考虑光伏发电集群的系统电-碳耦合运营决策研究框架及展望

周红莲; 廖孟柯; 买买提艾力&#x000b7;吾甫尔; 崔勇; 郑建; 武文颖

doi:10.19912/j.0254-0096.tynxb.2024-1717

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (2) : 30-39. DOI: 10.19912/j.0254-0096.tynxb.2024-1717

考虑光伏发电集群的系统电-碳耦合运营决策研究框架及展望

周红莲¹, 廖孟柯¹, 买买提艾力·吾甫尔¹, 崔勇^2,3, 郑建³, 武文颖²

作者信息 +

RESEARCH FRAMEWORK AND PROSPECT OF SYSTEM ELECTRICITY-CARBON COUPLING OPERATION DECISION-MAKING CONSIDERING PHOTOVOLTAIC POWER GENERATION CLUSTER

Zhou Honglian¹, Liao Mengke¹, Maimaiti Aili·Wupuer¹, Cui Yong^2,3, Zheng Jian³, Wu Wenying²

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文章历史 +

摘要

在构建以新能源为主的新型电力系统背景下,为充分挖掘光伏发电大规模并网对电力系统节能降碳的支撑潜力,以更好发挥光伏电站统一管理对提升电力系统运营效率的积极作用,提出考虑光伏发电集群模式下的系统电-碳耦合运营决策机制,主要从3个方面进行方案设计及机理论述：一是考虑电-碳指标的光伏集群划分方法;二是构建光伏发电集群对于系统电-碳指标影响关键路径分析模型;三是构建光伏集群模式下的系统电-碳耦合运营效率决策体系。最后,对下一步的研究重点进行展望。基于所提方案可为分析光伏发电集群对于系统低碳运营效率的支撑作用机制奠定基础,并为进一步厘清各环节碳排放责任和系统运营效率管理的薄弱环节提供分析方法,同时可为系统运行方式调节及低碳调度决策提供思路。

Abstract

In the context of constructing a new-type power system dominated by renewable energy, this study introduces an electricity-carbon coupling operational decision-making mechanism for photovoltaic power generation clusters. The goal is to fully utilize the potential of large-scale grid-connected photovoltaic power generation in supporting energy conservation and carbon reduction, while also enhancing the operational efficiency of power systems through the unified management of photovoltaic power stations. The design and theoretical analysis are concentrated on three key aspects： 1） Photovoltaic cluster partitioning methods that consider electricity-carbon indicators; 2） The construction of a critical path analysis model for the impact of photovoltaic clusters on system electricity-carbon indicators; 3） The establishment of an efficiency decision-making framework for electricity-carbon coupling operations within photovoltaic cluster models. Finally, the paper outlines future research priorities. The proposed approach establishes a foundation for analyzing how photovoltaic clusters can support low-carbon operational efficiency, provides analytical methods to clarify carbon emission responsibilities across operational stages and identify management weaknesses, and offers a reference for system operation mode adjustments and low-carbon dispatch decision-making.

导出引用

周红莲, 廖孟柯, 买买提艾力·吾甫尔, 崔勇, 郑建, 武文颖. 考虑光伏发电集群的系统电-碳耦合运营决策研究框架及展望[J]. 太阳能学报. 2026, 47(2): 30-39 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1717

Zhou Honglian, Liao Mengke, Maimaiti Aili·Wupuer, Cui Yong, Zheng Jian, Wu Wenying. RESEARCH FRAMEWORK AND PROSPECT OF SYSTEM ELECTRICITY-CARBON COUPLING OPERATION DECISION-MAKING CONSIDERING PHOTOVOLTAIC POWER GENERATION CLUSTER[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 30-39 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1717

中图分类号： TM732

参考文献

[1] 王磊, 张帆, 寇凌峰, 等. 基于Fast Unfolding聚类算法的规模化分布式光伏电源集群划分[J]. 太阳能学报, 2021, 42(10): 29-34.
WANG L, ZHANG F,KOU L F, et al.Large-scale distributed PV cluster division based on fast unfolding clustering algorithm[J]. Acta energiae solaris sinica, 2021, 42(10): 29-34.
[2] 石博, 肖传亮, 彭克, 等. 基于集群划分的配电网网-源-储双层联合扩展规划策略[J]. 电力系统自动化, 2023,47(14): 43-51.
SHI B, XIAO C L, PENG K, et al.Two-layer joint expansion planning strategy of grid-source-storage for distribution network based on cluster partition[J]. Automation of electric power systems, 2023, 47(14): 43-51.
[3] 卢俊杰, 蔡涛, 郎建勋, 等. 基于集群划分的光伏电站集群发电功率短期预测方法[J]. 高电压技术, 2022, 48(5): 1943-1951.
LU J, CAI T, LANG J X, et al.Short-term power output forecasting of clustered photovoltaic solar plants based on cluster partition[J]. High voltage engineering, 2022, 48(5): 1943-1951.
[4] 麻吕斌, 潘国兵, 蒋群, 等. 基于EOF-DBSCAN-GRU的分布式光伏集群出力预测方法研究[J]. 太阳能学报, 2024, 45(1): 39-46.
MA L B, PAN G B, JIANG Q, et al.Research on distributed PV cluster power output forecasting method based on EOF-DBSCAN-GRU[J]. Acta energiae solaris sinica, 2024, 45(1): 39-46.
[5] 安源, 苏瑞, 郑申印, 等. 计及碳交易和源-荷侧资源的综合能源系统低碳经济优化[J]. 太阳能学报, 2023, 44(11): 547-555.
AN Y, SU R, ZHENG S Y, et al.Low carbon economic optimization of integrated energy system considering carbon trading and source-load side resources[J]. Acta energiae solaris sinica, 2023, 44(11): 547-555.
[6] CHENG Y H, ZHANG N, WANG Y, et al.Modeling carbon emission flow in multiple energy systems[J]. IEEE transactions on smart grid, 2019, 10(4): 3562-3574.
[7] JIN J L, WEN Q L, CHENG S Q, et al.Optimization of carbon emission reduction paths in the low-carbon power dispatching process[J]. Renewable energy, 2022, 188425-188436.
[8] 周天睿, 康重庆, 徐乾耀, 等. 电力系统碳排放流的计算方法初探[J]. 电力系统自动化, 2012, 36(11): 44-49.
ZHOU T R, KANG Z Q, XU Q Y, et al.Preliminary investigation on a method for carbon emission flow calculation of power system[J]. Automation of electric power systems, 2012, 36(11): 44-49.
[9] 汪超群, 陈懿, 文福拴, 等. 电力系统碳排放流理论改进与完善[J]. 电网技术, 2022, 46(5): 1683-1693.
WANG C Q, CHEN Y, WEN F S, et al.Improvement and Perfection of carbon emission flow theory in power system[J]. Power system technology, 2022, 46(5):1683-1693.
[10] 张自强, 申富泰, 魏军, 等. 含光伏不确定性的电力系统碳排放流分析[J]. 重庆理工大学学报(自然科学), 2023, 37(10): 270-278.
ZHANG Z Z, SHEN F T, WEI J, et al.Carbon emission flow analysis of the power system considering photovoltaic uncertainty[J]. Journal of Chongqing University of Technology (natural science), 2023, 37(10): 270-278.
[11] CUI Y, SHAO M Z, ZHOU X, et al.Research on supporting mechanism of ancillary service of PV system to grid energy efficiency based on multi-time and space-time operation[J]. Plos one, 2022, 17(5): e0268173.
[12] 卢锦玲, 颜禄涵, 腊志源, 等. 基于数字孪生与动态能效模型的综合能源系统实时优化调度策略[J]. 电网技术, 2023, 47(1): 226-238.
LU J L, YAN L H, LA Z Y, et al.Real-time optimal scheduling strategy for integrated energy system based on digital twins and dynamic energy efficiency model[J]. Power system technology, 2023, 47(1): 226-238.
[13] SUN X Z, QIU J, ZHAO J H.Optimal local volt/var control for photovoltaic inverters in active distribution networks[J]. IEEE transactions on power systems, 2021, 36(6): 5756-5766.
[14] 马健, 樊艳芳, 王一波, 等. 适用于集中型光伏直流升压变换器的MPPT策略[J]. 太阳能学报, 2022, 43(5): 137-145.
MA J, FAN Y F, WANG Y B, et al.Maximum power point tracking strategy for centralized photovoltaic DC-DC converter[J]. Acta energiae solaris sinica, 2022, 43(5): 137-145.
[15] 潘登. 分布式电源接入配电网运营效率测评与仿真研究[D]. 北京: 北京交通大学, 2014.
PAN D.Evaluation and simulation research on the operation efficiency of distributed generation connected to distribution network[D]. Beijing: Beijing Jiaotong University, 2014.
[16] 陈厚合, 茅文玲, 张儒峰, 等. 基于碳排放流理论的电力系统源-荷协调低碳优化调度[J]. 电力系统保护与控制, 2021, 49(10): 1-11.
CHEN H H, MAO W L, ZHANG R F, et al.Low-carbon optimal scheduling of a power system source-load considering coordination based on carbon emission flow theory[J]. Power system protection and control, 2021, 49(10): 1-11.
[17] 周天睿, 康重庆, 徐乾耀, 等. 电力系统碳排放流分析理论初探[J]. 电力系统自动化, 2012, 36(7): 38-43+85.
ZHOU T R, KANG C Q, XU Q Y, et al.Preliminary theoretical investigation on power system carbon emission flow[J].Automation of electric power systems, 2012, 36(7): 38-43, 85.
[18] 周天睿, 康重庆, 徐乾耀, 等. 碳排放流在电力网络中分布的特性与机理分析[J]. 电力系统自动化, 2012,36(15): 39-44.
ZHOU T R, KANG C Q, XU Q Y, et al.Analysis on distribution characteristics and mechanisms of carbon emission flow in electric power network[J]. Automation of electric power systems, 2012, 36(15): 39-44
[19] 刘昱良, 李姚旺, 周春雷, 等. 电力系统碳排放计量与分析方法综述[J]. 中国电机工程学报, 2024, 44(6):2220-2236.
LIU Y L, LI Y M, ZHOU C L, et al.Overview of carbon measurement and analysis methods in power systems[J]. Proceedings of the CSEE, 2024, 44(6): 2220-2236.
[20] 康重庆, 杜尔顺, 李姚旺, 等. 新型电力系统的“碳视角”: 科学问题与研究框架[J]. 电网技术, 2022, 46(3): 821-833.
KANG C Q, DU E S, LI Y W, et al.Key scientific problems and research framework for carbon perspective research of new power systems[J]. Power system technology, 2022, 46(3): 821-833.
[21] 刘广一, 王继业, 汤亚宸, 等. 电网碳排放因子研究方向与应用需求的演变进程[J]. 电网技术, 2024, 48(1): 12-28.
LIU G Y, WANG J Y, TANG Y C, et al.Evolution process of research directions and application requirements of electricity carbon emission factors[J]. Power system technology, 2024, 48(1): 12-28.
[22] 栗峰, 丁杰, 周才期, 等. 新型电力系统下分布式光伏规模化并网运行关键技术探讨[J]. 电网技术, 2024,48(1): 184-199.
LI F, DING J, ZHOU C Q, et, al. Key Technologies of large-scale grid-connected operation of distributed photovoltaic under new-type power system[J]. Power system technology, 2024, 48(1): 184-199.
[23] 胡润泽, 吕世轩, 张灿, 等. 基于状态熵权和双轨制TOPSIS的电能质量实时综合评估方法[J]. 电力系统保护与控制, 2023, 51(23): 102-114.
HU R, LYU S X, ZHANG C, et al.Real time comprehensive evaluation method of power quality based on state entropy and dual track TOPSIS[J]. Power system protection and control, 2023, 51(23): 102-114.
[24] 张帆. 大规模分散光伏电源接入配电网的协同优化研究[D]. 合肥: 合肥工业大学, 2020.
ZHANG F.Research on collaborative optimization of large-scale distributed photovoltaic power access to distribution network[D]. Hefei: Hefei University of Technology, 2020.
[25] 叶宇静, 邢海军, 米阳, 等. 考虑低碳需求响应及主从博弈的综合能源系统低碳优化调度[J]. 电力系统自动化, 2024, 48(9): 34-43.
YE Y J, XING H J, MI Y, et al.Low-carbon optimal scheduling of integrated energy system considering low-carbon demand response and master-slave game[J]. Automation of electric power systems, 2024, 48(9): 34-43.
[26] WANG W, ZHOU C, LI X.Carbon reduction in a supply chain via dynamic carbon emission quotas[J]. Journal of cleaner production, 2019, 240:118244-118244.
[27] 童晓阳, 张绍迅. 基于灰色关联度的配电网故障区段定位与类型识别方法[J]. 电力系统自动化, 2019, 43(4): 113-118, 145.
TONG X Y, ZHANG S X.Fault section location and type identification method of distribution network based on grey correlation degree[J]. Automation of electric power systems, 2019, 43(4): 113-118, 145.
[28] 刘博, 陈佳漩, 李江. 基于SEM-MIP状态估计的配电网拓扑辨识方法[J]. 电力系统保护与控制, 2024, 52(13): 69-77.
LIU B, CHEN J X, LI J.Distribution network topology identification method based on SEM-MIP state estimation[J]. Power system protection and control, 2024, 52(13): 69-77.
[29] 杨琰, 殷玮川, 孙连英, 等. 基于SEM模型的新能源网约车用户充电行为关联因素分析[J]. 北京交通大学学报, 2024, 48(3): 39-47.
YANG Y, YIN W C, SUN L Y, et al.An analysis of the related factors of charging behavior of new energy network car-hailing users based on SEM model[J]. Journal of Beijing Jiaotong University, 2024, 48(3): 39-47.
[30] 李鹏, 文淼, 董存, 等. 基于模糊DEMATEL-超效率DEA的可再生能源发电技术综合效益评估方法[J]. 电网技术, 2024, 48(6): 2346-2355.
LI P, WEN M, DONG C, et al.Comprehensive benefit evaluation method of renewable energy power generation technology based on fuzzy DEMATEL-super efficiency DEA[J]. Power system technology, 2024, 48(6):2346-2355.
[31] 赵乐冰, 王蕾, 万灿, 等. 基于数据包络分析法的城市综合能源系统分环节能效评价[J]. 电力系统自动化, 2022, 46(17): 132-141.
ZHAO L B, WANG L, WAN C, et al.Energy efficiency evaluation of urban integrated energy system based on data envelopment analysis[J]. Automation of electric power systems, 2022, 46(17): 132-141.
[32] 崔勇, 韩一春, 郑谦, 等. 多能联盟低碳运营决策方法研究框架与展望[J]. 电测与仪表, 2024, 61(3): 10-19.
CUI Y, HAN Y C, ZHENG Q, et al.Research framework and prospect of multi-energy alliance low-carbonoperation decision-making method[J]. Electrical measurement & instrumentation, 2024, 61(3): 10-19.
[33] 赵春英, 马占新. 权重受限的超效率DEA模型及其投影分析[J]. 系统工程学报, 2019, 34(1): 116-129.
ZHAO C Y, MA Z X.Weight restricted super efﬁciency DEA model and projection analysis[J]. Journal of systems engineering, 2019, 34(1): 116-129.
[34] 吕文琪, 刘子文, 董晓霄, 等. 考虑灵活性资源可调容量和响应速度的集群划分方法[J]. 电力工程技术, 2024, 43(05): 37-47.
LYU W Q, LlU Z W, DONG X X, et al. A cluster partitioning method considering adjustable capacity and response speed of flexible resources[J]. Electric power engineering technology, 2024, 43(5): 37-47.
[35] 彭超逸, 徐苏越, 顾慧杰, 等. 基于主从博弈的虚拟电厂参与多元竞争市场投标策略研究[J]. 电力系统保护与控制, 2024, 52(7): 125-137.
PENG C Y, XU S Y, GU H J, et al.Bidding strategy for a virtual power plant participating in a multiple competitive market based on the Stackelberg game[J]. Power system protection and control, 2024, 52(7): 125-137.
[36] 李明扬,张智. 基于强化学习的含分布式风-光-储虚拟电厂优化调度[J]. 智慧电力, 2024, 52(8): 50-56.
LI M Y, ZHANG Z.Optimal dispatch of distributed wind-solar-storage virtual power lants based on reinforcement learning[J]. System optimization, 2024, 52(8): 50-56.