计及风光不确定性的电-热-氢综合能源系统分布鲁棒优化

doi:10.19595/j.cnki.1000-6753.tces.220759

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Abstract The operation flexibility of China's power system is insufficient, and the energy low-carbon transformation faces the problem of how to realize the coordinated development of multiple energy systems. Hydrogen energy storage (HES) can realize power transfer and storage of power system through peak shaving and valley filling of charging and discharging, which is the key to solve the flexibility problem. The existing research on the optimization of integrated energy system containing HES is aimed at the collaborative optimization of renewable energy and hydrogen energy storage, mainly focusing on the peak shaving and valley filling function of HES as energy storage. However, the research results of multiple energy coupling in HES are few, and most of them are electric energy and thermal energy, electric energy and hydrogen energy coupling. The HES coupling model containing electric, thermal and hydrogen energy is worth in-depth study. In view of the above situation, an electricity-heat-hydrogen integrated energy system (EHH-IES) model is established to study the coupling of electricity, heat and hydrogen.
In addition, renewable energy such as wind and solar in the integrated energy system is uncertain, and the high proportion of renewable energy access makes the safety and economic operation of the power system face great challenges, which seriously restricts the development of the integrated energy system. Therefore, combined with the characteristics of stochastic programming and robust optimization, a distributionally robust conditional value at risk (DRCVaR) method, based on moment information such as expectation, variance, and covariance, is proposed to quantify the risk of wind and solar uncertainties. Considering the game relationship between renewable energy and artificial decision makers, aiming at the maximum system revenue, the minimum carbon emissions in the whole life cycle and the minimum uncertainty risk cost, a zero-sum game model of EHH-IES distributed robust optimization problem is established. The model is transformed into a semi-definite programming problem by Lagrange duality principle and then solved. Finally, the effectiveness of the model and method is verified by numerical simulation.
In the case simulation, a comparative analysis is made on whether the HES coupling model is used and whether the DRCVaR method is used to quantify the risk of wind and solar uncertainties. The following conclusions can be drawn: (1) After applying the HES coupling model with multiple energy sources including electricity, heat and hydrogen, the total revenue of the system has increased by 1.4%, and the carbon emissions in the whole life cycle have decreased by 2.9%, which proves that the development and promotion of EHH-IES can give consideration to both economy and low carbon. (2) When adopting the optimization strategy of DRCVaR method to quantify the risk of wind and solar uncertainties, the economic risk of the system is less than the optimization strategy of conditional value at risk (CVaR) method, which shows that DRCVaR method effectively reduces the risk of wind and solar uncertainties to the system. (3) With the improvement of the confidence level, the total income of the system is reduced, the uncertainty risk cost is increased, and the carbon emissions of the whole life cycle are increased. This means that the decision-maker needs to measure according to the actual situation, and set a reasonable confidence level to achieve the coordination and unification of the system economy, low carbon and robustness.

Key words： Integrated energy system hydrogen energy storage distributionally robust optimization uncertainties carbon emission

Received: 07 May 2022

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TM73

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	Wu Mengxue
	Fang Fang

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Wu Mengxue,Fang Fang. Distributionally Robust Optimization of Electricity-Heat-Hydrogen Integrated Energy System with Wind and Solar Uncertainties[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3473-3485.

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[1] 吕佳炜, 张沈习, 程浩忠, 等. 考虑互联互动的区域综合能源系统规划研究综述[J]. 中国电机工程学报, 2021, 41(12): 4001-4021.
Lü Jiawei, Zhang Shenxi, Cheng Haozhong, et al.Review on district-level integrated energy system planning considering interconnection and interaction[J]. Proceedings of the CSEE, 2021, 41(12): 4001-4021.
[2] Jiang Yibao, Wan Can, Botterud A, et al.Efficient robust scheduling of integrated electricity and heat systems: a direct constraint tightening approach[J]. IEEE Transactions on Smart Grid, 2021, 12(4): 3016-3029.
[3] 刁涵彬, 李培强, 王继飞, 等. 考虑电/热储能互补协调的综合能源系统优化调度[J]. 电工技术学报, 2020, 35(21): 4532-4543.
Diao Hanbin, Li Peiqiang, Wang Jifei, et al.Optimal dispatch of integrated energy system considering complementary coordination of electric/thermal energy storage[J]. Transactions of China Electrotechnical Society, 2020, 35(21): 4532-4543.
[4] Hao Junhong, Chen Qun, He Kelun, et al.A heat current model for heat transfer/storage systems and its application in integrated analysis and optimization with power systems[J]. IEEE Transactions on Sustainable Energy, 2020, 11(1): 175-184.
[5] 荆涛, 陈庚, 王子豪, 等. 风光互补发电耦合氢储能系统研究综述[J]. 中国电力, 2022, 55(1): 75-83.
Jing Tao, Chen Geng, Wang Zihao, et al.Research overview on the integrated system of wind-solar hybrid power generation coupled with hydrogen-based energy storage[J]. Electric Power, 2022, 55(1): 75-83.
[6] Tao Yuechuan, Qiu Jing, Lai Shuying, et al.Integrated electricity and hydrogen energy sharing in coupled energy systems[J]. IEEE Transactions on Smart Grid, 2021, 12(2): 1149-1162.
[7] 张昊天, 韦钢, 袁洪涛, 等. 考虑氢-电混合储能的直流配电网优化调度[J]. 电力系统自动化, 2021, 45(14): 72-81.
Zhang Haotian, Wei Gang, Yuan Hongtao, et al.Optimal scheduling of DC distribution network considering hydrogen-power hybrid energy storage[J]. Automation of Electric Power Systems, 2021, 45(14): 72-81.
[8] 李奇, 赵淑丹, 蒲雨辰, 等. 考虑电氢耦合的混合储能微电网容量配置优化[J]. 电工技术学报, 2021, 36(3): 486-495.
Li Qi, Zhao Shudan, Pu Yuchen, Chen Weirong, et al.Capacity optimization of hybrid energy storage microgrid considering electricity-hydrogen coupling[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 486-495.
[9] 滕云, 王泽镝, 金红洋, 等. 用于电网调节能力提升的电热氢多源协调储能系统模型[J]. 中国电机工程学报, 2019, 39(24): 7209-7217, 7494.
Teng Yun, Wang Zedi, Jin Hongyang, et al.A model and coordinated optimization for the multi-energy storage system of electricity heat hydrogen to regulation enhancement of power grid[J]. Proceedings of the CSEE, 2019, 39(24): 7209-7217, 7494.
[10] 司杨, 陈来军, 麻林瑞, 等. 考虑氢储能热平衡的风-氢混合系统优化调度方法[J]. 电器与能效管理技术, 2021(11): 15-21.
Si Yang, Chen Laijun, Ma Linrui, et al.Optimal dispatching method of wind-hydrogen hybrid system considering heat balance of hydrogen energy storage[J]. Electrical & Energy Management Technology, 2021(11): 15-21.
[11] 熊宇峰, 司杨, 郑天文, 等. 基于主从博弈的工业园区综合能源系统氢储能优化配置[J]. 电工技术学报, 2021, 36(3): 507-516.
Xiong Yufeng, Si Yang, Zheng Tianwen, et al.Optimal configuration of hydrogen storage in industrial park integrated energy system based on stackelberg game[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 507-516.
[12] 黄雨涵, 丁涛, 李雨婷, 等. 碳中和背景下能源低碳化技术综述及对新型电力系统发展的启示[J]. 中国电机工程学报, 2021, 41(增刊1): 28-51.
Huang Yuhan, Ding Tao, Li Yuting, et al.Decarbonization technologies and inspirations for the development of novel power systems in the context of carbon neutrality[J]. Proceedings of the CSEE, 2021, 41(S1): 28-51.
[13] Lu Runzhao, Ding Tao, Qin Boyu, et al.Multi-stage stochastic programming to joint economic dispatch for energy and reserve with uncertain renewable energy[J]. IEEE Transactions on Sustainable Energy, 2020, 11(3): 1140-1151.
[14] 王昀, 谢海鹏, 孙啸天, 等. 计及激励型综合需求响应的电-热综合能源系统日前经济调度[J]. 电工技术学报, 2021, 36(9): 1926-1934.
Wang Yun, Xie Haipeng, Sun Xiaotian, et al.Day-ahead economic dispatch for electricity-heating integrated energy system considering incentive integrated demand response[J]. Transactions of China Electrotechnical Society, 2021, 36(9): 1926-1934.
[15] 娄素华, 吕梦璇, 王永灿, 等. 考虑投资风险的含风电系统电源投资扩展规划研究[J]. 中国电机工程学报, 2019, 39(7): 1944-1956.
Lou Suhua, Lü Mengxuan, Wang Yongcan, et al.Generation investment expansion planning for wind power accommodation considering investment risk[J]. Proceedings of the CSEE, 2019, 39(7): 1944-1956.
[16] Li Longfei, Xiao Jie, Zhao Yun, et al.Robust position anti-interference control for PMSM servo system with uncertain disturbance[J]. CES Transactions on Electrical Machines and Systems, 2020, 4(2): 151-160.
[17] 许刚, 张丙旭, 张广超. 电动汽车集群并网的分布式鲁棒优化调度模型[J]. 电工技术学报, 2021, 36(3): 565-578.
Xu Gang, Zhang Bingxu, Zhang Guangchao.Distributed and robust optimal scheduling model for large-scale electric vehicles connected to grid[J]. Transactions of China Electrotechnical Society, 2021, 36(3): 565-578.
[18] Cao Yang, Wei Wei, Mei Shengwei, et al.Analyzing and quantifying the intrinsic distributional robustness of CVaR reformulation for chance-constrained stochastic programs[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4908-4911.
[19] Jabr R A.Distributionally robust CVaR constraints for power flow optimization[J]. IEEE Transactions on Power Systems, 2020, 35(5): 3764-3773.
[20] 李运龙, 李志刚, 郑杰辉. 考虑风电不确定性和相关性的多区域电网分布鲁棒经济调度[J]. 电力自动化设备, 2021, 41(8): 97-104.
Li Yunlong, Li Zhigang, Zheng Jiehui.Distributionally robust economic dispatch of multi-regional power grid considering uncertainty and correlation of wind power[J]. Electric Power Automation Equipment, 2021, 41(8): 97-104.
[21] Huo Yuchong, Bouffard F, Joós G.Spatio-temporal flexibility management in low-carbon power systems[J]. IEEE Transactions on Sustainable Energy, 2020, 11(4): 2593-2605.
[22] 胡静哲, 王旭, 蒋传文, 等. 考虑区域碳排放均衡性的电力系统最优阶梯碳价[J]. 电力系统自动化, 2020, 44(6): 98-105.
Hu Jingzhe, Wang Xu, Jiang Chuanwen, et al.Optimal tiered carbon price of power system considering equilibrium of regional carbon emission[J]. Automation of Electric Power Systems, 2020, 44(6): 98-105.
[23] 梅生伟, 魏韡, 刘锋. 电力系统控制与决策中的博弈问题——工程博弈论初探[J]. 控制理论与应用, 2018, 35(5): 578-587.
Mei Shengwei, Wei Wei, Liu Feng.Game theoretical perspective of power system control and decision making: a brief review of engineering game theory[J]. Control Theory & Applications, 2018, 35(5): 578-587.
[24] 王海洋, 李珂, 张承慧, 等. 基于主从博弈的社区综合能源系统分布式协同优化运行策略[J]. 中国电机工程学报, 2020, 40(17): 5435-5445.
Wang Haiyang, Li Ke, Zhang Chenghui, et al.Distributed coordinative optimal operation of community integrated energy system based on stackelberg game[J]. Proceedings of the CSEE, 2020, 40(17): 5435-5445.
[25] 梅生伟, 郭文涛, 王莹莹, 等. 一类电力系统鲁棒优化问题的博弈模型及应用实例[J]. 中国电机工程学报, 2013, 33(19): 47-56, 20.
Mei Shengwei, Guo Wentao, Wang Yingying, et al.A game model for robust optimization of power systems and its application[J]. Proceedings of the CSEE, 2013, 33(19): 47-56, 20.