WANG J M, et al. Autoformer: decomposition transformers with auto-correlation for long-term series forecasting [C]//Proceedings of the 35th International Conference on Neural Information Processing Systems, December 6-14, 2021. Red Hook: Curran Associates Inc. , 2021: 1717.

CAO Y S, LIU G, LUO D H, et al. Multi-timescale photovoltaic power forecasting using an improved stacking ensemble algorithm based LSTM-informer model [J]. Energy, 2023, 283: 128669. DOI: 10.1016/j.energy.2023.128669.

SI Z Y, YANG M, YU Y X, et al. Ultra-short-term photovoltaic power prediction method based on satellite image feature region positioning [J]. High voltage engineering, 2021, 47(4): 1214-1223. DOI: 10.13336/j.1003-6520.hve.20201803.

SI Z Y, YANG M, YU Y X, et al. Photovoltaic power forecast based on satellite images considering effects of solar position [J]. Applied energy, 2021, 302: 117514. DOI: 10.1016/j.apenergy.2021.117514.

SABZEHGAR R, AMIRHOSSEINI D Z, RASOULI M. Solar power forecast for a residential smart microgrid based on numerical weather predictions using artificial intelligence methods [J]. Journal of building engineering, 2020, 32: 101629. DOI: 10.1016/j.jobe.2020.101629.

ALANAZI M, MAHOOR M, KHODAEI A. Two-stage hybrid day-ahead solar forecasting [C]// Anon. 2017 North American Power Symposium (NAPS), Morgantown, WV, USA, September 17-19, 2017. New York: IEEE, 2017: 1-6. DOI: 10.1109/NAPS.2017.8107319.

MASSIDDA L, MARROCU M. Quantile regression post-processing of weather forecast for short-term solar power probabilistic forecasting [J]. Energies, 2018, 11(7): 1763. DOI: 10.3390/en11071763.

LOPES F M, CONCEIÇÃO R, SILVA H G, et al. Improved ECMWF forecasts of direct normal irradiance: a tool for better operational strategies in concentrating solar power plants [J]. Renewable energy, 2021, 163: 755-771. DOI: 10.1016/j.renene.2020.08.140.

WU W B, PENG M G. A data mining approach combining K -means clustering with bagging neural network for short-term wind power forecasting [J ] . IEEE internet of things journal, 2017, 4(4): 979-986. DOI: 10.1109/JIOT.2017.2677578.

SU H Y, HUANG C R. Enhanced wind generation forecast using robust ensemble learning [J]. IEEE transactions on smart grid, 2021, 12(1): 912-915. DOI: 10.1109/TSG.2020.3021578.

YU Y X, YANG M, HAN X S, et al. A regional wind power probabilistic forecast method based on deep quantile regression [J]. IEEE transactions on industry applications, 2021, 57(5): 4420-4427. DOI: 10.1109/TIA.2021.3086077.

DONG X C, SUN Y Y, LI Y, et al. Spatio-temporal convolutional network based power forecasting of multiple wind farms [J]. Journal of modern power systems and clean energy, 2022, 10(2): 388-398. DOI: 10.35833/MPCE.2020.000849.

LI M L, YANG M, YU Y X, et al. Short-term wind power forecast based on continuous conditional random field [J]. IEEE transactions on power systems, 2024, 39(1): 2185-2197. DOI: 10.1109/TPWRS.2023.3270662.

LI M L, YANG M, YU Y X, et al. A wind speed correction method based on modified hidden Markov model for enhancing wind power forecast [J]. IEEE transactions on industry applications, 2022, 58(1): 656-666. DOI: 10.1109/TIA.2021.3127145.

WU Y K, SU P E, WU T Y, et al. Probabilistic wind-power forecasting using weather ensemble models [J]. IEEE transactions on industry applications, 2018, 54(6): 5609-5620. DOI: 10.1109/TIA.2018.2858183.

TAYLOR J W, MCSHARRY P E, BUIZZA R. Wind power density forecasting using ensemble predictions and time series models [J]. IEEE transactions on energy conversion, 2009, 24(3): 775-782. DOI: 10.1109/TEC.2009.2025431.

SU H Y, HUANG C R. Enhanced wind generation forecast using robust ensemble learning [J]. IEEE transactions on smart grid, 2021, 12(1): 912-915. DOI: 10.1109/TSG.2020.3021578.

LI M L, YANG M, YU Y X, et al. Adaptive weighted combination approach for wind power forecast based on deep deterministic policy gradient method [J]. IEEE transactions on power systems, 2024, 39(2): 3075-3087. DOI: 10.1109/TPWRS.2023.3294839.

LIU H B. Deep learning-based photovoltaic prediction method and system implementation [D]. Beijing: North China University of Technology, 2025. DOI: 10.26926/d.cnki.gbfgu.2025.000376.

ZHANG Q. Research on short-term photovoltaic power prediction based on deep learning and its interpretability [D]. Changchun: Jilin University, 2025.

CHENG T L. Wind power forecasting method based on artificial intelligence and weather forecasting [J]. Electric power equipment management, 2024(22): 150-152.

ZHANG D D, SHAN L K, LIU T H. Review on the application of artificial intelligence in data mining and wind and photovoltaic power forecasting [J]. Integrated intelligent energy, 2025, 47(3): 32-46. DOI: 10.3969/j.issn.2097-0706.2025.03.004.

DUPRÉ A, DROBINSKI P, BADOSA J, et al. The economic value of wind energy nowcasting [J]. Energies, 2020, 13(20): 5266. DOI: 10.3390/en13205266.

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

极端天气对风电开发全过程的影响及应对策略

AI技术在电力系统发展中的应用与前景

气象因子相对危险度在电网用电负荷预测中的应用

基于双工质气体压缩储能的供气系统

高比例风光集群并网多时间尺度惯量协同与暂态稳定优化控制