面向系统灵活性提升的大用户激励型需求响应优化策略

doi:10.19595/j.cnki.1000-6753.tces.240822

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (1833 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract With the increasing scale of renewable energy such as wind and solar energy into the power system, its generation power volatility and uncertainty make the system net load curve steeper. Independent system operators (ISO) needs to introduce more flexible resources to cope with the surge in flexibility ramping products (FRP) demand. Implementing demand response (DR) for large-scale users has become an important way to improve system flexibility. How to design reasonable incentive compensation measures to guide large-scale users to provide DR services and participate in the FRP market is of great significance to improve the flexibility of the system. Existing studies have proposed reasonable incentive compensation mechanisms and DR strategies under the market environment, but have not considered the relationship between the difference of large-scale users' response willingness and response cost. Therefore, this paper proposed a DR optimization strategy that adapts to the joint clearing of electric energy and FRP, and realized differentiated compensation according to the response willingness of large-scale users.
First, the customer demand management behavior model (CDMBM) was used to estimate the response cost function of users by using the historical DR participation data of large- scale users. Characterizing the differences in user response costs by introducing a response willingness parameter in the cost function. The higher the willingness of large-scale users to respond, the lower the cost of response. CDMBM provided a basis for the accurate modeling of large-scale users' decision behaviors of reduction-type DR and absorption-type DR.
Secondly, an incentive DR master-slave game model was constructed between ISO and multiple large-scale users. The upper level model was the optimization model of joint clearing of electric energy and FRP with ISO as the decision-making body, and the lower level model was the optimization model of DR decision-making with large-scale users participating in the joint market. ISO aimed to minimize incentive compensation costs with the goal of meeting the flexibility requirements, while large-scale users aimed to maximize the benefits of providing DR services. Both parties are interdependent.
Finally, by taking the KKT condition of the lower model as the constraint condition of the upper model, the master-slave game problem was transformed into a mathematical optimization problem with linear equilibrium constraint for solving. The game behavior between ISO and large-scale users was described by the master-slave game equilibrium. The DR unit incentive compensation cost considering user differentiation was obtained according to the equilibrium result of the game between both parties.
The results show that differentiated compensation is carried out according to the response willingness of large-scale users. Large-scale users are effectively encouraged to participate in the joint clearing market of electric energy and FRP market. The unit incentive compensation cost not only meets the needs of large-scale users and ISO, but also effectively alleviates the sharp increase in incentive compensation costs when there is a lack of flexible ramping capability. Large-scale users reserve flexible ramping capacity in the FRP market to provide flexible climbing capacity, which reduce the pressure of thermal power units. The cost of FRP accounts for only 0.31% of the total system cost. The proposed DR optimization strategy enhances the system's flexibility while considering economic efficiency.

Key words： Demand response flexible ramping products (FRP) market master-slave game equilibrium constraints unit incentive compensation cost

Received: 21 May 2024

PACS:

TM73

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Huang Dawei
	Guo Niankang
	Yu Na
	Kong Lingguo

Cite this article:

Huang Dawei,Guo Niankang,Yu Na等. Optimization Strategy for Enhancing System Flexibility Through Incentive Demand Response of Large-Scale Users[J]. Transactions of China Electrotechnical Society, 2024, 39(21): 6778-6792.

URL:

https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.240822 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I21/6778

[1] 韩丽, 王冲, 于晓娇, 等. 考虑风电爬坡灵活调节的碳捕集电厂低碳经济调度[J]. 电工技术学报, 2024, 39(7): 2033-2045.
Han Li, Wang Chong, Yu Xiaojiao, et al.Low-carbon and economic dispatch considering the carbon capture power plants with flexible adjustment of wind power ramp[J]. Transactions of China Electrotechnical Society, 2024, 39(7): 2033-2045.
[2] 杨雪, 王明强, 胡召永, 等. 精细考虑时刻间净负荷波动不确定性的机组组合[J]. 高电压技术, 2023, 49(11): 4839-4848.
Yang Xue, Wang Mingqiang, Hu Zhaoyong, et al.Unit commitment considering fluctuation uncertainty of net load between adjacent periods[J]. High Voltage Engineering, 2023, 49(11): 4839-4848.
[3] Shen Bo, Ghatikar G, Lei Zeng, et al.The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges[J]. Applied Energy, 2014, 130: 814-823.
[4] Asadinejad A, Tomsovic K.Optimal use of incentive and price based demand response to reduce costs and price volatility[J]. Electric Power Systems Research, 2017, 144: 215-223.
[5] 吴珊, 边晓燕, 张菁娴, 等. 面向新型电力系统灵活性提升的国内外辅助服务市场研究综述[J]. 电工技术学报, 2023, 38(6): 1662-1677.
Wu Shan, Bian Xiaoyan, Zhang Jingxian, et al.A review of domestic and foreign ancillary services market for improving flexibility of new power system[J]. Transactions of China Electrotechnical Society, 2023, 38(6): 1662-1677.
[6] 潘郑楠, 邓长虹, 徐慧慧, 等. 考虑灵活性补偿的高比例风电与多元灵活性资源博弈优化调度[J]. 电工技术学报, 2023, 38(增刊1): 56-69.
Pan Zhengnan, Deng Changhong, Xu Huihui, et al.Game optimization scheduling of high proportion wind power and multiple flexible resources considering flexibility compensation[J]. Transactions of China Electrotechnical Society, 2023, 38(S1): 56-69.
[7] 郑若楠, 李志浩, 唐雅洁, 等. 考虑居民用户参与度不确定性的激励型需求响应模型与评估[J]. 电力系统自动化, 2022, 46(8): 154-162.
Zheng Ruonan, Li Zhihao, Tang Yajie, et al.Incentive demand response model and evaluation considering uncertainty of residential customer participation degree[J]. Automation of Electric Power Systems, 2022, 46(8): 154-162.
[8] 张全明, 崔晓昱, 张笑弟, 等. 计及用户不确定性的多时段耦合需求响应激励优化策略[J]. 中国电机工程学报, 2022, 42(24): 8844-8853.
Zhang Quanming, Cui Xiaoyu, Zhang Xiaodi, et al.Incentive optimization strategy of multi period coupling demand response considering user uncertainty[J]. Proceedings of the CSEE, 2022, 42(24): 8844-8853.
[9] 郭昆健, 高赐威, 林国营, 等. 现货市场环境下售电商激励型需求响应优化策略[J]. 电力系统自动化, 2020, 44(15): 28-35.
Guo Kunjian, Gao Ciwei, Lin Guoying, et al.Optimization strategy of incentive based demand response for electricity retailer in spot market environment[J]. Automation of Electric Power Systems, 2020, 44(15): 28-35.
[10] 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.
[11] 胡嘉骅. 电力系统灵活性提升方法及灵活调节产品获取机制[D]. 杭州: 浙江大学, 2018.
Hu Jiahua.Power system flexibility improvement method and flexible adjustment product acquisition mechanism[D]. Hangzhou: Zhejiang University, 2018.
[12] 武昭原, 周明, 王剑晓, 等. 双碳目标下提升电力系统灵活性的市场机制综述[J]. 中国电机工程学报, 2022, 42(21): 7746-7763.
Wu Zhaoyuan, Zhou Ming, Wang Jianxiao, et al.Review on market mechanism to enhance the flexibility of power system under the dual-carbon target[J]. Proceedings of the CSEE, 2022, 42(21): 7746-7763.
[13] Wu Chenye, Hug G, Kar S.A functional approach to assessing flexible ramping products' impact on electricity market[C]//2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), Washington, DC, USA, 2015: 1-5.
[14] Zhao Yuzhou.Real-time dispatch of flexible ramping products for energy storage and demand response[C]// 2019 IEEE Innovative Smart Grid Technologies -Asia (ISGT Asia), Chengdu, China, 2019: 1936-1941.
[15] Hu Jiahua, Wen Fushuan, Wang Ke, et al.Simultaneous provision of flexible ramping product and demand relief by interruptible loads considering economic incentives[J]. Energies, 2017, 11(1): 46.
[16] Makhdoomi H, Moshtagh J.Optimal scheduling of electrical storage system and flexible loads to participate in energy and flexible ramping product markets[J]. Journal of Operation and Automation in Power Engineering, 2023, 11(3): 203-212.
[17] Khoshjahan M, Dehghanian P, Moeini-Aghtaie M, et al.Harnessing ramp capability of spinning reserve services for enhanced power grid flexibility[J]. IEEE Transactions on Industry Applications, 2019, 55(6): 7103-7112.
[18] Fahrioglu M, Alvarado F L.Using utility information to calibrate customer demand management behavior models[C]//2002 IEEE Power Engineering Society Winter Meeting, New York, NY, USA, 2002: 317-322.
[19] Wang Qin, Hodge B M.Enhancing power system operational flexibility with flexible ramping products: a review[J]. IEEE Transactions on Industrial Informatics, 2017, 13(4): 1652-1664.
[20] Sreekumar S, Yamujala S, Sharma K C, et al.Flexible Ramp Products: a solution to enhance power system flexibility[J]. Renewable and Sustainable Energy Reviews, 2022, 162: 112429.
[21] Villar J, Bessa R, Matos M.Flexibility products and markets: Literature review[J]. Electric Power Systems Research, 2018, 154: 329-340.
[22] Xu Lin, Tretheway D.Flexible ramping products[R]. CAISO Proposal, 2012.
[23] 刘英琪. 高比例风电参与的电力市场灵活运营策略研究[D]. 广州: 华南理工大学, 2020.
Liu Yingqi.Research on flexible operation strategy of power market with high proportion of wind power participation[D].Guangzhou: South China University of Technology, 2020.
[24] 钟佳宇, 陈皓勇, 陈武涛, 等. 含灵活性资源交易的电力市场实时出清[J]. 电网技术, 2021, 45(3): 1032-1040.
Zhong Jiayu, Chen Haoyong, Chen Wutao, et al.Real-time clearing of electricity markets with flexible resource transactions[J]. Power System Technology, 2021, 45(3): 1032-1040.
[25] 王玲玲, 刘恋, 张锞, 等. 电力系统灵活调节服务与市场机制研究综述[J]. 电网技术, 2022, 46(2): 442-452.
Wang Lingling, Liu Lian, Zhang Ke, et al.A review of power system flexible ramping product and market mechanism[J]. Power System Technology, 2022, 46(2): 442-452.
[26] Von Stackelberg H.Marketform and gleichgewicht[M]. Vienna: Springer, 1934.
[27] Fortuny-Amat J, McCarl B. A representation and economic interpretation of a two-level programming problem[J]. Journal of the Operational Research Society, 1981, 32(9): 783-792.
[28] Li Fangxing, Bo Rui.Small test systems for power system economic studies[C]//IEEE PES General Meeting, Minneapolis, MN, USA, 2010: 1-4.
[29] Kazarlis S A, Bakirtzis A G, Petridis V.A genetic algorithm solution to the unit commitment problem[J]. IEEE Transactions on Power Systems, 1996, 11(1): 83-92.
[30] Park H, Baldick R.Transmission planning under uncertainties of wind and load: sequential approximation approach[J]. IEEE Transactions on Power Systems, 2013, 28(3): 2395-2402.