RESEARCH ON OPERATION STRATEGY OF INTEGRATED POWER GENERATION SYSTEM OF “SOLAR-FIRED-STORAGE”

Wang Qiang, Li Bin, Zhang Jinhong, Wang Yumeng, Yang Jianmeng

Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 153-161.

PDF(2068 KB)
Welcome to visit Acta Energiae Solaris Sinica, Today is
PDF(2068 KB)
Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (11) : 153-161. DOI: 10.19912/j.0254-0096.tynxb.2023-1240

RESEARCH ON OPERATION STRATEGY OF INTEGRATED POWER GENERATION SYSTEM OF “SOLAR-FIRED-STORAGE”

  • Wang Qiang1, Li Bin1, Zhang Jinhong2, Wang Yumeng3, Yang Jianmeng1
Author information +
History +

Abstract

In order to improve the consumption of new energy and promote the flexibility transformation of thermal power units, taking the “solar-fired-storage” integrated power generation system as the research object, the Bayesian algorithm was used to optimize the long-term and short-term memory neural network, and a load demand forecasting model on the power consumption side was established. Based on the analysis of the output characteristics of coal-fired power plants (CFPP), the operation strategy of the “solar-fired-storage”integrated power generation system to stabilize CFPP output fluctuations is proposed. The results show that in terms of thermal performance, the adverse effects on CFPP after being coupled with energy storage can be offset. In terms of output fluctuation characteristics, compared with the original CFPP, the output fluctuation rate of the unit for 7 days of continuous operation is reduced by 7.16%, and the optimization effect is significant.

Key words

solar thermal energy / heat storage / compressed air energy storage / load forecasting / operation strategy

Cite this article

Download Citations
Wang Qiang, Li Bin, Zhang Jinhong, Wang Yumeng, Yang Jianmeng. RESEARCH ON OPERATION STRATEGY OF INTEGRATED POWER GENERATION SYSTEM OF “SOLAR-FIRED-STORAGE”[J]. Acta Energiae Solaris Sinica. 2024, 45(11): 153-161 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1240

References

[1] ZHANG K Z, LIU M, ZHAO Y L, et al.Thermo-economic optimization of the thermal energy storage system extracting heat from the reheat steam for coal-fired power plants[J]. Applied thermal engineering, 2022, 215: 119008.
[2] DAI Y T, PREECE R, PANTELI M.Risk assessment of cascading failures in power systems with increasing wind penetration[J]. Electric power systems research, 2022, 211: 108392.
[3] 张玥, 谢光龙, 张全, 等. 美国得州2·15大停电事故分析及对中国电力发展的启示[J]. 中国电力, 2021, 54(4): 192-198, 206.
ZHANG Y, XIE G L, ZHANG Q, et al.Analysis of 2·15 power outage in Texas and its implications for the power sector of China[J]. Electric power, 2021, 54(4): 192-198, 206.
[4] 张恒旭, 高志民, 曹永吉, 等. 高比例可再生能源接入下电力系统惯量研究综述及展望[J]. 山东大学学报(工学版), 2022, 52(5): 1-13.
ZHANG H X, GAO Z M, CAO Y J, et al.Review and prospect of research on power system inertia with high penetration of renewable energy source[J]. Journal of Shandong University (engineering science), 2022, 52(5): 1-13.
[5] 彭大健, 裴玮, 肖浩, 等. 数据驱动的用户需求响应行为建模与应用[J]. 电网技术, 2021, 45(7): 2577-2585.
PENG D J, PEI W, XIAO H, et al.Data-driven consumer demand response behavior modelization and application[J]. Power system technology, 2021, 45(7): 2577-2585.
[6] LAI C S, YANG Y X, PAN K D, et al.Multi-view neural network ensemble for short and mid-term load forecasting[J]. IEEE transactions on power systems, 2021, 36(4): 2992-3003.
[7] RAFI S H, NAHID-AL-MASOOD, DEEBA S R, et al.A short-term load forecasting method using integrated CNN and LSTM network[J]. IEEE access, 2019, 9: 32436-32448.
[8] 崔佳旭, 杨博. 贝叶斯优化方法和应用综述[J]. 软件学报, 2018, 29(10): 3068-3090.
CUI J X, YANG B.Survey on Bayesian optimization methodology and applications[J]. Journal of software, 2018, 29(10): 3068-3090.
[9] 张雪松, 李鹏, 周亦尧, 等. 基于贝叶斯概率的光伏出力组合预测方法[J]. 太阳能学报, 2021, 42(10): 80-86.
ZHANG X S, LI P, ZHOU Y Y, et al.Photovoltaic output combination forecasting method based on Bayesian probability[J]. Acta energiae solaris sinica, 2021, 42(10): 80-86.
[10] 李斌, 徐文韬, 杨建蒙. 带储热装置的太阳能辅助燃煤发电系统研究[J]. 太阳能学报, 2021, 42(8): 223-230.
LI B, XU W T, YANG J M.Research on solar energy-assisted coal-fired power generation system with heat storagy device[J]. Acta energiae solaris sinica, 2021, 42(8): 223-230.
[11] 王惠杰, 董学会, 昝永超, 等. 熔盐储热型塔式太阳能与燃煤机组耦合方式及热力性能分析[J]. 热力发电, 2019, 48(7): 47-52.
WANG H J, DONG X H, ZAN Y C, et al.Coupling method and thermal performance analysis for molten salt heat storage tower solar energy power station and thermal power unit[J]. Thermal power generation, 2019, 48(7): 47-52.
[12] JIANG Y, DUAN L Q, PANG L P, et al.Thermal performance study of tower solar aided double reheat coal-fired power generation system[J]. Energy, 2021, 230: 120857.
[13] LI C, ZHAI R R, ZHANG B, et al.Thermodynamic performance of a novel solar tower aided coal-fired power system[J]. Applied thermal engineering, 2020, 171: 115127.
[14] 徐文韬. 带储热装置的太阳能辅助燃煤发电系统研究[D]. 北京: 华北电力大学, 2020.
XU W T.Research on solar-assisted coal-fired power generation system with heat storage device[D]. Beijing: North China Electric Power University, 2020.
[15] 李斌, 王雨萌, 张庆来, 等. “光火储” 一体化发电系统研究[J]. 热力发电, 2022, 51(2): 56-64.
LI B, WANG Y M, ZHANG Q L, et al.Research on integrated power generation system of “solar, coal-fired power and energy storage”[J]. Thermal power generation, 2022, 51(2): 56-64.
[16] 李斌, 陈吉玲, 李晨昕, 等. 压缩空气储能系统与火电机组的耦合方案研究[J]. 动力工程学报, 2021, 41(3): 244-250.
LI B, CHEN J L, LI C X, et al.Research on coupling schemes of a compressed air energy storage system and thermal power unit[J]. Journal of Chinese Society of Power Engineering, 2021, 41(3): 244-250.
[17] 刘奕辰, 范成, 刘旭媛, 等. 基于循环神经网络的冷水机组故障诊断方法[J]. 建筑科学, 2022, 38(8): 160-171.
LIU Y C, FAN C, LIU X Y, et al.Deep recurrent neural network-based strategy for chiller fault detection and diagnosis[J]. Building science, 2022, 38(8): 160-171.
[18] ABDULRAHMAN M L, IBRAHIM K M, GITAL A Y, et al.A review on deep learning with focus on deep recurrent neural network for electricity forecasting in residential building[J]. Procedia computer science, 2021, 193: 141-154.
[19] 苏连成, 朱娇娇, 李英伟. 基于时间卷积网络残差校正的短期风电功率预测[J]. 太阳能学报, 2023, 44(7): 427-435.
SU L C, ZHU J J, LI Y W.Short-term wind power prediction based on temporal convolutional network residual correction model[J]. Acta energiae solaris sinica, 2023, 44(7): 427-435.
[20] 杨国清, 刘世林, 王德意, 等. 基于Attention-GRU风速修正和Stacking的短期风电功率预测[J]. 太阳能学报, 2022, 43(12): 273-281.
YANG G Q, LIU S L, WANG D Y, et al.Short-term wind power forecasting based on Attention-GRU wind speed correction and stacking[J]. Acta energiae solaris sinica, 2022, 43(12): 273-281.
[21] FENG J, SHEN W Z.Solving the wind farm layout optimization problem using random search algorithm[J]. Renewable energy, 2015, 78: 182-192.
[22] CHEN Y, CHEN H R, ZENG H, et al.Structural optimization design of sinusoidal wavy plate fin heat sink with crosscut by Bayesian optimization[J]. Applied thermal engineering, 2022, 213: 118755.
[23] KHAIR U, FAHMI H, AL HAKIM S, et al.Forecasting error calculation with mean absolute deviation and mean absolute percentage error[J]. Journal of physics: conference series, 2017, 930: 012002.
PDF(2068 KB)

Accesses

Citation

Detail

Sections
Recommended

/