基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法

doi:10.11930/j.issn.1004-9649.202505069

中国电力 ›› 2026, Vol. 59 ›› Issue (1): 10-19.DOI: 10.11930/j.issn.1004-9649.202505069

• 考虑分布式虚拟储能聚合的综合能源系统规划、运行和交易的关键技术 • 上一篇

基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法

蔡木良¹(), 赖信辉¹(), 李赢正¹(), 田野²(), 余杰³(), 程敏军⁴(), 许银亮²(), 蔺晨晖⁵()

1. 国网江西省电力有限公司电力科学研究院，江西南昌　330096
2. 清华大学深圳国际研究生院，广东深圳　518000
3. 国网江西省电力有限公司，江西南昌　330096
4. 国网江西省电力有限公司鹰潭供电分公司，江西鹰潭　335000
5. 清华大学电机工程与应用电子技术系，北京　100084

收稿日期:2025-05-26 修回日期:2025-12-05 发布日期:2026-01-13 出版日期:2026-01-28
作者简介:
蔡木良（1989），男，硕士，高级工程师，从事配电网运行、分布式能源调控研究，E-mail：dky_caiml@jx.sgcc.com.cn
田野（1999），男，通信作者，博士研究生，从事电力系统优化调度研究，E-mail：tiany25@mails.tsinghua.edu.cn
基金资助:
国家自然科学基金资助项目（52307101）；2024年国网江西电力重点研究科技项目（521820240022）。

Power-carbon-green certificate distributed cooperative optimization method based on flexibility support mechanism of active distribution networks

CAI Muliang¹(), LAI Xinhui¹(), LI Yingzheng¹(), TIAN Ye²(), YU Jie³(), CHENG Minjun⁴(), XU Yinliang²(), LIN Chenhui⁵()

1. Electric Power Research Institute, State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China
2. Tsinghua Shenzhen International Graduate School, Shenzhen 518000, China
3. State Grid Jiangxi Electric Power Co., Ltd., Nanchang 330096, China
4. Yingtan Power Supply Branch, State Grid Jiangxi Electric Power Co., Ltd., Yingtan 335000, China
5. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Received:2025-05-26 Revised:2025-12-05 Online:2026-01-13 Published:2026-01-28
Supported by:
This work is supported by National Natural Science Foundation of China (No.52307101), Key research projects of science and technology of State Grid Jiangxi Electric Power in 2024 (No.521820240022).

摘要/Abstract

摘要：

随着分布式新能源和电动汽车等虚拟储能的广泛接入，电力系统的不确定性和低惯量特性变得更加显著，系统的频率运行风险增加，并且主动配电网内部的供需关系呈现出更加灵活多样的特征，导致其与输电网之间的功率交互关系也更加复杂。为此，构建了一种基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法，以充分挖掘输配系统中分布式资源的支撑能力及其碳减排效益。首先，建立了与输配协同系统匹配的动态频率安全约束和基于双向调节模型的主动配电网灵活性支撑机制，提出了输配系统间的电-碳-绿证协同交互框架。为应对新能源出力的不确定性，将两侧子系统中涉及不确定变量的约束建模为联合机会约束。采用交替方向乘子法（alternating direction of multipliers algorithm，ADMM）进行输配系统的分布式协同。仿真结果表明，所提模型能够有效挖掘配电网中分布式资源的支撑潜力，灵活体现主动配电网的向外支撑能力或被支撑需求。与传统方法相比，系统动态频率安全性和输配协同调节灵活性显著提升，总运行成本较输电网单独调度模型降低13.42%，新能源减载量较忽略多市场耦合模型减少16.76%，系统运行经济性和低碳性明显改善。此外，所提出的求解方法比基于传统的样本平均近似方法减少约98%的计算时间，能够实现输配系统的快速分布式协同优化。

关键词: 输配协同调度, 频率安全约束, 电-碳-绿证耦合

Abstract:

The widespread integration of distributed renewable energy sources and virtual energy storage, such as electric vehicles, exacerbates the inherent uncertainty and low-inertia characteristics of power systems, thereby heightening frequency violation risks. Concurrently, the increasing flexibility and diversity of supply-demand dynamics within active distribution networks (ADNs) further complicate their interactions with transmission grids. To address these challenges, this paper proposes a novel power-carbon-green certificate distributed cooperative optimization framework based on the flexibility support mechanism of ADNs, with the objective of maximizing the utilization of distributed regulation resources and enhancing carbon reduction potential within integrated transmission-distribution (ITD) systems. Firstly, the dynamic frequency security constraints tailored to ITD systems and a flexibility support mechanism for ADNs based on interactive regulation modeling are established, forming a comprehensive ITD electricity-carbon-green certificate cooperation framework. To effectively address the uncertainty inherent in subsystems, joint chance constraints (JCCs) are employed to model the probabilistic nature of the problem. The Alternating direction of multipliers algorithm (ADMM) algorithm is adopted for the distributed cooperation of the transmission and distribution system. Case studies demonstrate the effectiveness of the proposed method in fully leveraging distributed resources and enhancing system frequency security. The framework also exhibits the ability to flexibly reflect the external support capacity or supported demand of ADNs. Compared to the transmission-grid independent dispatch model, the total operation cost is reduced by 13.42%, while renewable energy curtailment is decreased by 16.76% relative to the model that ignores multi-market coupling. These results demonstrate significant improvements in both the operational economy and low-carbon performance of the system. Furthermore, the proposed method achieves a reduction of approximately 98% in computational time compared to the traditional sample-average approximation (SAA)-based approach, thereby facilitating rapid distributed coordination within ITD systems.

Key words: integrated transmission and distribution dispatch, frequency security constraints, power-carbon-green certificate coupling

蔡木良, 赖信辉, 李赢正, 田野, 余杰, 程敏军, 许银亮, 蔺晨晖. 基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法[J]. 中国电力, 2026, 59(1): 10-19.

CAI Muliang, LAI Xinhui, LI Yingzheng, TIAN Ye, YU Jie, CHENG Minjun, XU Yinliang, LIN Chenhui. Power-carbon-green certificate distributed cooperative optimization method based on flexibility support mechanism of active distribution networks[J]. Electric Power, 2026, 59(1): 10-19.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202505069

https://www.electricpower.com.cn/CN/Y2026/V59/I1/10

图/表 8

图 1 电-碳-绿证协同交互框架

Fig.1 Collaborative interaction framework of electricity, carbon and green-certificates

图 2 一次和二次调节备用设定的示意

Fig.2 Schematic diagram of primary and secondary regulation reserve settings

表 1 不同调度模型的安全性和经济性比较

Table 1 Comparison of the security and economics for different dispatch models

优化模型	输电网单独调度	固定逆变器系数	POCD
运行成本/元	1.419e+06	1.417e+06	1.279e+06
碳-绿证市场成本/元	2.498e+05	2.623e+05	1.665e+05
总成本/元	1.669e+06	1.679e+06	1.445e+06
输电侧虚拟惯量均值/s	0.209	0.150	0.020
输电侧阻尼D_k均值 (p.u.)	0.787	0.554	0.083
配网侧虚拟惯量均值/s	0.000	0.330	0.163
配网侧阻尼D_k均值 (p.u.)	0.000	1.135	0.991
频率最低点/Hz	–0.454	–0.510	–0.498
最大频率偏差变/(Hz·s^–1)	–0.424	–0.500	–0.500

表 2 碳-绿证市场对调度决策的影响

Table 2 Influence of carbon-green certificate market for scheduling decision

优化模型	忽略电-碳-绿证耦合的模型	所提POCD模型
运行成本/元	1.278e+06	1.279e+06
碳-绿证市场成本/元	2.054e+05	1.665e+05
总成本/元	1.483e+06	1.445e+06
新能源减载量/MW	69.772	58.080
新能源虚拟惯量均值/s	0.009	0.001
新能源下垂阻尼均 (p.u.)	0.050	0.040
储能虚拟惯量均值/s	0.041	0.055
储能下垂阻尼均值 (p.u.)	1.034	1.034

图 3 不同调度模型下的频率关键指标变化曲线

Fig.3 Changing curve of critical frequency indexes associated with different dispatch model

图 4 边界交互备用变化曲线

Fig.4 Changing curve of interactive reserve at boundary

表 3 分布式协同的求解时间

Table 3 Computational time of distributed cooperation

协同方法	SAA-ADMM	MSAA-ADMM
最优误差/%	0.22	0.49
迭代次数/次	134	116
求解时间/s	1502.46	28.14

图 5 最大残差的收敛曲线

Fig.5 Convergence curve of maximum residual

参考文献 35

1	邱忠涛, 格根敖其, 贾跃龙, 等. 新型电力系统供需协同全要素理论框架[J]. 中国电力, 2025, 58 (10): 147- 162. DOI
	QIU Zhongtao, GE Gen'aoqi, JIA Yuelong, et al. All-factor theoretical framework for supply-demand synergy in new-type power systems[J]. Electric Power, 2025, 58 (10): 147- 162. DOI
2	许喆, 陈晓东, 贾旭东, 等. 考虑可再生能源配额的区域电力市场竞价行为推演[J]. 中国电力, 2025, 58 (10): 225- 234.
	XU Zhe, CHEN Xiaodong, JIA Xudong, et al. Bidding behavior evolution in regional electricity markets considering renewable energy quotas[J]. Electric Power, 2025, 58 (10): 225- 234.
3	彭竹弈, 宋杉, 许偲轩, 等. 基于时序运行模拟的新能源电力系统典型场景生成方法[J]. 浙江电力, 2025, 44 (5): 66- 75.
	PENG Zhuyi, SONG Shan, XU (CaiSi)(Xuan), et al. A typical scenario generation method for renewable energy power systems based on time-sequenced operational simulations[J]. Zhejiang Electric Power, 2025, 44 (5): 66- 75.
4	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.
5	王臻, 刘东, 徐重酉, 等. 新型电力系统多源异构数据融合技术研究现状及展望[J]. 中国电力, 2023, 56 (4): 1- 15. DOI
	WANG Zhen, LIU Dong, XU (Chong\| Zhong)(You), et al. Status quo and prospect of multi-source heterogeneous data fusion technology for new power system[J]. Electric Power, 2023, 56 (4): 1- 15. DOI
6	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.
7	MORREN J, DE HAAN S W H, KLING W L, et al. Wind turbines emulating inertia and supporting primary frequency control[J]. IEEE Transactions on Power Systems, 2006, 21 (1): 433- 434. DOI
8	闫丽梅, 丁泽华. 基于谱聚类的主动配电网多时间尺度无功优化策略[J]. 浙江电力, 2024, 43 (2): 58- 68. DOI
	YAN Limei, DING Zehua. A multi-timescale reactive power optimization strategy for active distribution net-works based on spectral clustering[J]. Zhejiang Electric Power, 2024, 43 (2): 58- 68. DOI
9	SHEN Y K, WU W C, WANG B, et al. Optimal allocation of virtual inertia and droop control for renewable energy in stochastic look-ahead power dispatch[J]. IEEE Transactions on Sustainable Energy, 2023, 14 (3): 1881- 1894. DOI
10	TIAN Y, LI Z S, WU W C, et al. Joint chance-constrained economic dispatch involving joint optimization of frequency-related inverter control and regulation reserve allocation[J]. CSEE Journal of Power and Energy Systems, 2025, 11 (3): 1030- 1044. DOI
11	TIAN Y, LI Z S. Coordinated frequency-constrained stochastic economic dispatch for integrated transmission and distribution system via distributed optimization[J]. CSEE Journal of Power and Energy Systems, 2024. DOI:10.17775/CSEEJPES.2023.04100.
12	LI P, WU Q W, YANG M, et al. Distributed distributionally robust dispatch for integrated transmission-distribution systems[J]. IEEE Transactions on Power Systems, 2021, 36 (2): 1193- 1205. DOI
13	董雷, 李扬, 陈盛, 等. 考虑多重不确定性与电碳耦合交易的多微网合作博弈优化调度[J]. 电工技术学报, 2024, 39 (9): 2635- 2651. DOI
	DONG Lei, LI Yang, CHEN Sheng, et al. Multi-microgrid cooperative game optimization scheduling considering multiple uncertainties and coupled electricity-carbon transactions[J]. Transactions of China Electrotechnical Society, 2024, 39 (9): 2635- 2651. DOI
14	柴超, 刘松阳, 孔维康, 等. 考虑绿证交易的虚拟电厂运行策略优化研究[J]. 电力需求侧管理, 2024, 26 (5): 100- 105. DOI
	CHAI Chao, LIU Songyang, KONG Weikang, et al. Operation strategy optimization of virtual power plant considering green card transaction[J]. Power Demand Side Management, 2024, 26 (5): 100- 105. DOI
15	崔杨, 沈卓, 王铮, 等. 考虑绿证–碳排等价交互机制的区域综合能源系统绿色调度[J]. 中国电机工程学报, 2023, 43 (12): 4508- 4516. DOI
	CUI Yang, SHEN Zhuo, WANG Zheng, et al. Green dispatch of regional integrated energy system considering green certificate-carbon emission equivalent interaction mechanism[J]. Proceedings of the CSEE, 2023, 43 (12): 4508- 4516. DOI
16	朱瑛, 郭健伟, 周亦洲, 等. 考虑碳-绿证市场耦合的多虚拟电厂电-碳P2P交易模型[J]. 电力自动化设备, 2025, 45 (1): 51- 58. DOI
	ZHU Ying, GUO Jianwei, ZHOU Yizhou, et al. Electricity-carbon P2P trading model for multiple virtual power plants considering carbon-green certificate market coupling[J]. Electric Power Automation Equipment, 2025, 45 (1): 51- 58. DOI
17	马佳彤, 张儒峰, 付麟博, 等. 输配电网多主体参与的碳-电耦合市场双层交易决策方法[J]. 电力系统保护与控制, 2024, 52 (17): 12- 26. DOI
	MA Jiatong, ZHANG Rufeng, FU Linbo, et al. A bi-level trading decision-making method for the carbon-electricity coupling market with multi-agent participation in transmission and distribution networks[J]. Power System Protection and Control, 2024, 52 (17): 12- 26. DOI
18	黄铭浩, 陈一丰, 董树锋. 基于安德森加速的输配协同低碳最优潮流[J]. 电网技术, 2023, 47 (8): 3132- 3140.
	HUANG Minghao, CHEN Yifeng, DONG Shufeng. Low-carbon optimal power flow of transmission-distribution-coupled networks based on Anderson acceleration[J]. Power System Technology, 2023, 47 (8): 3132- 3140.
19	杨黎, 兰怡希, 林玲, 等. 风储系统中储能虚拟惯量评估与频率支撑技术[J]. 浙江电力, 2024, 43 (6): 52- 60. DOI
	YANG Li, LAN Yixi, LIN Ling, et al. A virtual inertia assessment and frequency support technology for wind-storage power generation system[J]. Zhejiang Electric Power, 2024, 43 (6): 52- 60. DOI
20	ZHANG Z Y, DU E S, TENG F, et al. Modeling frequency dynamics in unit commitment with a high share of renewable energy[J]. IEEE Transactions on Power Systems, 2020, 35 (6): 4383- 4395. DOI
21	李正烁, 田野, 田健, 等. 考虑频率动态安全约束的输配电协同优化调度方法和系统: CN116436040B[P]. 2024-09-17.
22	罗超, 杨军, 孙元章, 等. 考虑备用容量优化分配的含风电电力系统动态经济调度[J]. 中国电机工程学报, 2014, 34 (34): 6109- 6118. DOI
	LUO Chao, YANG Jun, SUN Yuanzhang, et al. Dynamic economic dispatch of wind integrated power system considering optimal scheduling of reserve capacity[J]. Proceedings of the CSEE, 2014, 34 (34): 6109- 6118. DOI
23	田野. 计及不确定性和系统频率特性的新型电力系统有功调度研究[D]. 济南: 山东大学, 2024.
	TIAN Ye. Research on active power dispatch of new power systems considering uncertainty and system frequency characteristics[D]. Jinan: Shandong University, 2024.
24	YANG J W, ZHANG N, KANG C Q, et al. A state-independent linear power flow model with accurate estimation of voltage magnitude[J]. IEEE Transactions on Power Systems, 2017, 32 (5): 3607- 3617. DOI
25	张冲标, 钱辰雯, 俞红燕, 等. 基于ADMM的多场景县域多微电网交互运行策略[J]. 中国电力, 2024, 57 (2): 9- 18.
	ZHANG Chongbiao, QIAN Chenwen, YU Hongyan, et al. Interactive operation strategy for multi-scenario county-level multi-microgrid based on ADMM[J]. Electric Power, 2024, 57 (2): 9- 18.
26	张丽娟, 保富. 含分布式新能源的多虚拟电厂协同运行[J]. 电测与仪表, 2025, 62 (9): 134- 141. DOI
	ZHANG Lijuan, BAO Fu. Coordinated operation of multiple virtual power plants integrated with distributed renewable energy[J]. Electrical Measurement & Instrumentation, 2025, 62 (9): 134- 141. DOI
27	刘硕, 马兆兴, 刘金鑫, 等. 面向新能源消纳的新型电力系统优化运行策略[J]. 湖南电力, 2024, 44 (1): 59- 66. DOI
	LIU Shuo, MA Zhaoxing, LIU Jinxin, et al. Optimal operation strategies of new power system aimed at renewable energy system consumption[J]. Hunan Electric Power, 2024, 44 (1): 59- 66. DOI
28	石鑫, 刘奇央, 高峰. 深度神经网络在新型能源系统中的应用及展望[J]. 综合智慧能源, 2025, 47 (2): 88- 101. DOI
	SHI Xin, LIU Qiyang, GAO Feng. Application and prospects of deep neural network in new energy systems[J]. Integrated Intelligent Energy, 2025, 47 (2): 88- 101. DOI
29	郑焕坤, 李钰静. 考虑碳交易和信息物理故障的综合能源系统优化运行[J]. 电测与仪表, 2025, 62 (12): 1- 10. DOI
	ZHENG Huankun, LI Yujing. Optimization operation of integrated energy system considering carbon trading and information physical failures[J]. Electrical Measurement & Instrumentation, 2025, 62 (12): 1- 10. DOI
30	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. DOI
	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. DOI
31	YANG B, LIU S W, GAO H D. Energy e-commerce user portrait and multi-agent cooperative game price mechanism design[J]. Frontiers in Energy Research, 2024, 12, 1379612. DOI
32	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. DOI
	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. DOI
33	罗莎莎, 郭经韬, 蔡颖倩, 等. 面向碳中和的广东省电源结构转型分析[J]. 南方能源建设, 2024, 11 (4): 102- 110. DOI
	LUO Shasha, GUO Jingtao, CAI Yingqian, et al. Analysis on power supply structure transformation towards carbon neutrality in Guangdong[J]. Southern Energy Construction, 2024, 11 (4): 102- 110. DOI
34	马文祚, 夏周武. 考虑新能源不确定性的电网运营商多时间尺度鲁棒交易[J]. 山东电力技术, 2024, 51 (8): 49- 58. DOI
	MA Wenzuo, XIA Zhouwu. Multi-time scale robust trading of power grid operators considering new energy uncertainty[J]. Shandong Electric Power, 2024, 51 (8): 49- 58. DOI
35	李令宇, 程浩忠, 张衡, 等. 含高比例新能源电力系统的低碳电源规划方法[J]. 电测与仪表, 2025, 62 (7): 30- 37. DOI
	LI Lingyu, CHENG Haozhong, ZHANG Heng, et al. Low-carbon power planning method for power system with high proportion of renewable energy[J]. Electrical Measurement & Instrumentation, 2025, 62 (7): 30- 37. DOI

基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法

Power-carbon-green certificate distributed cooperative optimization method based on flexibility support mechanism of active distribution networks

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基于主动配电网灵活性支撑机制的电-碳-绿证分布式协同优化方法

Power-carbon-green certificate distributed cooperative optimization method based on flexibility support mechanism of active distribution networks

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