基于IMOSSA算法的并网型风光蓄氢系统容量优化配置

李婵娟, 孟高军, 丁妍文

太阳能学报 ›› 2025, Vol. 46 ›› Issue (11) : 79-86.

PDF(2396 KB)
欢迎访问《太阳能学报》官方网站,今天是
PDF(2396 KB)
太阳能学报 ›› 2025, Vol. 46 ›› Issue (11) : 79-86. DOI: 10.19912/j.0254-0096.tynxb.2024-1189

基于IMOSSA算法的并网型风光蓄氢系统容量优化配置

  • 李婵娟1, 孟高军2, 丁妍文2
作者信息 +

CAPACITY OPTIMIZATION CONFIGURATION OF GRID CONNECTED WIND SOLAR HYDROGEN STORAGE SYSTEM BASED ON IMOSSA ALGORITHM

  • Li Chanjuan1, Meng Gaojun2, Ding Yanwen2
Author information +
文章历史 +

摘要

在保障风光氢储综合供电系统可靠性的前提下,为最大限度地降低单位发电成本,提升系统整体的经济性,该研究以离网型系统为基础,在目标函数中引入联络线功率相对标准偏差,以衡量系统稳定性,在经济性目标函数中引入阶梯型碳排放成本,并在约束条件中增加与电网交互功率约束、系统自平衡率约束,同时,提出一种计及电网分时电价的运行控制策略来改善系统的运行状况,采用IMOSSA算法形成最优配置方案。研究结果表明:此次优化可使系统自平衡率在10%~20%的范围内提升,有效降低系统年总净现成本约15%,充分证明所提研究方法的有效性。

Abstract

On the premise of ensuring the reliability of the wind solar hydrogen storage integrated power supply system, in order to minimize the unit power generation cost and improve the overall economic performance of the system, this paper adds the relative standard deviation of interconnection line power in the objective function to measure the stability of the system on the basis of an off-grid system, and introduces a stepped carbon emission cost in the economic objective function. The constraint conditions include the interaction power constraint with the grid and the system self-balancing rate constraint. At the same time, an operation control strategy that takes into account the time-of-use electricity pricing of the grid is proposed to improve the operation of the system. The IMOSSA algorithm is used to obtain the optimal configuration scheme. The results of the study show that this optimization can increase the self-balancing rate of the system in the range of 10% to 20% and effectively reduce the total annual net present cost of the system by about 15%, which demonstrates the effectiveness of the proposed research methodology.

关键词

风力发电 / 光伏发电 / 氢储能 / 容量配置 / IMOSSA算法

Key words

wind power generation / photovoltaic power generation / hydrogen energy storage / capacity optimization configuration / IMOSSA algorithm

引用本文

导出引用
李婵娟, 孟高军, 丁妍文. 基于IMOSSA算法的并网型风光蓄氢系统容量优化配置[J]. 太阳能学报. 2025, 46(11): 79-86 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1189
Li Chanjuan, Meng Gaojun, Ding Yanwen. CAPACITY OPTIMIZATION CONFIGURATION OF GRID CONNECTED WIND SOLAR HYDROGEN STORAGE SYSTEM BASED ON IMOSSA ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 79-86 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1189
中图分类号: TM732   

参考文献

[1] 申建建, 王月, 程春田, 等. 水风光多能互补发电调度问题研究现状及展望[J]. 中国电机工程学报, 2022, 42(11): 3871-3885.
SHEN J J, WANG Y, CHENG C T, et al.Research status and prospect of generation scheduling for hydropower-wind-solar energy complementary system[J]. Proceedings of the CSEE, 2022, 42(11): 3871-3885.
[2] 王磊, 王昭, 冯斌, 等. 基于双层优化模型的风-光-储互补发电系统优化配置[J]. 太阳能学报, 2022, 43(5): 98-104.
WANG L, WANG Z, FENG B, et al.Optimal configuration of wind-photovoltaic-ESS complementary power generation system based on bi-level optimization model[J]. Acta energiae solaris sinica, 2022, 43(5): 98-104.
[3] 辛保安, 单葆国, 李琼慧, 等. “双碳” 目标下“能源三要素” 再思考[J]. 中国电机工程学报, 2022, 42(9): 3117-3126.
XIN B A, SHAN B G, LI Q H, et al.Rethinking of the “three elements of energy” toward carbon peak and carbon neutrality[J]. Proceedings of the CSEE, 2022, 42(9): 3117-3126.
[4] 徐万万, 王斌, 刘江, 等. 电-氢微网群功率协调与谐波补偿控制策略[J]. 太阳能学报, 2024, 45(6): 201-207.
XU W W, WANG B, LIU J, et al.Power coordinated control and harmonic compensation of electricity-hydrogen energy microgrid clusters[J]. Acta energiae solaris sinica, 2024, 45(6): 201-207.
[5] 张琦, 谢丽蓉, 王威, 等. 计及补偿风电预测误差和平抑波动的混合储能分区优化控制策略[J]. 太阳能学报, 2023, 44(7): 7-13.
ZHANG Q, XIE L R, WANG W, et al.Optimal control strategy for hybrid energy storage zoning considering compensating wind power forecasting error and smoothing fluctuation[J]. Acta energiae solaris sinica, 2023, 44(7): 7-13.
[6] 吴小刚, 刘宗歧, 田立亭, 等. 独立光伏系统光储容量优化配置方法[J]. 电网技术, 2014, 38(5): 1271-1276.
WU X G, LIU Z Q, TIAN L T, et al.Optimized capacity configuration of photovoltaic generation and energy storage device for stand-alone photovoltaic generation system[J]. Power system technology, 2014, 38(5): 1271-1276.
[7] 孟贤, 郭启蒙, 李颖欢, 等. 考虑双向需求的混合储能容量多目标优化配置[J]. 太阳能学报, 2023, 44(8): 45-53.
MENG X, GUO Q M, LI Y H, et al.Multi-objective optimal configuration of hybrid energy storage capacity considering two-way demand[J]. Acta energiae solaris sinica, 2023, 44(8): 45-53.
[8] 刘峪涵, 汪沨, 谭阳红. 并网型微电网多目标容量优化配置及减排效益分析[J]. 电力系统及其自动化学报, 2017, 29(9): 70-75.
LIU Y H, WANG F, TAN Y H.Multi-objective optimal capacity configuration and emission reduction benefit analysis of grid-connected microgrid[J]. Proceedings of the CSU-EPSA, 2017, 29(9): 70-75.
[9] GHIASI M.Detailed study, multi-objective optimization, and design of an AC-DC smart microgrid with hybrid renewable energy resources[J]. Energy, 2019, 169: 496-507.
[10] 薛静杰, 宋新甫, 周红莲, 等. 计及上网电价经济调度的风光并网系统储能容量的研究[J]. 电器与能效管理技术, 2018(1): 60-65.
XUE J J, SONG X F, ZHOU H L, et al.Study on energy storage capacity of grid-connected wind & PV generation system with power tariff[J]. Electrical & energy management technology, 2018(1): 60-65.
[11] KIM S D, JO K Y, GIM J.A study on the thermal and electrical optimization algorithms of campus microgrid[J]. Journal of the Korean institute of illuminating and electrical installation engineers, 2019, 33(2): 45-52.
[12] NGUYEN T A, CROW M L.Stochastic optimization of renewable-based microgrid operation incorporating battery operating cost[J]. IEEE transactions on power systems, 2016, 31(3): 2289-2296.
[13] 刘忠, 陈星宇, 邹淑云, 等. 计及碳排放的风-光-抽水蓄能系统容量优化配置方法[J]. 电力系统自动化, 2021, 45(22): 9-18.
LIU Z, CHEN X Y, ZOU S Y, et al.Optimal capacity configuration method for wind-photovoltaic-pumped-storage system considering carbon emission[J]. Automation of electric power systems, 2021, 45(22): 9-18.
[14] 赵超, 王斌, 孙志新, 等. 基于改进灰狼算法的独立微电网容量优化配置[J]. 太阳能学报, 2022, 43(1): 256-262.
ZHAO C, WANG B, SUN Z X, et al.Optimal configuration optimization of islanded microgrid using improved grey wolf optimizer algorithm[J]. Acta energiae solaris sinica, 2022, 43(1): 256-262.
[15] 李蕊睿, 李奇, 蒲雨辰, 等. 计及功率交互约束的含电-氢混合储能的多微电网系统容量优化配置[J]. 电力系统保护与控制, 2022, 50(14): 53-64.
LI R R, LI Q, PU Y C, et al.Optimal configuration of an electric-hydrogen hybrid energy storage multi-microgrid system considering power interaction constraints[J]. Power system protection and control, 2022, 50(14): 53-64.
[16] 张盛, 郑津洋, 戴剑锋, 等. 可再生能源大规模制氢及储氢系统研究进展[J]. 太阳能学报, 2024, 45(1): 457-465.
ZHANG S, ZHENG J Y, DAI J F, et al.Research progress on renewable energy system coupled with large-scale hydrogen production and storage[J]. Acta energiae solaris sinica, 2024, 45(1): 457-465.
[17] 闫群民, 董新洲, 穆佳豪, 等. 基于改进多目标粒子群算法的有源配电网储能优化配置[J]. 电力系统保护与控制, 2022, 50(10): 11-19.
YAN Q M, DONG X Z, MU J H, et al.Optimal configuration of energy storage in an active distribution network based on improved multi-objective particle swarm optimization[J]. Power system protection and control, 2022, 50(10): 11-19.

基金

国家社会科学基金一般项目(21BJL082); 江苏省重点研发计划(BE2021094)

PDF(2396 KB)

Accesses

Citation

Detail

段落导航
相关文章

/