支持虚拟电厂提供灵活爬坡服务的云边协同市场模型

doi:10.16183/j.cnki.jsjtu.2023.240

上海交通大学学报 ›› 2025, Vol. 59 ›› Issue (2): 186-199.doi: 10.16183/j.cnki.jsjtu.2023.240

• 新型电力系统与综合能源 • 上一篇下一篇

支持虚拟电厂提供灵活爬坡服务的云边协同市场模型

彭超逸¹, 陈文哲², 徐苏越², 李建设¹, 周华锋¹, 顾慧杰¹, 聂涌泉¹, 孙海顺²()

1.中国南方电网电力调度控制中心,广州 510530
2.华中科技大学强电磁工程与新技术国家重点实验室,武汉 430074

收稿日期:2023-06-13 修回日期:2023-07-24 接受日期:2023-08-09 出版日期:2025-02-28 发布日期:2025-03-11
通讯作者: 孙海顺 E-mail:haishunsun@hust.edu.cn
作者简介:彭超逸(1990—),博士,从事电力系统优化、电力市场、电力系统运行与控制等方向的研究.
基金资助:
中国南方电网有限公司科技项目(000000KK52200035)

Modeling of Cloud-Edge Collaborated Electricity Market Considering Flexible Ramping Products Provided by VPPs

PENG Chaoyi¹, CHEN Wenzhe², XU Suyue², LI Jianshe¹, ZHOU Huafeng¹, GU Huijie¹, NIE Yongquan¹, SUN Haishun²()

1. China Southern Power Grid Dispatching and Control Center, Guangzhou 510530, China
2. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China

Received:2023-06-13 Revised:2023-07-24 Accepted:2023-08-09 Online:2025-02-28 Published:2025-03-11
Contact: SUN Haishun E-mail:haishunsun@hust.edu.cn

1. 2023-193-附录.pdf(210KB)

摘要/Abstract

摘要：

虚拟电厂(VPP)拥有一定的负荷时移能力和功率调节能力,具备参与电力市场并提供灵活性爬坡服务(FRPs)的潜能,但受系统需求不确定影响,VPP难以进行准确的市场申报.因此,提出支持VPP参与电力市场并提供FRPs的云边协同市场架构,建立了对应的分布式优化交易模型.市场出清过程由系统运营商和VPP协同交互完成,能够准确引导VPP优化用电曲线并提供灵活爬坡服务.分布式优化模型采用目标级联分析方法迭代求解,并引入启发式约束条件来提升算法的收敛性.最后,在“鸭形曲线”典型场景中验证了所提市场交易模型的有效性,算例结果表明所提模型能有效降低系统运营费用,同时提升可再生能源消纳.

关键词: 虚拟电厂, 灵活性爬坡服务, 云边协同, 分布式优化

Abstract:

Due to its load time shifting and power regulation capabilities, virtual power plants (VPPs) have the potential to participate in the electricity market and provide flexible ramping products (FRPs). However, it is hard for VPPs to make accurate bidding in the market, due to the uncertainty of their dispatching capability and system requirements. Therefore, a cloud-edge collaborated market architecture supporting VPPs participation in the electricity market and providing FRPs services is proposed, and the corresponding distributed optimization trading model is established. The market clearing process is completed through the collaborative interaction between the independent system operator and VPPs, which can accurately guide VPPs to optimize the electricity consumption and provide flexible climbing services. The distributed optimization model is iteratively solved using the analytical target cascading (ATC) method, and heuristic constraints are introduced to improve the convergence of the algorithm. Finally, the proposed method is evaluated by the simulation results of typical cases featuring the “duck-curve” net load, which demonstrate that the cloud-edge collaborated market can effectively reduce operating costs and promote the consumption of renewable energy.

Key words: virtual power plant (VPP), flexible ramping product (FRP), cloud-edge collaboration, distributed optimization

中图分类号:

TM732

彭超逸, 陈文哲, 徐苏越, 李建设, 周华锋, 顾慧杰, 聂涌泉, 孙海顺. 支持虚拟电厂提供灵活爬坡服务的云边协同市场模型[J]. 上海交通大学学报, 2025, 59(2): 186-199.

PENG Chaoyi, CHEN Wenzhe, XU Suyue, LI Jianshe, ZHOU Huafeng, GU Huijie, NIE Yongquan, SUN Haishun. Modeling of Cloud-Edge Collaborated Electricity Market Considering Flexible Ramping Products Provided by VPPs[J]. Journal of Shanghai Jiao Tong University, 2025, 59(2): 186-199.

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参考文献 32

[1]	周孝信, 陈树勇, 鲁宗相, 等. 能源转型中我国新一代电力系统的技术特征[J]. 中国电机工程学报, 2018, 38(7): 1893-1904.
	ZHOU Xiaoxin, CHEN Shuyong, LU Zongxiang, et al. Technology features of the new generation power system in China[J]. Proceedings of the CSEE, 2018, 38(7): 1893-1904.
[2]	黄远明, 张玉欣, 夏赞阳, 等. 考虑需求响应资源和储能容量价值的新型电力系统电源规划方法[J]. 上海交通大学学报, 2023, 57(4): 432-441. doi: 10.16183/j.cnki.jsjtu.2021.477
	HUANG Yuanming, ZHANG Yuxin, XIA Zanyang, et al. Power system planning considering demand response resources and capacity value of energy storage[J]. Journal of Shanghai Jiao Tong University, 2023, 57(4): 432-441.
[3]	黎博, 陈民铀, 钟海旺, 等. 高比例可再生能源新型电力系统长期规划综述[J]. 中国电机工程学报, 2023, 43(2): 555-581.
	LI Bo, CHEN Minyou, ZHONG Haiwang, et al. A review of long-term planning of new power systems with large share of renewable energy[J]. Proceedings of the CSEE, 2023, 43(2): 555-581.
[4]	CALERO I, CAÑIZARES C A, BHATTACHARYA K, et al. Duck-curve mitigation in power grids with high penetration of PV generation[J]. IEEE Transactions on Smart Grid, 2022, 13(1): 314-329.
[5]	CAISO. Flexible ramping product:Revised draft final proposal[R]. California: CAISO, 2015.
[6]	MISO. Ramp capability for load following in MISO markets white paper[R]. Indiana: MISO, 2016.
[7]	王蓓蓓, 丛小涵, 高正平, 等. 高比例新能源接入下电网灵活性爬坡能力市场化获取机制现状分析及思考[J]. 电网技术, 2019, 43(8): 2691-2702.
	WANG Beibei, CONG Xiaohan, GAO Zhengping, et al. Status analysis and thoughts of market-oriented acquisition mechanism on flexible ramp capability for power grid with high proportion of renewable energy[J]. Power System Technology, 2019, 43(8): 2691-2702.
[8]	郭鸿业, 陈启鑫, 夏清, 等. 电力市场中的灵活调节服务: 基本概念、均衡模型与研究方向[J]. 中国电机工程学报, 2017, 37(11): 3057-3066.
	GUO Hongye, CHEN Qixin, XIA Qing, et al. Flexible ramping product in electricity markets: Basic concept, equilibrium model and research prospect[J]. Proceedings of the CSEE, 2017, 37(11): 3057-3066.
[9]	马洪艳, 贠靖洋, 严正. 基于分布鲁棒优化的灵活爬坡备用调度方法[J]. 中国电机工程学报, 2020, 40(19): 6121-6132.
	MA Hongyan, YUN Jingyang, YAN Zheng. Distributionally robust optimization based dispatch methodology of flexible ramping products[J]. Proceedings of the CSEE, 2020, 40(19): 6121-6132.
[10]	WU H Y, SHAHIDEHPOUR M, ALABDULWAHAB A, et al. Thermal generation flexibility with ramping costs and hourly demand response in stochastic security-constrained scheduling of variable energy sources[J]. IEEE Transactions on Power Systems, 2015, 30(6): 2955-2964.
[11]	WU H Y, SHAHIDEHPOUR M, KHODAYAR M E. Hourly demand response in day-ahead scheduling considering generating unit ramping cost[J]. IEEE Transactions on Power Systems, 2013, 28(3): 2446-2454.
[12]	CHEN R Z, WANG J H, BOTTERUD A, et al. Wind power providing flexible ramp product[J]. IEEE Transactions on Power Systems, 2017, 32(3): 2049-2061.
[13]	夏芹芹, 罗永捷, 王荣茂, 等. 考虑新能源爬坡的风光火耦合系统源荷匹配性分析及容量优化配置[J]. 上海交通大学学报, 2024, 58(1): 69-81. doi: 10.16183/j.cnki.jsjtu.2022.260
	XIA Qinqin, LUO Yongjie, WANG Rongmao, et al. Source-load matching analysis and optimal planning of wind-solar-thermal coupled system considering renewable energy ramps[J]. Journal of Shanghai Jiao Tong University, 2024, 58(1): 69-81.
[14]	HU J H, SARKER M R, WANG J H, et al. Provision of flexible ramping product by battery energy storage in day-ahead energy and reserve markets[J]. IET Generation, Transmission & Distribution, 2018, 12(10): 2256-2264.
[15]	ZHANG Z, LI F R, PARK S W, et al. Local energy and planned ramping product joint market based on a distributed optimization method[J]. CSEE Journal of Power and Energy Systems, 2021, 7(6): 1357-1368.
[16]	ZHANG B, KEZUNOVIC M. Impact on power system flexibility by electric vehicle participation in ramp market[J]. IEEE Transactions on Smart Grid, 2016, 7(3): 1285-1294.
[17]	ZHANG X, HU J F, WANG H Z, et al. Electric vehicle participated electricity market model considering flexible ramping product provisions[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5868-5879.
[18]	HEYDARIAN-FORUSHANI E, GOLSHAN M E H, SHAFIE-KHAH M, et al. Optimal operation of emerging flexible resources considering sub-hourly flexible ramp product[J]. IEEE Transactions on Sustainable Energy, 2018, 9(2): 916-929.
[19]	YAMUJALA S, JAIN A, SREEKUMAR S, et al. Enhancing power systems operational flexibility with ramp products from flexible resources[J]. Electric Power Systems Research, 2022, 202: 107599.
[20]	李嘉媚, 艾芊, 殷爽睿. 虚拟电厂参与调峰调频服务的市场机制与国外经验借鉴[J]. 中国电机工程学报, 2022, 42(1): 37-56.
	LI Jiamei, AI Qian, YIN Shuangrui. Market mechanism and foreign experience of virtual power plant participating in peak-regulation and frequency-regulation[J]. Proceedings of the CSEE, 2022, 42(1): 37-56.
[21]	TENG F, DING Z H, HU Z C, et al. Technical review on advanced approaches for electric vehicle charging demand management, part I: Applications in electric power market and renewable energy integration[J]. IEEE Transactions on Industry Applications, 2020, 56(5): 5684-5694.
[22]	BARINGO L, SáNCHEZ AMARO R. A stochastic robust optimization approach for the bidding strategy of an electric vehicle aggregator[J]. Electric Power Systems Research, 2017, 146: 362-370.
[23]	SONG M, AMELIN M, WANG X, et al. Planning and operation models for EV sharing community in spot and balancing market[J]. IEEE Transactions on Smart Grid, 2019, 10(6): 6248-6258.
[24]	PORRAS Á, FERNÁNDEZ-BLANCO R, MORALES J M, et al. An efficient robust approach to the day-ahead operation of an aggregator of electric vehicles[J]. IEEE Transactions on Smart Grid, 2020, 11(6): 4960-4970.
[25]	AFZALI P, RASHIDINEJAD M, ABDOLLAHI A, et al. Risk-constrained bidding strategy for demand response, green energy resources, and plug-In electric vehicle in a flexible smart grid[J]. IEEE Systems Journal, 2021, 15(1): 338-345.
[26]	詹祥澎, 杨军, 韩思宁, 等. 考虑电动汽车可调度潜力的充电站两阶段市场投标策略[J]. 电力系统自动化, 2021, 45(10): 86-96.
	ZHAN Xiangpeng, YANG Jun, HAN Sining, et al. Two-stage market bidding strategy of charging station considering schedulable potential capacity of electric vehicle[J]. Automation of Electric Power Systems, 2021, 45(10): 86-96.
[27]	ORTEGA-VAZQUEZ M A, BOUFFARD F, SILVA V. Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement[J]. IEEE Transactions on Power Systems, 2013, 28(2): 1806-1815.
[28]	WU T, ROTHLEDER M, ALAYWAN Z, et al. Pricing energy and ancillary services in integrated market systems by an optimal power flow[J]. IEEE Transactions on Power Systems, 2004, 19(1): 339-347.
[29]	KARGARIAN A, MEHRTASH M, FALAHATI B. Decentralized implementation of unit commitment with analytical target cascading: A parallel approach[J]. IEEE Transactions on Power Systems, 2018, 33(4): 3981-3993.
[30]	谢敏, 胡昕彤, 刘明波. 目标级联分析法在完全竞争发电市场迭代竞价机制中的应用[J]. 电力系统自动化, 2020, 44(6): 106-112.
	XIE Min, HU Xintong, LIU Mingbo. Application of analytical target cascading in iterative bidding mechanism of complete competitive power generation market[J]. Automation of Electric Power Systems, 2020, 44(6): 106-112.
[31]	SHAHIDEHPOUR M, WANG Y Y. Appendix C: IEEE 30 bus system data[EB/OL]. [2003-06-05](2023-06-13). https://ieeexplore.ieee.org/document/5225036.
[32]	GUO Y, CHEN C, TONG L. Pricing multi-interval dispatch under uncertainty, part I: Dispatch-following incentives[J]. IEEE Transactions on Power Systems, 2021, 36(5): 3865-3877.

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对象	购能费用/元
对象	场景1	场景2	场景3
VPP1	118392	91466	83668
VPP2	247826	208825	193741
VPP3	58615	50543	49760
VPP4	95285	89284	92085
VPP5	195258	150644	147212
VPP6	131562	99567	98282
VPP总计	846938	690328	664748
ISO	3285026	3051999	3026178

场景	集中式新能源利用率	分布式新能源利用率	新能源整体利用率
1	82.88	99.47	87.33
2	89.89	100.00	92.60
3	91.70	100.00	93.93

场景	ISO购买爬坡容量费用	传统发电商提供爬坡容量获利	VPP提供爬坡容量获利
1	48660	48660	0
2	7213	3358	3855
3	2037	973	1064

场景	平均迭代次数	VPP优化申报子问题平均求解时间/ms	ISO优化出清子问题平均求解时间/ms
1	2.2	24.9	217.1
2	14.9	113.3	302.5
3	11.6	104.9	302.7

单次优化出清时长/h	算法收敛成功率/%
单次优化出清时长/h	引入启发式约束条件	未引入启发式约束条件
3	100.00	100.00
6	100.00	98.96

支持虚拟电厂提供灵活爬坡服务的云边协同市场模型

Modeling of Cloud-Edge Collaborated Electricity Market Considering Flexible Ramping Products Provided by VPPs

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