基于伊藤过程的电制氢合成氨负荷随机最优控制

doi:10.11930/j.issn.1004-9649.202209056

中国电力 ›› 2023, Vol. 56 ›› Issue (7): 66-77.DOI: 10.11930/j.issn.1004-9649.202209056

• 面向新型电力系统的氢能及其系统集成控制关键技术 • 上一篇下一篇

基于伊藤过程的电制氢合成氨负荷随机最优控制

杨国山¹, 朱杰², 宋汶秦¹, 邱一苇², 周步祥²

1. 国网甘肃省电力公司经济技术研究院，甘肃兰州 730000;
2. 四川大学电气工程学院，四川成都 610065

收稿日期:2022-09-15 修回日期:2023-05-20 发布日期:2023-07-28
作者简介:杨国山(1977-),男,硕士,高级工程师,从事新型电力系统、新能源规划以及新型储能技术等研究,E-mail:yguoshan@163.com;朱杰朱杰(1997-),男,博士研究生,从事电力系统优化调度、电氢耦合系统研究,E-mail:jiezhu@stu.scu.edu.cn;宋汶秦(1983-),女,硕士,高级工程师,从事电力系统规划研究,E-mail:13919376765@163.com;邱一苇(1991-),男,通信作者,博士,副研究员,博士生导师,从事电力系统不确定性、电制氢技术、电氢耦合系统等研究,E-mail:ywqiu@scu.edu.cn;周步祥(1965-),男,博士,教授,博士生导师,从事电力系统规划、信息物理系统、综合能源系统等研究,E-mail:hiway_scu@126.com
基金资助:
国网甘肃省电力公司重点科技项目（基于氢电高效转化的氢能系统参与电网调峰调频辅助服务的研究，SGGSJY00NYJS2200024）；国家自然科学基金资助项目（基于伊藤理论的高比例可再生能源电力系统分析与控制技术研究， 51907099）。

Flexible Load Stochastic Optimal Control of Wind Power-Based Hydrogen Production and Ammonia Synthesis Systems Based on the Itô Process

YANG Guoshan¹, ZHU Jie², SONG Wenqin¹, QIU Yiwei², ZHOU Buxiang²

1. Economic and Technological Research Institute of Gansu Electric Power Corp., Lanzhou 730050, China;
2. College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Received:2022-09-15 Revised:2023-05-20 Published:2023-07-28
Supported by:
This work is supported by Gansu Electric Power Corp. Key Science and Technology Project (Research on Hydrogen Energy System Participating in Power Grid Peak Load Regulation and Frequency Regulation Auxiliary Service Based on High-Efficiency Hydrogen-to-Electricity Conversion, No.SGGSJY00NYJS2200024), the National Natural Science Foundation of China (On the Analysis and Control Technologies for Power Systems with High-Proportion Renewable Energies Based on Itô Theory, No.51907099).

摘要/Abstract

摘要： 风电制氢进而合成氨（power to ammonia，P2A）是规模化消纳可再生发电资源，实现电力与化工行业碳减排的潜在技术路线之一。利用电制氢作为媒介，P2A可作为大型工业负荷参与电网能量平衡调节。然而，P2A负荷受化学工艺及过程控制的限制，负载调控惯性较大，当风电出力偏离预测轨迹时P2A负荷难以快速响应。为此，提出计及风电出力时序不确定性的P2A负荷随机最优控制方法。首先建立P2A系统柔性调控的状态空间模型。其次，考虑合成氨工段的调节惯性与风电出力时序相关性的耦合影响，基于伊藤过程建模风电出力的不确定性，构造随机动力学约束的P2A系统优化控制模型。之后，基于动态轨迹灵敏度分解，将随机动力学优化问题变换为确定性二阶锥规划，并采用随机模型预测控制（stochastic model predictive control，SMPC）滚动求解，有效避免了传统基于随机抽样模拟的方法计算复杂度高、求解效率低的问题。算例分析表明，与确定性控制相比，所提方法能够充分发挥合成氨柔性生产的优势，提升P2A负荷消纳波动性风电的能力。

关键词: 风电制氢, 合成氨, 绿氨, 伊藤过程, 时序相关性, 随机模型预测控制

Abstract: Wind power-based hydrogen production and ammonia synthesis (P2A) is one of the potential technical routes for large-scale renewable energy consumption and carbon emission reduction in the power and chemical industries. Taking power-to-hydrogen production as a medium, P2A can participate in grid balancing regulation as a large-scale industrial load. However, due to the limitation of the chemical process and control of the ammonia synthesis load, the inertia of load regulation is large. When the wind power output deviates from the predicted trajectory, it is difficult for the P2A load to respond quickly. Thus, a stochastic optimal control method for the P2A load considering temporally correlated uncertainty of wind power is proposed. Firstly, the flexible regulation state-space model of the P2A system is established. Second, considering the coupling of the stochastic process of wind power output and the adjustment inertia of the ammonia synthesis section, the Itô process is used to model the stochastic process of wind power output, and a stochastic dynamics-constrained optimal control model of the P2A system is constructed. Then, the stochastic optimization problem is transformed into a deterministic second-order cone programming by the trajectory sensitivity decomposition and solved by the stochastic model predictive control (SMPC) in a rolling-horizon manner, avoiding the disadvantages of high computational complexity and low efficiency of the traditional sampling-based stochastic control methods. The case study shows that, compared with deterministic control, the proposed method fully takes advantage of the flexible production of ammonia synthesis and greatly improves the ability of P2A Load to consume the fluctuating wind power.

Key words: wind power-based hydrogen production, ammonia synthesis, green ammonia, Itô process, temporal correlation, stochastic model predictive control

杨国山, 朱杰, 宋汶秦, 邱一苇, 周步祥. 基于伊藤过程的电制氢合成氨负荷随机最优控制[J]. 中国电力, 2023, 56(7): 66-77.

YANG Guoshan, ZHU Jie, SONG Wenqin, QIU Yiwei, ZHOU Buxiang. Flexible Load Stochastic Optimal Control of Wind Power-Based Hydrogen Production and Ammonia Synthesis Systems Based on the Itô Process[J]. Electric Power, 2023, 56(7): 66-77.

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参考文献

[1] 徐钢, 薛小军, 张钟, 等. 一种基于电解水制氢及甲醇合成的碳中和能源技术路线[J]. 中国电机工程学报, 2023, 43(1): 191–201
XU Gang, XUE Xiaojun, ZHANG Zhong, et al. A new carbon neutral energy technology route based on electrolytic water to hydrogen and methanol synthesis[J]. Proceedings of the CSEE, 2023, 43(1): 191–201
[2] 全国煤化工信息总站. 首套千吨级太阳能发电、电解水制氢、二氧化碳与氢气合成甲醇示范项目试车成功[J]. 煤化工, 2020, 48(1): 60
[3] 达茂旗风光制氢与绿色灵活化工一体化项目开工. [EB/OL]. [2022-3-25](2023-01-10). https://www.cpnn.com.cn/news/kj/202207/t20220706_1530664_wap.html.
[4] 中国氢能联盟. 中国氢能源及燃料电池产业白皮书(2020)[R]. 2020.4. 21.
[5] KLYAPOVSKIY S, ZHENG Y, YOU S, et al. Optimal operation of the hydrogen-based energy management system with P2X demand response and ammonia plant[J]. Applied Energy, 2021, 304: 117559.
[6] 朱伟业, 罗毅, 胡博, 等. 热负荷弹性与分时电价需求侧响应协同促进碳减排的电热优化调度[J]. 电网技术, 2021, 45(10): 3803–3813
ZHU Weiye, LUO Yi, HU Bo, et al. Optimized combined heat and power dispatch considering decreasing carbon emission by coordination of heat load elasticity and time-of-use demand response[J]. Power System Technology, 2021, 45(10): 3803–3813
[7] 郭梦婕, 严正, 周云, 等. 含风电制氢装置的综合能源系统优化运行[J]. 中国电力, 2020, 53(1): 115–123,161
GUO Mengjie, YAN Zheng, ZHOU Yun, et al. Optimized operation design of integrated energy system with wind power hydrogen production[J]. Electric Power, 2020, 53(1): 115–123,161
[8] 杨辰星. 公共楼宇空调负荷参与电网调峰关键技术研究[D]. 南京: 东南大学, 2017.
YANG Chenxing. Research on key technologies for peak load shaving by public buildings’air conditioning loads[D]. Nanjing: Southeast University, 2017.
[9] 曹昉, 郭培林, 王科, 等. 等室内舒适指数比调整的空调群负荷响应方法[J]. 中国电力, 2017, 50(11): 152–157
CAO Fang, GUO Peilin, WANG Ke, et al. Load response method of air conditioning groups based on equalize proportion adjustment of indoor comfort index[J]. Electric Power, 2017, 50(11): 152–157
[10] BAO Y, XU J, FENG W, et al. Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants[J]. Applied Energy, 2019, 241: 302–312.
[11] LIAO S Y, XU J, SUN Y Z, et al. Control of energy-intensive load for power smoothing in wind power plants[J]. IEEE Transactions on Power Systems, 2018, 33(6): 6142–6154.
[12] 彭生江, 孙传帅, 妥建军, 等. 面向统一能源系统的中长期氢负荷预测[J]. 中国电力, 2022, 55(1): 84–90
PENG Shengjiang, SUN Chuanshuai, TUO Jianjun, et al. Medium and long-term hydrogen load forecast for unified energy system[J]. Electric Power, 2022, 55(1): 84–90
[13] 袁铁江, 孙传帅, 谭捷, 等. 考虑氢负荷的新型电力系统电源规划[J]. 中国电机工程学报, 2022, 42(17): 6316–6326
YUAN Tiejiang, SUN Chuanshuai, TAN Jie, et al. Generation planning of new power system considering hydrogen load[J]. Proceedings of the CSEE, 2022, 42(17): 6316–6326
[14] 蒋东方, 贾跃龙, 鲁强, 等. 氢能在综合能源系统中的应用前景[J]. 中国电力, 2020, 53(5): 135–142
JIANG Dongfang, JIA Yuelong, LU Qiang, et al. Application prospect of hydrogen energy in integrated energy systems[J]. Electric Power, 2020, 53(5): 135–142
[15] SÁNCHEZ A, MARTÍN M. Optimal renewable production of ammonia from water and air[J]. Journal of Cleaner Production, 2018, 178: 325–342.
[16] XU D, ZHOU B, WU Q W, et al. Integrated modelling and enhanced utilization of power-to-ammonia for high renewable penetrated multi-energy systems[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4769–4780.
[17] ZHENG Y, YOU S, LI X M, et al. Data-driven robust optimization for optimal scheduling of power to methanol[J]. Energy Conversion and Management, 2022, 256: 115338.
[18] 伍海华, 杨德平. 随机过程: 金融资产定价之应用[M]. 北京: 中国金融出版社, 2002.
[19] 陈晓爽, 林今, 刘锋, 等. 新能源发电的伊藤随机过程模型[J]. 中国电机工程学报, 2020, 40(1): 83–95, 376
CHEN Xiaoshuang, LIN Jin, LIU Feng, et al. Itô stochastic process model for renewable generations[J]. Proceedings of the CSEE, 2020, 40(1): 83–95, 376
[20] ZHANG Y, KONG L Q. Photovoltaic power prediction based on hybrid modeling of neural network and stochastic differential equation[J]. ISA Transactions, 2022, 128: 181–206.
[21] CHEN X S, LIN J, LIU F, et al. Optimal control of AGC systems considering non-gaussian wind power uncertainty[J]. IEEE Transactions on Power Systems, 2019, 34(4): 2730–2743.
[22] CHEN X S, LIN J, LIU F, et al. Stochastic assessment of AGC systems under non-gaussian uncertainty[J]. IEEE Transactions on Power Systems, 2019, 34(1): 705–717.
[23] OJO G, CAMARDA K. Sustainable ammonia production via electrolysis and haber-bosch process[M]//Computer Aided Chemical Engineering. Amsterdam: Elsevier, 2022: 229–234.
[24] 袁铁江, 万志, 王进君, 等. 考虑电解槽启停特性的制氢系统日前出力计划[J]. 中国电力, 2022, 55(1): 101–109
YUAN Tiejiang, WAN Zhi, WANG Jinjun, et al. The day-ahead output plan of hydrogen production system considering the start-stop characteristics of electrolytic cell[J]. Electric Power, 2022, 55(1): 101–109
[25] LI J R, LIN J, HEUSER P M, et al. Co-planning of regional wind resources-based ammonia industry and the electric network: a case study of inner Mongolia[J]. IEEE Transactions on Power Systems, 2022, 37(1): 65–80.
[26] VERLEYSEN K, COPPITTERS D, PARENTE A, et al. How can power-to-ammonia be robust? optimization of an ammonia synthesis plant powered by a wind turbine considering operational uncertainties[J]. Fuel, 2020, 266: 117049.
[27] QIU Y W, LIN J, LIU F, et al. Stochastic online generation control of cascaded run-of-the-river hydropower for mitigating solar power volatility[J]. IEEE Transactions on Power Systems, 2020, 35(6): 4709–4722.
[28] HISKENS I A, ALSEDDIQUI J. Sensitivity, approximation, and uncertainty in power system dynamic simulation[J]. IEEE Transactions on Power Systems, 2006, 21(4): 1808–1820.
[29] ARMIJO J, PHILIBERT C. Flexible production of green hydrogen and ammonia from variable solar and wind energy: case study of Chile and Argentina[J]. International Journal of Hydrogen Energy, 2020, 45(3): 1541–1558.
[30] SIDDIQUI O, ISHAQ H, CHEHADE G, et al. Experimental investigation of an integrated solar powered clean hydrogen to ammonia synthesis system[J]. Applied Thermal Engineering, 2020, 176: 115443.
[31] PJM Data Viewer - Market clearing prices [EB/OL]. [2022-09-02](2023-03-01).https://dataviewer.pjm.com/.
[32] LIN J, SUN Y Z, CHENG L, et al. Assessment of the power reduction of wind farms under extreme wind condition by a high resolution simulation model[J]. Applied Energy, 2012, 96: 21–32.

基于伊藤过程的电制氢合成氨负荷随机最优控制

Flexible Load Stochastic Optimal Control of Wind Power-Based Hydrogen Production and Ammonia Synthesis Systems Based on the Itô Process

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基于伊藤过程的电制氢合成氨负荷随机最优控制

Flexible Load Stochastic Optimal Control of Wind Power-Based Hydrogen Production and Ammonia Synthesis Systems Based on the Itô Process

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