考虑吸热器工作状态的太阳能热发电系统精细化建模与分布鲁棒机组组合研究

林克曼; 项文昕; 顾然; 王璨珉; 吴峰; 白建波

doi:10.19912/j.0254-0096.tynxb.2024-1034

PDF(3680 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (11) : 383-394. DOI: 10.19912/j.0254-0096.tynxb.2024-1034

考虑吸热器工作状态的太阳能热发电系统精细化建模与分布鲁棒机组组合研究

林克曼¹, 项文昕¹, 顾然¹, 王璨珉², 吴峰¹, 白建波³

作者信息 +

DETAILED MODELING AND DISTRIBUTIONALLY ROBUST UNIT COMMITMENT INTEGRATING CONCENTRATING SOLAR POWER PLANT CONSIDERING OPERATION MODE OF CENTRAL RECEIVER

Lin Keman¹, Xiang Wenxin¹, Gu Ran¹, Wang Canmin², Wu Feng¹, Bai Jianbo³

Author information +

文章历史 +

摘要

考虑太阳能热发电系统吸热器工作状态,提出含光热发电系统的分布鲁棒机组组合决策方法,该方法充分考虑光照不确定性对光热发电系统出力的影响,保障决策的鲁棒性和经济性。首先,建立考虑吸热器工作状态的光热发电系统精细化模型,准确描述光-热-电全过程特性和实时储热状态;其次,基于光伏与光热互补运行机制,建立以运行总成本最优为目标的分布鲁棒机组组合模型,采用列与约束生成算法求解;最后,通过历史数据聚类生成典型场景,验证了所提方法的有效性。

Abstract

The combined operation of concentrating solar power and photovoltaics（PV） is an effective way to deal with the curtailment of solar power generation. The unit commitment problem involving concentrating solar power （CSP） system is solved by the proposed distributionally robust optimization method considering the receiver operation mode. The proposed method fully considers the uncertainties of CSP and PV output while ensuring the robustness and economic efficiency. Firstly, a detailed model of CSP plant considering the receiver operational mode is built taking account of the real-time state of thermal energy storage as well as the solar-thermal-electric energy conversion process. Secondly, a distributionally robust optimization method for the unit commitment model is proposed based on the complementary operation mechanism between CSP and PV. The columns and constraints generation algorithm is employed to solve the optimization problem and the total operation cost is minimized. Finally, typical scenarios are generated through historical data clustering, and the effectiveness of the proposed method is verified by the numerical results.

导出引用

林克曼, 项文昕, 顾然, 王璨珉, 吴峰, 白建波. 考虑吸热器工作状态的太阳能热发电系统精细化建模与分布鲁棒机组组合研究[J]. 太阳能学报. 2025, 46(11): 383-394 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1034

Lin Keman, Xiang Wenxin, Gu Ran, Wang Canmin, Wu Feng, Bai Jianbo. DETAILED MODELING AND DISTRIBUTIONALLY ROBUST UNIT COMMITMENT INTEGRATING CONCENTRATING SOLAR POWER PLANT CONSIDERING OPERATION MODE OF CENTRAL RECEIVER[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 383-394 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1034

中图分类号： TM615

参考文献

[1] 朱凌志, 董存, 陈宁, 等. 新能源发电建模与并网仿真技术[M]. 北京: 中国水利水电出版社, 2018.
ZHU L Z, DONG C, CHEN N, et al.Modeling and simulation for grid integration of renewable energy generation[M]. Beijing: China Water & Power Press, 2018.
[2] 姚玉璧, 郑绍忠, 杨扬, 等. 中国太阳能资源评估及其利用效率研究进展与展望[J]. 太阳能学报, 2022, 43(10): 524-535.
YAO Y B, ZHENG S Z, YANG Y, et al.Progress and prospects on solar energy resource evaluation and utilization efficiency in China[J]. Acta energiae solaris sinica, 2022, 43(10): 524-535.
[3] 杜尔顺, 张宁, 康重庆, 等. 太阳能光热发电并网运行及优化规划研究综述与展望[J]. 中国电机工程学报, 2016, 36(21): 5765-5775.
DU E S, ZHANG N, KANG C Q, et al.Reviews and prospects of the operation and planning optimization for grid integrated concentrating solar power[J]. Proceedings of the CSEE, 2016, 36(21): 5765-5775.
[4] 陈伟, 刘文翰, 魏占宏, 等. 计及需求响应的光热电站参与深度调峰的分层优化调度策略[J]. 太阳能学报, 2024, 45(3): 579-590.
CHEN W, LIU W H, WEI Z H, et al.Hierarchical optimal scheduling strategy for concentrating solar power participating in deep peak shaving considering demand response[J]. Acta energiae solaris sinica, 2024, 45(3): 579-590.
[5] 崔杨, 张汇泉, 仲悟之, 等. 计及价格型需求响应及CSP电站参与的风电消纳日前调度[J]. 电网技术, 2020, 44(1): 183-191.
CUI Y, ZHANG H Q, ZHONG W Z, et al.Day-ahead scheduling considering participation of price-based demand response and CSP plant in wind power accommodation[J]. Power system technology, 2020, 44(1): 183-191.
[6] 熊伟, 马志程, 张晓英, 等. 计及风、光消纳的风电-光伏-光热互补发电二层优化调度[J]. 太阳能学报, 2022, 43(7): 39-48.
XIONG W, MA Z C, ZHANG X Y, et al.Two-layer optimal dispatch of WF-PV-CSP hybrid power generation considering wind power and photovoltaic consumption[J]. Acta energiae solaris sinica, 2022, 43(7): 39-48.
[7] DU E S, ZHANG N, HODGE B M, et al.Operation of a high renewable penetrated power system with CSP plants: a look-ahead stochastic unit commitment model[J]. IEEE transactions on power systems, 2019, 34(1): 140-151.
[8] 雷宇, 杨明, 韩学山. 基于场景分析的含风电系统机组组合的两阶段随机优化[J]. 电力系统保护与控制, 2012, 40(23): 58-67.
LEI Y, YANG M, HAN X S.A two-stage stochastic optimization of unit commitment considering wind power based on scenario analysis[J]. Power system protection and control, 2012, 40(23): 58-67.
[9] 刘斌, 刘锋, 王程, 等. 考虑风电场灵活性及出力不确定性的机组组合[J]. 电网技术, 2015, 39(3): 730-736.
LIU B, LIU F, WANG C, et al.Unit commitment considering flexibility and uncertainty of wind power generation[J]. Power system technology, 2015, 39(3): 730-736.
[10] LU R Z, DING T, QIN B Y, 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.
[11] NING C, YOU F Q.Data-driven stochastic robust optimization: general computational framework and algorithm leveraging machine learning for optimization under uncertainty in the big data era[J]. Computers & chemical engineering, 2018, 111: 115-133.
[12] 于丹文, 杨明, 翟鹤峰, 等. 鲁棒优化在电力系统调度决策中的应用研究综述[J]. 电力系统自动化, 2016, 40(7): 134-143, 148.
YU D W, YANG M, ZHAI H F, et al.An overview of robust optimization used for power system dispatch and decision-making[J]. Automation of electric power systems, 2016, 40(7): 134-143, 148.
[13] 范刘洋, 汪可友, 李国杰, 等. 计及风电时间相关性的鲁棒机组组合[J]. 电力系统自动化, 2018, 42(18): 91-97, 176.
FAN L Y, WANG K Y, LI G J, et al.Robust unit commitment considering temporal correlation of wind power[J]. Automation of electric power systems, 2018, 42(18): 91-97, 176.
[14] 艾小猛, 韩杏宁, 文劲宇, 等. 考虑风电爬坡事件的鲁棒机组组合[J]. 电工技术学报, 2015, 30(24): 188-195.
AI X M, HAN X N, WEN J Y, et al.Robust unit commitment considering wind power ramp events[J]. Transactions of China Electrotechnical Society, 2015, 30(24): 188-195.
[15] SHANG C, YOU F Q.Distributionally robust optimization for planning and scheduling under uncertainty[J]. Computers & chemical engineering, 2018, 110: 53-68.
[16] XU Y T, DING T, QU M,et al.Adaptive dynamic programming for gas-power network constrained unit commitment to accommodate renewable energy with combined-cycle units[J]. IEEE transactions on sustainable energy, 2020, 11(3): 2028-2039.
[17] 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 H Y, YUN J Y, YAN Z.Distributionally robust optimization based dispatch methodology of flexible ramping products[J]. Proceedings of the CSEE, 2020, 40(19): 6121-6132.
[18] 郑诗程, 许浩, 郎佳红, 等. 计及光伏不确定性的多区域综合能源系统多场景分布鲁棒优化调度[J]. 太阳能学报, 2024, 45(3): 460-469.
ZHENG S C, XU H, LANG J H, et al.Multi-scenario distributed robust optimal scheduling of multi-area integrated energy systems considering photovoltaic uncertainty[J]. Acta energiae solaris sinica, 2024, 45(3): 460-469.
[19] XIONG P, JIRUTITIJAROEN P, SINGH C.A distributionally robust optimization model for unit commitment considering uncertain wind power generation[J]. IEEE transactions on power systems, 2017, 32(1): 39-49.
[20] ZHAO C Y, GUAN Y P.Data-driven stochastic unit commitment for integrating wind generation[J]. IEEE transactions on power systems, 2016, 31(4): 2587-2596.
[21] 施云辉, 王橹裕, 陈玮, 等. 基于风电预测误差聚类的分布鲁棒含储能机组组合[J]. 电力系统自动化, 2019, 43(22): 3-12, 121.
SHI Y H, WANG L Y, CHEN W, et al.Distributed robust unit commitment with energy storage based on forecasting error clustering of wind power[J]. Automation of electric power systems, 2019, 43(22): 3-12, 121.
[22] ZURITA A, MATA-TORRES C, CARDEMIL J M, et al.Assessment of time resolution impact on the modeling of a hybrid CSP-PV plant: a case of study in Chile[J]. Solar energy, 2020, 202: 553-570.
[23] CHEN R Z, SUN H B, GUO Q L, et al.Reducing generation uncertainty by integrating CSP with wind power: an adaptive robust optimization-based analysis[J]. IEEE transactions on sustainable energy, 2015, 6(2): 583-594.
[24] 沙韵, 周明, 杨宏基, 等. 考虑光热电站和直流联络线灵活性的高比例新能源互联系统优化运行[J]. 电网技术, 2020, 44(9): 3306-3313.
SHA Y, ZHOU M, YANG H J, et al.Interconnected power system optimal operation with renewable generation considering flexibility of concentrating solar power plants & HVDC Tie-line[J]. Power system technology, 2020, 44(9): 3306-3313.
[25] 崔杨, 杨志文, 仲悟之, 等. 基于成本最优的含储热光热电站与火电机组联合出力日前调度[J]. 电力自动化设备, 2019, 39(2): 71-77.
CUI Y, YANG Z W, ZHONG W Z, et al.Day-ahead dispatch for output of combined CSP with thermal storage system and thermal power units based on minimized operation cost[J]. Electric power automation equipment, 2019, 39(2): 71-77.
[26] HE G N, CHEN Q X, KANG C Q, et al.Optimal offering strategy for concentrating solar power plants in joint energy, reserve and regulation markets[J]. IEEE transactions on sustainable energy, 2016, 7(3): 1245-1254.
[27] YU D M, EBADI A G, JERMSITTIPARSERT K, et al.Risk-constrained stochastic optimization of a concentrating solar power plant[J]. IEEE transactions on sustainable energy, 2020, 11(3): 1464-1472.
[28] WU Z Y, ZHOU M, WANG J X, et al.Profit-sharing mechanism for aggregation of wind farms and concentrating solar power[J]. IEEE transactions on sustainable energy, 2020, 11(4): 2606-2616.
[29] 贺帅佳, 阮贺彬, 高红均, 等. 分布鲁棒优化方法在电力系统中的理论分析与应用综述[J]. 电力系统自动化, 2020, 44(14): 179-191.
HE S J, RUAN H B, GAO H J, et al.Overview on theory analysis and application of distributionally robust optimization method in power system[J]. Automation of electric power systems, 2020, 44(14): 179-191.
[30] 鲁卓欣, 徐潇源, 严正, 等. 不确定性环境下数据驱动的电力系统优化调度方法综述[J]. 电力系统自动化, 2020, 44(21): 172-183.
LU Z X, XU X Y, YAN Z, et al.Overview on data-driven optimal scheduling methods of power system in uncertain environment[J]. Automation of electric power systems, 2020, 44(21): 172-183.
[31] 税月, 刘俊勇, 高红均, 等. 考虑风电不确定性的电热综合系统分布鲁棒协调优化调度模型[J]. 中国电机工程学报, 2018, 38(24): 7235-7247.
SHUI Y, LIU J Y, GAO H J, et al.A distributionally robust coordinated dispatch model for integrated electricity and heating systems considering uncertainty of wind power[J]. Proceedings of the CSEE, 2018, 38(24): 7235-7247.
[32] DING T, ZENG Z Y, QU M, et al.Two-stage chance-constrained stochastic thermal unit commitment for optimal provision of virtual inertia in wind-storage systems[J]. IEEE transactions on power systems, 2021, 36(4): 3520-3530.
[33] 杨易达, 高红均, 刘挺坚, 等. 考虑CCER收益共享激励火电机组深度调峰的电力系统低碳调度[J]. 电力自动化设备, 2024, 44(9): 121-128.
YANG Y D, GAO H J, LIU T J, et al.Low carbon dispatch of power system considering CCER revenue sharing incentive for deep peak-shaving of thermal power units[J]. Electric power automation equipment, 2024, 44(9): 121-128.