考虑光伏出力的储能系统容量优化配置研究

程跃珂; 王钰洁

doi:10.19912/j.0254-0096.tynxb.2024-1169

PDF(1958 KB)

太阳能学报 ›› 2025, Vol. 46 ›› Issue (11) : 61-68. DOI: 10.19912/j.0254-0096.tynxb.2024-1169

考虑光伏出力的储能系统容量优化配置研究

程跃珂, 王钰洁

作者信息 +

RESEARCH ON CAPACITY OPTIMIZATION OF ENERGY STORAGE SYSTEM CONSIDERING PHOTOVOLTAIC OUTPUT

Cheng Yueke, Wang Yujie

Author information +

文章历史 +

摘要

为剔除光伏出力波动对储能配置的干扰,提出一种基于光伏出力预测模型和改进遗传算法确定最优储能容量的方法。基于长短期记忆神经网络（LSTM）预测光伏出力,利用变分模态分析（VMD）捕捉光伏波动特性,结合改进麻雀算法（ISSA）优化LSTM神经网络超参数建立VMD-ISSA-LSTM光伏出力预测模型。通过分割种群和改进突变算子改进遗传算法（GA）实现储能容量优化配置。由实际算例验证得到VMD-ISSA-LSTM预测光伏出力平均绝对误差、均方根误差、平均绝对百分比误差可达12.48 kW、14.44 kW、2.16%;改进后遗传算法平均收敛速度提升51.33%,算法耗时降低31.16%;考虑光伏出力优化后的储能容量装机成本降低6.63%,投资回收期减少0.7 a。算例结果表明：该方法有助于解决光伏出力波动造成的储能建设投资大、运行收益低、投资回收周期长等经济效益差的问题,有利于推动光伏储能事业持续健康发展,保障“双碳”目标顺利实现。

Abstract

In order to eliminate the interference of PV output fluctuation on energy storage configuration, a method based on a PV output prediction model and an improved genetic algorithm （IGA） was proposed to determine the optimal energy storage capacity. Based on long short-term memory neural network （LSTM） to predict PV output, variational mode decomposition （VMD） to capture PV fluctuation characteristics, and improved sparrow algorithm （ISSA） to optimize LSTM neural network hyperparameters, a VMD-ISA-LSTM PV output prediction model was established. The optimal allocation of energy storage capacity is realized by an IGA that employs population partitioning and an improved mutation operator. The average absolute error, root mean square error and average absolute percentage error of PV output predicted by VMD-ISSA-LSTM can reach 12.48 kW, 14.44 kW and 2.16%, respectively. After the improvement, the average convergence speed of genetic algorithm is increased by 51.33%, and the algorithm runtime is reduced by 31.16%. Considering the optimization of photovoltaic output, the installed cost of energy storage capacity is reduced by 6.63%, and the investment payback period is reduced by 0.7 years. The results of the example show that the method is helpful to solve the problems of large investment in energy storage construction, low operating income, long investment recovery cycle and other poor economic benefits caused by photovoltaic output fluctuations, and is conducive to promoting the sustainable and healthy development of photovoltaic energy storage industry and ensuring the smooth realization of the “dual carbon” goal.

导出引用

程跃珂, 王钰洁. 考虑光伏出力的储能系统容量优化配置研究[J]. 太阳能学报. 2025, 46(11): 61-68 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1169

Cheng Yueke, Wang Yujie. RESEARCH ON CAPACITY OPTIMIZATION OF ENERGY STORAGE SYSTEM CONSIDERING PHOTOVOLTAIC OUTPUT[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 61-68 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1169

中图分类号： TM715

参考文献

[1] 国家能源局. 《关于促进新型储能并网和调度运用的通知》[EB/OL].[2024-04-02]. https://www.gov.cn/zhengce/zheng ceku/202404/content_6945448.htm.
NATIONAL ENERGY ADMINISTRATION.Notice on promoting the grid connection and scheduling application of new energy storage facilities[EB/OL] . [2024-04-02].https://www.gov.cn/zhengce/ zhengceku/202404/content_6945448.htm.
[2] 丁涛, 孙嘉玮, 黄雨涵, 等. 储能参与容量市场的国内外现状及机制思考[J]. 电力系统自动化, 2024, 48(6): 226-239.
DING T, SUN J W, HUANG Y H, et al.Domestic and foreign present situation of capacity market with energy storage and reflection on its mechanism[J]. Automation of electric power systems, 2024, 48(6): 226-239.
[3] 国家能源局. 太阳能发电装机容量超7亿千瓦[EB/OL].[2024-07-26]. https://www.nea.gov.cn/2024-07/26/c_131078281 3.htm.
NATIONAL ENERGY ADMINISTRATION.Installed capacity of solar power generation exceeds 700 million kilowatts[EB/OL]. [2024-07-26].https://www.nea.gov.cn/2024-07/26/c_131078281 3.htm.
[4] ZHANG D L, MA Y Y, LIU J X, et al.Stochastic optimization method for energy storage system configuration considering self-regulation of the state of charge[J]. Sustainability, 2022, 14(1): 553.
[5] 黎博, 陈民铀, 钟海旺, 等. 高比例可再生能源新型电力系统长期规划综述[J]. 中国电机工程学报, 2023, 43(2): 555-581.
LI B, CHEN M Y, ZHONG H W, et al.A review of longterm planning of new power systems with large share of renewable energy[J]. Proceedings of the CSEE, 2023, 43(2): 555-581.
[6] ALQUTHAMI T, RAVINDRA H, FARUQUE M O, et al.Study of photovoltaic integration impact on system stability using custom model of PV arrays integrated with PSS/E[C]//North American Power Symposium 2010. Arlington, TX, USA, 2010: 1-8.
[7] 柳长发, 付立衡, 张增丽, 等. 高比例光伏接入的分布式储能容量自适应协调控制方法[J]. 储能科学与技术, 2024, 13(8): 2696-2703.
LIU C F, FU L H, ZHANG Z L, et al.Adaptive coordinated control method for distributed energy storage capacity with high proportion of photovoltaic access[J]. Energy storage science and technology, 2024, 13(8): 2696-2703.
[8] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[9] 王涛, 王旭, 许野, 等. 计及相似日的LSTM光伏出力预测模型研究[J]. 太阳能学报, 2023, 44(8): 316-323.
WANG T, WANG X, XU Y, et al.Study on LSTM photovoltaic output prediction model considering similar days[J]. Acta energiae solaris sinica, 2023, 44(8): 316-323.
[10] 史彭珍, 魏霞, 张春梅, 等. 基于VMD-BOA-LSSVM-AdaBoost的短期风电功率预测[J]. 太阳能学报, 2024, 45(1): 226-233.
SHI P Z, WEI X, ZHANG C M, et al.Short-term wind power prediction based on VMD-BOA-LSSVM-AdaBoost[J]. Acta energiae solaris sinica, 2024, 45(1): 226-233.
[11] 王超, 蔺红, 庞晓虹. 基于HPO-VMD和MISMA-DHKELM的短期光伏功率组合预测[J]. 太阳能学报, 2023, 44(12): 65-73.
WANG C, LIN H, PANG X H.Short-term photovoltaic power combination prediction based on HPO-VMD and MISMA-DHKELM[J]. Acta energiae solaris sinica, 2023, 44(12): 65-73.
[12] 李争, 张杰, 徐若思, 等. 基于相似日聚类和PCC-VMD-SSA-KELM模型的短期光伏功率预测[J]. 太阳能学报, 2024, 45(2): 460-468.
LI Z, ZHANG J, XU R S, et al.Short term photovoltaic power prediction based on similar day clustering and PCC-VMD-SSA-KELM model[J]. Acta energiae solaris sinica, 2024, 45(2): 460-468.
[13] 张德隆, Mubaarak Saif, 蒋思宇, 等. 基于概率潮流的光伏电站中储能系统的优化配置方法[J]. 储能科学与技术, 2021, 10(6): 2244-2251.
ZHANG D L, SAIF M, JIANG S Y, et al.Optimal allocation method of energy storage in PV station based on probabilistic power flow[J]. Energy storage science and technology, 2021, 10(6): 2244-2251.
[14] 李建林, 郭斌琪, 牛萌, 等. 风光储系统储能容量优化配置策略[J]. 电工技术学报, 2018, 33(6): 1189-1196.
LI J L, GUO B Q, NIU M, et al.Optimal configuration strategy of energy storage capacity in wind/PV/storage hybrid system[J]. Transactions of China Electrotechnical Society, 2018, 33(6): 1189-1196.
[15] 王子辉, 贾燕冰, 李彦晨, 等. 计及电能质量影响的主动配电网光储容量优化[J]. 电网技术, 2024, 48(2): 607-620.
WANG Z H, JIA Y B, LI Y C, et al.Optimal capacity configuration of photovoltaic and energy storage system in active distribution network considering influence of power quality[J]. Power system technology, 2024, 48(2): 607-620.
[16] 孙东磊, 赵龙, 孙凯祺, 等. 新能源接入下配电系统混合储能选址定容优化策略研究[J]. 太阳能学报, 2024, 45(1): 423-432.
SUN D L, ZHAO L, SUN K Q, et al.Research on optimization strategy of HESS site and capacity selection in distribution system with renewable energy access[J]. Acta energiae solaris sinica, 2024, 45(1): 423-432.
[17] 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.
[18] 马汇海, 宋金鹏, 康家玉. 基于分时电价的用户侧光伏储能系统容量配置[J]. 科学技术与工程, 2023, 23(31): 13387-13393.
MA H H, SONG J P, KANG J Y.Capacity configuration of user-side photovoltaic energy storage system based on time-of-use electric price[J]. Science technology and engineering, 2023, 23(31): 13387-13393.
[19] 董明, 李晓枫, 杨章, 等. 基于数据驱动的分布式光伏发电功率预测方法研究进展[J]. 电网与清洁能源, 2024, 40(1): 8-17, 28.
DONG M, LI X F, YANG Z, et al.Research progress on data-driven prediction methods for distributed photovoltaic power generation[J]. Power system and clean energy, 2024, 40(1): 8-17, 28.
[20] 周家亿, 赵双双, 王忠东, 等. 结合用户画像的DTW-MANN-FM分布式光伏短期出力预测模型[J]. 太阳能学报, 2023, 44(9): 187-193.
ZHOU J Y, ZHAO S S, WANG Z D, et al.DTW-MANN-FM model combined with user profile for distributed photovoltaic power short-term forecasting[J]. Acta energiae solaris sinica, 2023, 44(9): 187-193.
[21] 南京图德科技. 羲和能源气象大数据平台[EB/OL].[2024-05-09]. https://xihe-energy.com.
Nanjing Tuduo Technology.Xihe energy meteorological big data platform[EB/OL]. [2024-05-09].https://xihe-energy.com.
[22] 贾宏新, 张宇, 王育飞, 等. 储能技术在风力发电系统中的应用[J]. 可再生能源, 2009, 27(6): 10-15.
JIA H X, ZHANG Y, WANG Y F, et al.Energy storage for wind energy applications[J]. Renewable energy resources, 2009, 27(6): 10-15.
[23] 张文亮, 丘明, 来小康. 储能技术在电力系统中的应用[J]. 电网技术, 2008, 32(7): 1-9.
ZHANG W L, QIU M, LAI X K.Application of energy storage technologies in power grids[J]. Power system technology, 2008, 32(7): 1-9.
[24] 中国储能网. 开局2024:储能价格战“起底”[EB/OL].[2024-01-23]. https://www.escn.com.cn/.
ENERGY STORAGE CN. The start of2024: the price war in energy storage begins[EB/OL]. [2024-01-23]. https://www.escn.com.cn/.