FISHING RAFT MULTI-MICROGRID SYSTEM CONSIDERING ENERGY STORAGE LIFE DEGRADATION MULTI-SCENARIO TWO-LAYEROPTIMIZATION STRATEGY

Huang Jing; Luo Hao; Guo Haowen; Huang He

doi:10.19912/j.0254-0096.tynxb.2024-1531

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 765-773. DOI: 10.19912/j.0254-0096.tynxb.2024-1531

FISHING RAFT MULTI-MICROGRID SYSTEM CONSIDERING ENERGY STORAGE LIFE DEGRADATION MULTI-SCENARIO TWO-LAYEROPTIMIZATION STRATEGY

Huang Jing, Luo Hao, Guo Haowen, Huang He

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Abstract

This paper proposes a two-layer multi-scenario collaborative optimization configuration model for off-grid multi-microgrid systems that considers the life degradation of energy storage equipment. Among them, the upper model configures the capacity of microgrid equipment with the optimization goal of minimizing the annualized total cost of the multi-microgrid system; the lower model optimizes the output of equipment with the goal of minimizing the daily operating cost of the multi-microgrid system. The model considers the influence of the state of charge and depth of discharge depth on the life of energy storage, and establishes a calculation model for the life degradation of energy storage. By applying the established model, case analysis and calculation of independent optimization and coordinated optimization of multi-microgrid systems were carried out, verifying that the coordinated planning of the multi-microgrid system can effectively reduce the annualized total cost of the system.

Key words

microgrid / optimization / scheduling / energy storage life / capacity configuration / distributed generations

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Huang Jing, Luo Hao, Guo Haowen, Huang He. FISHING RAFT MULTI-MICROGRID SYSTEM CONSIDERING ENERGY STORAGE LIFE DEGRADATION MULTI-SCENARIO TWO-LAYEROPTIMIZATION STRATEGY[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 765-773 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1531

References

[1] 黄加勉, 蔡学志. 双碳背景下新能源在新型电力系统中的实践运用[J]. 电工技术, 2023(S1): 111-113.HUANG J M,CAI X Z. Practical application of new energy in new power system under the dual carbon background[J]. Electric engineering,2023(S1): 111-113.
[2] HUANG J, GUO H W, LUO H.Economic optimization scheduling strategy for offshore fishing raft microgrid clusters[J]. IEEE access, 2024, 12: 76986-76994.
[3] 邓辉平, 江冰, 蔡昌春. 海岛智能微电网多源协调优化运行研究[J]. 信息技术, 2017(4): 104-109.DENG H P,JIANG B,CAI C C. Optimal and coordinated operation of multi-sources of smart island micro-grid[J]. Information technology, 2017(4): 104-109.
[4] 黄冬梅, 吕嘉欣, 时帅, 等. 计及需求响应的海岛微电网群优化运行研究[J]. 电力系统保护与控制, 2024, 52(9): 88-98.HUANG D M, LYU J X,SHI S, et al. Optimal operation of island microgrid clusters considering demand response [J]. Power system protection and control, 2024, 52(9): 88-98.
[5] 马宽, 何国锋, 董燕飞, 等. 基于多策略改进PSO算法的微电网优化调度研究[J]. 智慧电力, 2023, 51(8): 23-29, 45.MA K, HE G, DONG Y F, et al. Microgrid optimal scheduling based on multi-strategy improved PSO algorithm[J]. Smart power, 2023, 51(8): 23-29, 45.
[6] 杨欢红, 王洁, 邰能灵, 等. 基于灰靶决策和多目标布谷鸟算法的微电网分布式电源鲁棒优化[J]. 电力系统保护与控制, 2019, 47(1): 20-27. YANG H H , WANG J, TAI N L, et al. Robust optimization of distributed generation in a microgrid based on grey target decision-making and multi-objective cuckoo search algorithm[J]. Power system protection and control, 2019, 47(1): 20-27.
[7] LEI W C, SUN W Y, ZHAO Y, et al.Research on market trading strategy of multi-microgrid intelligent power distribution system based on bi-level optimization[J]. Frontiers in energy research, 2023, 10: 1032051.
[8] 毛亚哲, 何柏娜, 王德顺, 等. 基于改进深度强化学习的智能微电网群控制优化方法[J]. 智慧电力, 2021, 49(3): 19-25, 58.MAO Y Z, HE B N ,WANG D S, et al. Optimization method for smart multi-microgrid control based on improved deep reinforcement learning[J]. Smart power, 2021, 49(3): 19-25, 58.
[9] 周辉, 张玉, 肖烈禧, 等. 基于改进秃鹰算法的微电网群经济优化调度研究[J]. 太阳能学报, 2024, 45(2): 328-335. ZHOU H, ZHANG Y, XIAO L X, et al. Research on economicoptimal dispatching of Microgrid cluster based on improved bald eagle search algorithm[J]. Acta energiae solaris sinica, 2024, 45(2): 328-335.
[10] DEWAN A, TOMAR S S, BISHNOI A K, et al.Computational fluid dynamics and turbulence modelling in various blades of Savonius turbines for wind and hydro energy: progress and perspectives[J]. Ocean engineering, 2023, 283: 115168.
[11] 王驰鑫, 赵玉, 齐琦, 等. 含多微网的经济优化调度[J]. 电子设计工程, 2021, 29(22): 108-113.WANG C X, ZHAO Y, QI Q, et al. Economic optimization of scheduling with multi-microgrid[J]. Electronic design engineering, 2021, 29(22): 108-113.
[12] MOTAPON S N, LACHANCE E, DESSAINT L A, et al.A generic cycle life model for lithium-ion batteries based on fatigue theory and equivalent cycle counting[J]. IEEE open journal of the Industrial Electronics Society, 2020, 1: 207-217.
[13] 谢姿, 张惠娟, 刘琪, 等. 考虑蓄电池寿命的分布式电源容量优化配置[J]. 太阳能学报, 2021, 42(10): 424-430.XIE Z, ZAHNG H J, LIU Q, et al. Optimal configeration of distributed power supply capacity considering battery life[J]. Acta engergiae solaris sinica, 2021, 42(10): 424-430.
[14] 赵诣, 方陈, 冯冬涵, 等. 考虑电池储能寿命损耗的风光储微网随机日前调度策略[J]. 电气工程学报, 2022, 17(3): 210-218.ZHAO Y, FANG C, FENG D H, et al. Stochastic day-ahead scheduling optimization for wind-PV-ES microgrid considering battery life loss[J]. Journal of electrical engineering, 2022, 17(3): 210-218.
[15] 林顺富, 刘持涛, 李东东, 等. 考虑电能交互的冷热电区域多微网系统双层多场景协同优化配置[J]. 中国电机工程学报, 2020, 40(5): 1409-1421.LIN S F, LIU C T, LI D D, et al. Bi-level multiple scenarios collaborative optimization configuration of CCHP regional multi-microgrid system considering power interaction among microgrids[J]. Proceedings of the CSEE, 2020, 40(5): 1409-1421.
[16] 胡骅, 徐冲, 吴汕, 等. 影响用户侧分布式发电经济性因素分析[J]. 电力自动化设备, 2008(5): 29-33.HU J, XV C, WU S, et al. Analysis of factor s influencing economy of customer-side distr ibuted gener ation[J]. Electric power automation equipment , 2008(5): 29-33.
[17] 马溪原, 郭晓斌, 雷金勇. 面向多能互补的分布式光伏与气电混合容量规划方法[J]. 电力系统自动化, 2018, 42(4): 55-63.MA X Y ,GUO X B, LEI J Y. Capacity planning method of distributed PV and P2Gin multi-energy coupled system [J]. Automation of electric power systems, 2018, 42(4): 55-63.
[18] LIANG J J, QIN A K, SUGANTHAN P N, et al.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions[J]. IEEE transactions on evolutionary computation, 2006, 10(3): 281-295.