风储系统多目标之间存在难以协调控制的问题,权重系数的选择对多目标控制效果具有显著影响。首先,构建以储能充放平衡、并网波动最小和储能输出最小的风储系统多目标函数,考虑各子目标权重系数对风储系统的影响,提出一种改进鲸鱼算法（IWOA）优化模型预测控制（MPC）的风储协调运行方法,采用组合隶属度函数进行满意度评价,得到混合储能最优总控制量和权重系数。然后,采用集合经验模式分解（ICEEMDAN）算法对混合储能总控制量进行分解,利用Pearson相关系数对分解分量进行重构,实现混合储能功率的分配。最后,以新疆某风电场数据进行仿真,验证所提优化模型的有效性和功率分配策略的合理性。
Abstract
There is a problem that it is difficult to coordinate the control between multiple objectives of the wind storage system, the selection of weight coefficient has a significant impact on the control effect of multiple objectives. Firstly, this paper constructs a multi-objective function of the wind storage system with the balance of energy storage charging and discharging, the smallest grid-connected fluctuation and the smallest energy storage output, considers the influence of each sub-target weight coefficient on the wind storage system, and proposes a wind storage coordinated operation method that improves the predictive control （MPC） of the whale algorithm （IWOA） optimization model, and uses the combined membership function for satisfaction evaluation to obtain the optimal total control and weight coefficient of hybrid energy storage. Then, the set empirical mode decomposition （ICEEMDAN） is used to decompose the total control quantity of hybrid energy storage, and the Pearson correlation coefficient is used to reconstruct the decomposition component to realize the distribution of hybrid energy storage power. Finally, the simulation of a wind farm data in Xinjiang verifies the effectiveness of the proposed optimization model and the rationality of the power allocation strategy.
关键词
风储系统 /
模型预测控制 /
混合储能 /
权重系数 /
改进鲸鱼算法 /
组合模糊隶属度函数
Key words
wind storage system /
MPC /
hybrid energy storage /
weight coefficient /
IWOA /
combinatorial fuzzy membership function
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] AL HADI A, SANTOS SILVA C A, HOSSAIN E, et al. Algorithm for demand response to maximize the penetration of renewable energy[J]. IEEE access, 1877, 8: 55279-55288.
[2] ZHOU A P, YANG M, WANG Z Y, et al.A linear solution method of generalized robust chance constrained real-time dispatch[J]. IEEE transactions on power systems, 2018, 33(6): 7313-7316.
[3] 徐国栋, 程浩忠, 马紫峰, 等. 用于平滑风电出力的储能系统运行与配置综述[J]. 电网技术, 2017, 41(11): 3470-3479.
XU G D, CHENG H Z, MA Z F, et al.An overview of operation and configuration of energy storage systems for smoothing wind power outputs[J]. Power system technology, 2017, 41(11): 3470-3479.
[4] 刘军, 甘乾煜, 张泽秋, 等. 考虑储能电池运行寿命的风电功率波动平抑方法研究[J]. 电网技术, 2023, 47(5): 2098-2108.
LIU J, GAN Q Y, ZHANG Z Q, et al.Research on wind power fluctuation flattening method considering the operating life of energy storage battery[J]. Power system technology, 2023, 47(5): 2098-2108.
[5] 谢丽蓉, 郑浩, 魏成伟, 等. 兼顾补偿预测误差和平抑波动的光伏混合储能协调控制策略[J]. 电力系统自动化, 2021, 45(3): 130-138.
XIE L R, ZHENG H, WEI C W, et al.Coordinated control strategy of photovoltaic hybrid energy storage considering prediction error compensation and fluctuation suppression[J]. Automation of electric power systems, 2021, 45(3): 130-138.
[6] 贾伟青, 任永峰, 薛宇, 等. 基于小波包-模糊控制的混合储能平抑大型风电场功率波动[J]. 太阳能学报, 2021, 42(9): 357-363.
JIA W Q, REN Y F, XUE Y, et al.Wavelet packet-fuzzy control of hybrid energy storage for power fluctuation smoothing of large wind farm[J]. Acta energiae solaris sinica, 2021, 42(9): 357-363.
[7] 孙玉树, 唐西胜, 孙晓哲, 等. 基于MPC-HHT的多类型储能协调控制策略研究[J]. 中国电机工程学报, 2018, 38(9): 2580-2588, 2826.
SUN Y S, TANG X S, SUN X Z, et al.Research on multi-type energy storage coordination control strategy based on MPC-HHT[J]. Proceedings of the CSEE, 2018, 38(9): 2580-2588, 2826.
[8] 李中豪, 张沛超, 马军, 等. 采用动态赋权的风储协调多目标优化控制方法[J]. 电力系统自动化, 2016, 40(12): 94-99, 206.
LI Z H, ZHANG P C, MA J, et al.Dynamic weighted multi-objective optimization method for coordinated control of wind power and battery energy storage system[J]. Automation of electric power systems, 2016, 40(12): 94-99, 206.
[9] 李亚楠, 王倩, 宋文峰, 等. 混合储能系统平滑风电出力的变分模态分解-模糊控制策略[J]. 电力系统保护与控制, 2019, 47(7): 58-65.
LI Y N, WANG Q, SONG W F, et al.Variational mode decomposition and fuzzy control strategy of hybrid energy storage for smoothing wind power outputs[J]. Power system protection and control, 2019, 47(7): 58-65.
[10] 张晴, 李欣然, 杨明, 等. 净效益最大的平抑风电功率波动的混合储能容量配置方法[J]. 电工技术学报, 2016, 31(14): 40-48.
ZHANG Q, LI X R, YANG M, et al.Capacity determination of hybrid energy storage system for smoothing wind power fluctuations with maximum net benefit[J]. Transactions of China Electrotechnical Society, 2016, 31(14): 40-48.
[11] 郭玲娟, 魏斌, 韩肖清, 等. 基于集合经验模态分解的交直流混合微电网混合储能容量优化配置[J]. 高电压技术, 2020, 46(2): 527-537.
GUO L J, WEI B, HAN X Q, et al.Capacity optimal configuration of hybrid energy storage in hybrid AC/DC micro-grid based on ensemble empirical mode decomposition[J]. High voltage engineering, 2020, 46(2): 527-537.
[12] 马兰, 谢丽蓉, 叶林, 等. 基于混合储能双层规划模型的风电波动平抑策略[J]. 电网技术, 2022, 46(3): 1016-1029.
MA L, XIE L R, YE L, et al.Wind power fluctuation suppression strategy based on hybrid energy storage Bi-level programming model[J]. Power system technology, 2022, 46(3): 1016-1029.
[13] 朱常安, 胡文华, 郭宝峰, 等. 基于熵权法和组合隶属函数的雷达质量评估方法研究[J]. 计算机测量与控制, 2022, 30(6): 302-307.
ZHU C A, HU W H, GUO B F, et al.Research on radar quality evaluation method based on entropy weight method and combined membership function[J]. Computer measurement & control, 2022, 30(6): 302-307.
[14] 张达敏, 徐航, 王依柔, 等. 嵌入Circle映射和逐维小孔成像反向学习的鲸鱼优化算法[J]. 控制与决策, 2021, 36(5): 1173-1180.
ZHANG D M, XU H, WANG Y R, et al.Whale optimization algorithm for embedded Circle mapping and one dimensional oppositional learning based small hole imaging[J]. Control and decision, 2021, 36(5): 1173-1180.
基金
国家自然科学基金（62163034）; 新疆重大科技专项（2022A01001-1）; 新疆风光柴储微电网工程技术研究中心; 新疆自治区天山创新团队（2021D14012）
PDF(1763 KB)
