市场环境下考虑多元用户侧资源协同的虚拟电厂低碳优化调度

doi:10.11930/j.issn.1004-9649.202407102

摘要/Abstract

摘要：

为发挥多元用户侧资源协同作用，提高虚拟电厂（virtual power plant，VPP）参与电碳联合市场收益，降低风光及电价不确定性引起的风险，提出了一种市场环境下考虑多元用户侧资源协同的VPP低碳优化调度方法。首先，基于各种用户侧资源协同作用，形成了VPP参与电碳联合市场运行策略；然后，建立VPP奖惩阶梯型碳交易模型，根据碳交易量设定不同交易区间价格，实现了碳电耦合；最后，构建VPP参与电碳联合市场的低碳优化调度模型，在模型中引入条件风险价值，衡量市场收益与风险的关系，并将模型转换为混合整数线性规划问题求解。通过算例分析，证明了该方法可以发挥用户侧资源的协同作用，有效应对风光及电价的不确定性风险，实现VPP参与市场运行的经济性和低碳性。

关键词: 用户侧资源, 虚拟电厂, 不确定风险, 电碳联合市场, 优化调度

Abstract:

To leverage the synergistic effects of diverse user-side resources, enhance the revenue of Virtual Power Plants (VPPs) participating in the joint electricity-carbon market, and mitigate risks arising from the uncertainty of renewable energy sources (such as wind and solar) and electricity prices, an optimized low-carbon dispatching method for VPPs considering the coordination of diverse user-side resources in a market environment is proposed. Firstly, based on the synergistic effects of various user-side resources, a strategy for VPPs to participate in the joint electricity-carbon market operation is formulated. Secondly, a tiered carbon trading model with incentives and penalties for VPPs is established, where different transaction interval prices are set according to carbon trading volumes, thereby achieving carbon-electricity coupling. Finally, an optimized low-carbon dispatching model for VPPs participating in the joint electricity-carbon market is constructed. In this model, Conditional Value at Risk (CVaR) is introduced to measure the relationship between market returns and risks, and the model is transformed into a Mixed Integer Linear Programming (MILP) problem for solution. Through case studies, it is demonstrated that this method can effectively harness the synergistic effects of user-side resources, address the uncertainty risks associated with renewable energy sources and electricity prices, and achieve both economic efficiency and low-carbon performance for VPPs participating in market operations.

Key words: user-side resource, virtual power plant, risk of uncertainty, electricity-carbon combined market, economic dispatch

李明冰, 李强, 管西洋, 周皓阳, 卢瑞, 冯延坤. 市场环境下考虑多元用户侧资源协同的虚拟电厂低碳优化调度[J]. 中国电力, 2025, 58(2): 66-76.

Mingbing LI, Qiang LI, Xiyang GUAN, Haoyang ZHOU, Rui LU, Yankun FENG. Market Oriented Low-Carbon Optimal Scheduling of Virtual Power Plants Considering Multiple User-Side Resources Coordination[J]. Electric Power, 2025, 58(2): 66-76.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202407102

https://www.electricpower.com.cn/CN/Y2025/V58/I2/66

图/表 14

图 1 VPP参与电碳联合市场运行策略

Fig.1 VPP participates in the operation strategy of electric carbon joint market

图 2 VPP参与电碳联合市场模型求解流程

Fig.2 VPP participates in solving flow of electric carbon joint market model

图 3 柔性负荷的初始值

Fig.3 The initial value of the flexible load

表 1 CDG和储能的相关参数

Table 1 CDG and energy storage related parameters

CDG：燃气轮机		储能
额定功率/MW	2	额定容量/(MW·h)	10
最小发电功率/MW	0.5	额定充放电功率/MW	2
成本系数a	0.008	荷电状态范围	0.1~0.9
成本系数b	0.0175	初始荷电状态	0.2
成本系数c	105	充放电效率	0.95

表 2 调峰市场时段及价格

Table 2 Peak market hours and prices

项目	时段	价格/(元·(MW·h)^–1)
填谷调峰	01:00—08:00	350
削峰调峰	09:00—12:00，18:00—21:00	500

图 4 日前市场电价数据

Fig.4 Day ahead market price data

图 5 某地区风光预测数据

Fig.5 Scenic forecast data for an area

图 6 风光及日前市场电价典型场景集

Fig.6 Typical scene set of scenery and market electricity price

表 3 各场景对应概率

Table 3 Each scenario corresponds to the probability

场景		1		2		3		4		5
概率		0.2187		0.1732		0.2359		0.2129		0.1593

图 7 不同风险偏好系数下的收益-风险结果

Fig.7 Benefit-risk outcomes under different risk preference coefficients

表 4 各场景下收益对比

Table 4 Income comparison under each scenario

场景	VPP总收益/元	电能量市场收益/元	调峰市场收益/元	碳市场收益/元	VPP运行成本/元	碳排放量/t
1	6835.09	9856.75	0	0	3021.66	31.2
2	24555.21	7512.51	19681.63	0	2978.93	29.8
3	9294.07	9639.22	0	2486.13	2831.28	26.8
4	26870.93	7255.11	19510.82	2626.56	2521.56	24.0

图 8 VPP电能量市场购售电量及电价

Fig.8 VPP electricity market purchase and sale of electricity and electricity price

图 9 VPP中各主体功率

Fig.9 Power of each body in VPP

图 10 储能与柔性负荷的优化结果

Fig.10 Results of energy storage and flexible load optimization

参考文献 25

1	ZHANG Y M, SUN P K, JI X Q, et al. Low-carbon economic dispatch of integrated energy systems considering full-process carbon emission tracking and low carbon demand response[J]. IEEE Transactions on Network Science and Engineering, 2024, PP (99): 1- 16.
2	魏震波, 孙舟倍, 梁政. 基于多目标两阶段规划的虚拟电厂优化调度[J]. 南方电网技术, 2023, 17 (4): 109- 118.
	WEI Zhenbo, SUN Zhoubei, LIANG Zheng. Virtual power plant optimal dispatching based on multi-objective and two-stage programming[J]. Southern Power System Technology, 2023, 17 (4): 109- 118.
3	徐慧慧, 田云飞, 缪猛, 等. 计及碳交易和需求响应的虚拟电厂低碳经济调度[J]. 智慧电力, 2023, 51 (8): 1- 7. DOI
	XU Huihui, TIAN Yunfei, MIAO Meng, et al. Low carbon economy dispatch of virtual power plants considering carbon trading and demand response[J]. Smart Power, 2023, 51 (8): 1- 7. DOI
4	宋天琦, 吕志鹏, 宋振浩, 等. 虚拟电厂规模化灵活资源聚合调控框架研究与思考[J]. 中国电力, 2024, 57 (1): 2- 8.
	SONG Tianqi, LV Zhipeng, SONG Zhenhao, et al. Research and thinking on the aggregation and dispatching control framework of virtual power plant's large scale flexible resources[J]. Electric Power, 2024, 57 (1): 2- 8.
5	李嘉媚, 艾芊. 考虑调峰辅助服务的虚拟电厂运营模式[J]. 电力自动化设备, 2021, 41 (6): 1- 13.
	LI Jiamei, AI Qian. Operation mode of virtual power plant considering peak regulation auxiliary service[J]. Electric Power Automation Equipment, 2021, 41 (6): 1- 13.
6	孙辉, 范轩轩, 胡姝博, 等. 虚拟电厂参与日前电力市场的内外协调竞标策略[J]. 电网技术, 2022, 46 (4): 1248- 1262.
	SUN Hui, FAN Xuanxuan, HU Shubo, et al. Internal and external coordination bidding strategy of virtual power plant participating in day-ahead power market[J]. Power System Technology, 2022, 46 (4): 1248- 1262.
7	石梦舒, 许小峰, 张继广, 等. 考虑电-氢市场的虚拟电厂两阶段优化策略研究[J]. 发电技术, 2023, 44 (5): 645- 655. DOI
	SHI Mengshu, XU Xiaofeng, ZHANG Jiguang, et al. A two-stage optimization strategy for virtual power plants considering the electricity-hydrogen market[J]. Power Generation Technology, 2023, 44 (5): 645- 655. DOI
8	王钦, 陈业夫, 蔡新雷, 等. 考虑柔性负荷和阶梯型碳交易的低碳经济优化调度策略[J]. 广东电力, 2024, 37 (1): 76- 85.
	WANG Qin, CHEN Yefu, CAI Xinlei, et al. Optimization scheduling strategy for low-carbon economy considering flexible loads and tiered carbon trading[J]. Guangdong Electric Power, 2024, 37 (1): 76- 85.
9	李强, 王尧, 胡迎迎, 等. 考虑碳交易成本的输储多阶段协调规划方法[J]. 中国电力, 2023, 56 (12): 199- 205.
	LI Qiang, WANG Yao, HU Yingying, et al. Multi-stage coordinated planning method for transmission network and energy storage considering carbon trading cost[J]. Electric Power, 2023, 56 (12): 199- 205.
10	ZHANG L, LIU D Y, CAI G W, et al. An optimal dispatch model for virtual power plant that incorporates carbon trading and green certificate trading[J]. International Journal of Electrical Power & Energy Systems, 2023, 144, 108558.
11	YAN Q Y, ZHANG M J, LIN H Y, et al. Two-stage adjustable robust optimal dispatching model for multi-energy virtual power plant considering multiple uncertainties and carbon trading[J]. Journal of Cleaner Production, 2022, 336, 130400. DOI
12	WANG Y, WU X M, LIU M C, et al. Bidding strategy of the virtual power plant considering green certificates and carbon trading[J]. Energy Reports, 2023, 9, 73- 84.
13	LI Y, DENG Y Y, WANG Y H, et al. Robust bidding strategy for multi-energy virtual power plant in peak-regulation ancillary service market considering uncertainties[J]. International Journal of Electrical Power & Energy Systems, 2023, 151, 109101.
14	WANG Y N, LI G D, ZHOU B W, et al. Optimal dispatch strategy for virtual power plants with adjustable capacity assessment of high-energy-consuming industrial loads participating in ancillary service markets[J]. Sustainability, 2023, 15 (13): 10479. DOI
15	WANG J, ILEA V, BOVO C, et al. Optimal self-scheduling for a multi-energy virtual power plant providing energy and reserve services under a holistic market framework[J]. Energy, 2023, 278, 127903. DOI
16	李凌昊, 邱晓燕, 张浩禹, 等. 电力市场下的虚拟电厂风险厌恶模型与利益分配方法[J]. 电力建设, 2021, 42 (1): 67- 75. DOI
	LI Linghao, QIU Xiaoyan, ZHANG Haoyu, et al. Risk aversion model and profit distribution method of virtual power plant in power market[J]. Electric Power Construction, 2021, 42 (1): 67- 75. DOI
17	卫志农, 陈妤, 黄文进, 等. 考虑条件风险价值的虚拟电厂多电源容量优化配置模型[J]. 电力系统自动化, 2018, 42 (4): 39- 46. DOI
	WEI Zhinong, CHEN Yu, HUANG Wenjin, et al. Optimal allocation model for multi-energy capacity of virtual power plant considering conditional value-at-risk[J]. Automation of Electric Power Systems, 2018, 42 (4): 39- 46. DOI
18	ZHAO H T, WANG B, WANG X Y, et al. Active dynamic aggregation model for distributed integrated energy system as virtual power plant[J]. Journal of Modern Power Systems and Clean Energy, 2024, 52 (7): 125- 137.
19	NAVAL N, YUSTA J M. Virtual power plant models and electricity markets - A review[J]. Renewable and Sustainable Energy Reviews, 2021, 149, 111393. DOI
20	崔杨, 安宁, 付小标, 等. 面向高比例新能源电力系统调峰需求的储能容量配置方法综述[J]. 东北电力大学学报, 2023, 43 (1): 1- 8.
	CUI Yang, AN Ning, FU Xiaobiao, et al. Overview of energy storage capacity allocation methods for high-proportion new energy power system peak shaving demand[J]. Journal of Northeast Electric Power University, 2023, 43 (1): 1- 8.
21	吴艳娟, 靳鹏飞, 刘长铖, 等. 基于奖惩阶梯型碳价机制的能源枢纽低碳优化策略[J]. 电力工程技术, 2024, 43 (3): 88- 98. DOI
	WU Yanjuan, JIN Pengfei, LIU Changcheng, et al. Low-carbon optimization strategy for energy hub based on reward-punishment ladder carbon price mechanism[J]. Electric Power Engineering Technology, 2024, 43 (3): 88- 98. DOI
22	苏志鹏, 王莉, 梁欣怡, 等. 考虑阶梯式碳交易及综合需求响应的虚拟电厂优化调度[J]. 中国电力, 2023, 56 (12): 174- 182.
	SU Zhipeng, WANG Li, LIANG Xinyi, et al. Optimal dispatch of virtual power plant considering stepped carbon trading and comprehensive demand response[J]. Electric Power, 2023, 56 (12): 174- 182.
23	李亚峰, 王维庆. 考虑阶梯碳交易机制的含混氢天然气综合能源系统容量配置[J]. 电力科学与技术学报, 2023, 38 (6): 237- 247.
	LI Yafeng, WANG Weiqing. Capacity allocation of hydrogen-blended natural gas integrated energy system considering ladder carbon trading mechanism[J]. Journal of Electric Power Science and Technology, 2023, 38 (6): 237- 247.
24	李晓舟, 秦文萍, 景祥, 等. 计及不确定风险和多主体协同的虚拟电厂参与主辅市场联合优化策略[J/OL]. 电网技术: 1–16 [2024-07-10]. https://doi.org/10.13335/j.1000-3673.pst.2023.1309.
	LI Xiaozhou, QIN Wenping, JING Xiang, et al. Joint optimization strategy for virtual power plant participating in main and auxiliary markets considering uncertain risks and multi-agent coordination[J/OL]. Power System Technology: 1–16 [2024-07-10]. https://doi.org/10.13335/j.1000-3673.pst.2023.1309.
25	张玉敏, 孙鹏凯, 吉兴全, 等. 考虑扩展碳排放流的综合能源系统低碳经济调度[J]. 电网技术, 2023, 47 (8): 3174- 3191.
	ZHANG Yumin, SUN Pengkai, JI Xingquan, et al. Low-carbon economic dispatch of integrated energy system with augmented carbon emission flow[J]. Power System Technology, 2023, 47 (8): 3174- 3191.

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