OPTIMAL ALLOCATION METHOD OF ENERGY STORAGE CAPACITY BASED ON TLBO ALGORITHM

Sun Huiying, Li Yueqiao, Liu Zifa

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 333-341.

PDF(1625 KB)
Welcome to visit Acta Energiae Solaris Sinica, Today is
PDF(1625 KB)
Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (9) : 333-341. DOI: 10.19912/j.0254-0096.tynxb.2024-0863

OPTIMAL ALLOCATION METHOD OF ENERGY STORAGE CAPACITY BASED ON TLBO ALGORITHM

  • Sun Huiying, Li Yueqiao, Liu Zifa
Author information +
History +

Abstract

In order to improve the output stability of photovoltaic power plants and ensure power quality, it is necessary to optimise the allocation of energy storage capacity in photovoltaic power plants. This paper proposes an optimal allocation method of energy storage capacity based on Teaching-Learning-Based Optimization (TLBO) algorithm. Considering the impact of multiple factors on PV output, a two-layer energy storage capacity optimisation model is constructed. The upper layer is solved by the TLBO algorithm with the objective function of minimising the whole life cycle cost of energy storage; the lower layer is solved by the Gurobi solver with the objective function of maximising the operating revenue, and the optimal daily operation strategy is solved by the Gurobi solver. Finally, a real PV power plant in Daqing is taken as an example for simulation, and the simulation results show the effectiveness of the method.

Key words

photovoltaic power / energy storage / optimization / teaching-learning-based optimization (TLBO) algorithm

Cite this article

Download Citations
Sun Huiying, Li Yueqiao, Liu Zifa. OPTIMAL ALLOCATION METHOD OF ENERGY STORAGE CAPACITY BASED ON TLBO ALGORITHM[J]. Acta Energiae Solaris Sinica. 2025, 46(9): 333-341 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0863

References

[1] 李洪言, 于淼, 郑一鸣, 等. 基于情景的2050年世界能源供需展望分析: 基于《bp世界能源展望(2023年版)》[J]. 天然气与石油, 2023, 41(5): 131-137.
LI H Y, YU M, ZHENG Y M, et al.Scenario based analysis on the outlook for global energy supply and demand in 2050: based on bp Energy Outlook (2023 edition)[J]. Natural gas and oil, 2023, 41(5): 131-137.
[2] 于东霞, 张建华, 王晓燕, 等. 并网型风光储互补发电系统容量优化配置[J]. 电力系统及其自动化学报, 2019, 31(10): 59-65.
YU D X, ZHANG J H, WANG X Y, et al.Optimal capacity configuration of grid-connected wind-PV-storage hybrid power generation system[J]. Proceedings of the CSU-EPSA, 2019, 31(10): 59-65.
[3] 赵宇洋, 赵钰欢, 郭英军, 等. 离网型风光氢储系统容量配置与控制优化[J]. 太阳能学报, 2024, 45(7): 50-59.
ZHAO Y Y, ZHAO Y H, GUO Y J, et al.Capacity configuration and control optimization of off-grid wind solar hydrogen storage system[J]. Acta energiae solaris sinica, 2024, 45(7): 50-59.
[4] ÁLVARO D, ARRANZ R, AGUADO J A.Sizing and operation of hybrid energy storage systems to perform ramp-rate control in PV power plants[J]. International journal of electrical power & energy systems, 2019, 107: 589-596.
[5] 赵峰, 张帆, 陈小强, 等. 基于VMD-APSO的风电场混合储能系统容量优化配置[J]. 高压电器, 2023, 59(6): 120-127.
ZHAO F, ZHANG F, CHEN X Q, et al.Optimal configuration of capacity of wind farm hybrid energy storage system based on VMD-APSO algorithm[J]. High voltage apparatus, 2023, 59(6): 120-127.
[6] 马丙泰, 刘海涛, 郝思鹏, 等. 基于价格需求响应的储能系统退化成本模型研究[J]. 太阳能学报, 2023, 44(10): 531-540.
MA B T, LIU H T, HAO S P, et al.Research on degradation cost model of energy storage system based on price demand response[J]. Acta energiae solaris sinica, 2023, 44(10): 531-540.
[7] 刘尚奇, 胡健, 张晓杰, 等. 含光伏和储氢的电-氢集成化能源站容量配置[J]. 太阳能学报, 2023, 44(8): 171-179.
LIU S Q, HU J, ZHANG X J, et al.Capacity configuration of integrated electricity charging and hydrogen refueling station containing photovoltaic and hydrogen storage[J]. Acta energiae solaris sinica, 2023, 44(8): 171-179.
[8] 刘子祺, 苏婷婷, 何佳阳, 等. 基于多目标粒子群算法的配电网储能优化配置研究[J]. 综合智慧能源, 2023, 45(6): 9-16.
LIU Z Q, SU T T, HE J Y, et al.Research on the optimal allocation of energy storage in distribution network based on multi-objective particle swarm optimization algorithm[J]. Integrated intelligent energy, 2023, 45(6): 9-16.
[9] 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.
[10] 温春雪, 赵天赐, 于赓, 等. 基于改进粒子群算法的储能优化配置[J]. 电气技术, 2022, 23(10): 1-9.
WEN C X, ZHAO T C, YU G, et al.Optimization configuration of energy storage based on the improved particle swarm optimization[J]. Electrical engineering, 2022, 23(10): 1-9.
[11] 张楠. 基于粒子群算法的风光一体化电站储能优化配置方法[J]. 电工技术, 2022(24): 83-85.
ZHANG N.Optimal allocation method of energy storage in wind-solar integrated power station based on particle swarm algorithm[J]. Electric engineering, 2022(24): 83-85.
[12] 刘红. 基于改进粒子群算法的储能调峰容量优化配置研究[J]. 广东电力, 2023, 36(1): 68-76.
LIU H.Research on optimal configuration of energy storage peak shaving capacity based on improved particle swarm optimization algorithm[J]. Guangdong electric power, 2023, 36(1): 68-76.
[13] 冯侃, 边辉, 陈丽娜, 等. 基于遗传算法的分布式光伏配网储能优化配置研究[J]. 自动化仪表, 2024, 45(1): 64-68.
FENG K, BIAN H, CHEN L N, et al.Study on optimized allocation of distributed photovoltaic distribution network energy storage based on genetic algorithm[J]. Process automation instrumentation, 2024, 45(1): 64-68.
[14] 肖小龙, 史明明, 周琦, 等. 基于改进海洋捕食者算法的配电网储能多目标优化配置[J]. 储能科学与技术, 2023, 12(8): 2565-2574.
XIAO X L, SHI M M, ZHOU Q, et al.Multiobjective optimization configuration of energy storage in distribution networks based on improved marine predator algorithm[J]. Energy storage science and technology, 2023, 12(8): 2565-2574.
[15] 吴成明, 扬臻辉. 基于改进鲸鱼算法的混合储能系统容量优化配置[J]. 电工材料, 2024(1): 84-89.
WU C M, YANG Z H.Capacity optimization configuration of hybrid energy storage system based on improved whale algorithm[J]. Electrical engineering materials, 2024(1): 84-89.
[16] 涂强, 范宏, 王宏祥. 多主体分布式综合能源系统两级协调优化调度方法[J]. 水利水电技术(中英文), 2023, 54(11): 29-39.
TU Q, FAN H, WANG H X.Bi-level coordination and optimal scheduling method for multi-body distributed integrated energy systems[J]. Water resources and hydropower engineering, 2023, 54(11): 29-39.
[17] 刘昳娟, 陈云龙, 刘继彦, 等. 基于集成学习的分布式光伏发电功率日前预测[J]. 中国电力, 2022, 55(9): 38-45.
LIU Y J, CHEN Y L, LIU J Y, et al.Ensemble learning-based day-ahead power forecasting of distributed photovoltaic generation[J]. Electric power, 2022, 55(9): 38-45.
[18] 赵靖英, 乔珩埔, 姚帅亮, 等. 考虑储能SOC自恢复的风电波动平抑混合储能容量配置策略[J]. 电工技术学报, 2024, 39(16): 5206-5219.
ZHAO J Y, QIAO H P, YAO S L, et al.Hybrid energy storage system capacity configuration strategy for stabilizing wind power fluctuation considering SOC self-recovery[J]. Transactions of China Electrotechnical Society, 2024, 39(16): 5206-5219.
[19] ABAEIFAR A, BARATI H, TAVAKOLI A R.Inertia-weight local-search-based TLBO algorithm for energy management in isolated micro-grids with renewable resources[J]. International journal of electrical power & energy systems, 2022, 137: 107877.
[20] 于运永, 金钧. 基于改进粒子群算法混合储能优化调度[J]. 电气应用, 2024, 43(2): 1-6.
YU Y Y, JIN J.Hybrid energy storage optimization scheduling based on improved particle swarm algorithm[J]. Electrotechnical application, 2024, 43(2): 1-6.
PDF(1625 KB)

Accesses

Citation

Detail

Sections
Recommended

/