为降低微电网的综合发电成本,该文进行基于改进麻雀搜索算法（ISSA）的微电网容量优化配置研究。以微电网等年值综合成本最低为目标函数,综合考虑系统价格型需求响应、储能系统平准化度电成本和分时电价,构建微电网容量优化配置模型。针对麻雀搜索算法出现的收敛精度不足及易陷入局部最优的问题,提出改进麻雀搜索算法,首先采用混沌反向学习策略使初始种群分布均匀;其次加入黄金正弦公式提高算法收敛精度;再引入动态权重因子改善算法动态性能,提高全局搜索能力;最后利用混合变异扰动策略、贪心策略跳出局部最优。将ISSA与麻雀搜索算法、灰狼算法以及鲸鱼算法进行对比测试,验证了该文所提算法具有更高的收敛精度以及更快的收敛速度,并选取某地区实际数据进行算例仿真,所求取的综合发电成本相比其他3种算法分别降低了6.39%、7.35%、11.68%,验证了ISSA的有效性和优越性。
Abstract
To minimize the comprehensive power generation cost of microgrids, this article undertakes a study focusing on the optimization configuration of microgrid capacity, employing the improved sparrow search algorithm （ISSA）. A capacity optimization model is formulated, with the annual comprehensive cost of microgrids serving as the objective function. This model incorporates various factors, such as price-based demand response, the levelized cost of energy storage systems, and time-of-use electricity pricing. Addressing the challenges of inadequate convergence accuracy and susceptibility to local optima in the traditional sparrow search algorithm, this study introduces an enhanced version of the algorithm. Initially, a chaotic reverse learning strategy is adopted to ensure a uniform distribution of the initial population. Subsequently, the golden sine formula is integrated to enhance the algorithm's convergence accuracy. Additionally, dynamic weight factors are introduced to improve the algorithm's dynamic performance and bolster its global search capabilities. Lastly, a mixed mutation perturbation strategy and a greedy strategy are employed to facilitate escaping from local optima. To validate the proposed ISSA, a comparative test is conducted against the sparrow search algorithm, grey wolf algorithm, and whale algorithm. The results demonstrate that ISSA exhibits superior convergence accuracy and faster convergence speed. Utilizing actual data from a specific region for numerical simulation, the calculated comprehensive power generation cost is reduced by 6.39%, 7.35%, and 11.68% when compared to the other three algorithms, respectively. This underscores the effectiveness and superiority of the ISSA in optimizing microgrid capacity configuration.
关键词
微电网 /
混合储能 /
改进麻雀搜索算法 /
容量优化
Key words
microgrid /
hybrid energy storage /
improved sparrow search algorithm /
capacity optimization
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 崔杨, 于世鹏, 王学斌, 等. 考虑系统调峰需求与光热电站收益平衡的储热容量优化配置[J]. 中国电机工程学报, 2023, 43(22): 8745-8757.
CUI Y, YU S P, WANG X B, et al.Optimal configuration of heat storage capacity considering the balance between system peak shaving demand and concentrating solar power plant revenue[J]. Proceedings of the CSEE, 2023, 43(22): 8745-8757.
[2] 赖钧杰, 文小玲, 张淇, 等. 基于改进麻雀搜索算法的直流微电网容量优化配置[J]. 太阳能学报, 2023, 44(8): 157-163.
LAI J J, WEN X L, ZHANG Q, et al.Capacity optimization configuration of DC microgrid based on improved sparrow search algorithm[J]. Acta energiae solaris sinica, 2023, 44(8): 157-163.
[3] 谢姿, 张惠娟, 刘琪, 等. 基于改进鲸鱼算法的并网型微网能量管理[J]. 科学技术与工程, 2020, 20(15): 6087-6093.
XIE Z, ZHANG H J, LIU Q, et al.Grid-connected micro-grid energy management in improved whale algorithm[J]. Science technology and engineering, 2020, 20(15): 6087-6093.
[4] 李聪, 秦立军. 基于改进粒子群算法的混合储能独立调频的容量优化研究[J]. 太阳能学报, 2023, 44(1): 426-434.
LI C, QIN L J.Sizing optimization for hybrid energy storage system independently participating in regulation market using improved particle swarm optimization[J]. Acta energiae solaris sinica, 2023, 44(1): 426-434.
[5] 李圣清, 邓娜, 颜石, 等. 基于改进蚁群动态规划的光储微网容量优化配置[J]. 太阳能学报, 2023, 44(2): 468-476.
LI S Q, DENG N, YAN S, et al.Optimal configuration optimization of PV energy storage microgrid using improved ant colony dynamic programming[J]. Acta energiae solaris sinica, 2023, 44(2): 468-476.
[6] 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.
[7] 向开端, 王辉, 彭婷婷, 等. 含混合储能的风光储系统容量优化配置[J]. 科学技术与工程, 2023, 23(31): 13415-13422.
XIANG K D, WANG H, PENG T T, et al.Optimal capacity allocation of wind-solar-storage system with hybrid energy storage[J]. Science technology and engineering, 2023, 23(31): 13415-13422.
[8] 胡臻达, 姜文瑾, 张林垚, 等. 基于改进猫群算法的氢储能容量优化配置[J]. 中国电力, 2023, 56(10): 33-42.
HU Z D, JIANG W J, ZHANG L Y, et al.Optimal allocation of hydrogen storage capacity based on improved cat swarm optimization[J]. Electric power, 2023, 56(10): 33-42.
[9] 朱莉, 汪小豪, 李豪, 等. 不平衡样本下基于变异麻雀搜索算法和改进SMOTE的变压器故障诊断方法[J]. 高电压技术, 2023, 49(12): 4993-5001.
ZHU L,WANG X H,LI H, et al.Transformer fault diagnosis method based on variation sparrow search algorithm and improved SMOTE under unbalanced samples[J]. High voltage engineering, 2023, 49(12): 4993-5001.
[10] LIU Y W, SUN J, SHANG Y L, et al.A novel remaining useful life prediction method for lithium-ion battery based on long short-term memory network optimized by improved sparrow search algorithm[J]. Journal of energy storage, 2023, 61: 106645.
[11] LI Z, GUO J F, GAO X Y, et al.A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: optimal loading distribution of chillers[J]. Applied energy, 2023, 349: 121623.
[12] 宋冬然, 晏嘉琪, 夏鄂, 等. 基于改进麻雀搜索算法的大型海上风电场电缆布置优化[J]. 电力系统保护与控制, 2022, 50(12): 134-143.
SONG D R, YAN J Q, XIA E, et al.Optimization of cable layout for large-scale offshore wind farms based on an improved sparrow search algorithm[J]. Power system protection and control, 2022, 50(12): 134-143.
[13] 杨力帆, 周鲲, 齐增清, 等. 基于需求响应的虚拟电厂多时间尺度优化调度[J]. 电网与清洁能源, 2024, 40(3): 10-21.
YANG L F, ZHOU K, QI Z Q, et al.A multi-time scale optimal scheduling strategy of virtual power plants based on demand response[J]. Advances of power system & hydroelectric engineering, 2024, 40(3): 10-21.
[14] SINGH S, CHAUHAN P, SINGH N.Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm[J]. International journal of hydrogen energy, 2020, 45(16): 10070-10088.
[15] 曹雅琦, 赵波, 王丽婕, 等. 基于遗传蚁群的光储电站运行效益提升策略研究[J]. 中国电力, 2022, 55(2): 9-18.
CAO Y Q, ZHAO B, WANG L J, et al.Research on operational benefit improvement strategy of optical storage power station based on genetic ant colony algorithm[J]. Electric power, 2022, 55(2): 9-18.
[16] GUO S, HE Y, PEI H J, et al.The multi-objective capacity optimization of wind-photovoltaic-thermal energy storage hybrid power system with electric heater[J]. Solar energy, 2020, 195: 138-149.
PDF(1498 KB)
