基于目标优选和模型预测控制的风储优化策略

doi:10.19781/j.issn.1673-9140.2023.01.001

首页 > 过刊浏览>2023年第38卷第1期 >1-10. DOI:10.19781/j.issn.1673-9140.2023.01.001

基于目标优选和模型预测控制的风储优化策略
DOI:
                        10.19781/j.issn.1673-9140.2023.01.001
                    
CSTR:
                        [cstr]
                    
作者:
                        严 潇1,2严 潇
三峡大学电气与新能源学院，湖北 宜昌 443002；三峡大学智慧能源技术湖北省工程研究中心，湖北 宜昌 443002
在期刊界中查找
在百度中查找
在本站中查找
程 杉1,2程 杉
三峡大学电气与新能源学院，湖北 宜昌 443002；三峡大学智慧能源技术湖北省工程研究中心，湖北 宜昌 443002
在期刊界中查找
在百度中查找
在本站中查找
左先旺1,2左先旺
三峡大学电气与新能源学院，湖北 宜昌 443002；三峡大学智慧能源技术湖北省工程研究中心，湖北 宜昌 443002
在期刊界中查找
在百度中查找
在本站中查找
魏康林1,3魏康林
三峡大学电气与新能源学院，湖北 宜昌 443002；宜宾学院智能制造学院，四川 宜宾 644000
在期刊界中查找
在百度中查找
在本站中查找

                    
作者单位:(1.三峡大学电气与新能源学院，湖北 宜昌 443002;2.三峡大学智慧能源技术湖北省工程研究中心，湖北 宜昌 443002;3.宜宾学院智能制造学院，四川 宜宾 644000)
作者简介:
通讯作者:程 杉(1981—)，男，博士，教授，主要从事智能配用电、综合能源系统、新能源微电网研究；E?mail:hpucquyzu@ctgu.edu.cn
中图分类号:TM863
基金项目:国家自然科学基金(51607105)；四川省重点实验室开放基金(SCITLAB?1009)

Optimally selected objective and model predictive control based optimal strategy of wind power with energy storage

Author:

YAN Xiao ^{^1,2}
YAN Xiao
College of Electrical Engineering and New Energy， China Three Gorges University, Yichang 443002, China; Hubei Provincial Engineering Research Center of Intelligent Energy Technology，China Three Gorges University， Yichang 443002, China
在期刊界中查找
在百度中查找
在本站中查找
CHENG Shan ^{^1,2}
CHENG Shan
College of Electrical Engineering and New Energy， China Three Gorges University, Yichang 443002, China; Hubei Provincial Engineering Research Center of Intelligent Energy Technology，China Three Gorges University， Yichang 443002, China
在期刊界中查找
在百度中查找
在本站中查找
ZUO Xianwang ^{^1,2}
ZUO Xianwang
College of Electrical Engineering and New Energy， China Three Gorges University, Yichang 443002, China; Hubei Provincial Engineering Research Center of Intelligent Energy Technology，China Three Gorges University， Yichang 443002, China
在期刊界中查找
在百度中查找
在本站中查找
WEI Kanglin ^{^1,3}
WEI Kanglin
College of Electrical Engineering and New Energy， China Three Gorges University, Yichang 443002, China; Faculty of Intelligence Manufacturing, Yibin University, Yibin 644000,China
在期刊界中查找
在百度中查找
在本站中查找

Affiliation:

(1.College of Electrical Engineering and New Energy， China Three Gorges University, Yichang 443002, China; 2.Hubei Provincial Engineering Research Center of Intelligent Energy Technology，China Three Gorges University， Yichang 443002, China; 3.Faculty of Intelligence Manufacturing, Yibin University, Yibin 644000,China)

Fund Project:

摘要

图/表

访问统计

参考文献 [21]

相似文献

引证文献

资源附件

文章评论

摘要:

针对风储系统风电功率波动平抑效果不佳的问题，对风储系统的运行进行优化。在考虑风储系统运行的时序耦合特性和未来风电功率波动对储能系统的影响下，提出基于平抑目标优选方法和模型预测控制的优化策略。首先根据风电预测功率和储能固有约束求出期望的并网功率，再结合当前的储能荷电状态（SOC）等条件由模糊控制求出优选平抑目标，并引入局部预测准度对其进行修正；然后利用模型预测控制—粒子群优化算法（MPC?PSO）策略滚动优化储能功率，使下一时段并网功率与优选目标功率之差最小且储能充放功率最小；最后基于算例的仿真和对比分析结果可见，所提优化策略既能提升风电波动的平抑效果，又能有效地降低储能运行成本。

关键词:风电波动平抑;平抑目标优选;模糊控制;模型预测控制

Abstract:

Aiming at the problem that the wind storage system's power fluctuation suppression effect is unsatisfactory, the wind storage system's operation is optimized. Considering the time series coupling characteristics of wind storage system operation and the influence of future wind power fluctuation on the energy storage system (ESS), an optimization strategy based on stabilizing target optimization method and model predictive control (MPC) is proposed. Firstly, the expected grid?connected power is calculated according to the predicted wind power and the constraints of ESS, and the local prediction accuracy is introduced to correct it. Then, combined with the current state of charge (SOC) of ESS, the optimal stabilization target is obtained by fuzzy control; Lastly, the MPC with particle swarm optimization (MPC?PSO) strategy is used to optimize the ESS power, so as to minimize the difference between the grid?connected power of next time and the optimal target power and minimize the ESS power. The simulation results show that the strategy proposed in this paper has a better wind power fluctuation smoothing effect and can effectively reduce the operation cost of energy storage.

Key words:wind power fluctuation smoothing; optimal selection of target; fuzzy control; model predictive control

参考文献

[1] 程杉,冯毅煁,黄天力.含风电的电力系统区间调度问题求解[J].可再生能源,2019,37[(2)]:199?204. CHENG Shan,FENG Yichen,HUANG Tianli.A solution to interval scheduling problem in power system containing wind power[J].Renewable Energy Resources,2019,37[(2)]:199?204.

[2] 刘栋,魏霞,王维庆,等.基于SSA?ELM的短期风电功率预测[J].智慧电力,2021,49[(6)]:53?59+123. LIU Dong，WEI Xia，WANG Weiqing,et al.Short?term wind power prediction based on SSA?ELM[J].Smart Power,2021,49[(6)]:53?59+123.

[3] 刘明,曾成碧,苗虹.考虑风电不确定性的分布鲁棒机会约束机组组合模型[J].电力科学与技术学报,2021,36[(2)]:51?57. LIU Ming,ZENG Chengbi,MIAO Hong.Distributionally robust chance?constrained unit commitment model considering uncertainty of wind power[J].Journal of Electric Power Science and Technology,2021,36[(2)]:51?57.

[4] 姚方,王佳伟,文福拴,等.计及碳税的含风电和储能的电力系统经济调度[J].电力科学与技术学报,2019,34[(1)]:37?46. YAO Fang,WANG Jiawei,WEN Fushuan,et al.Economic dispatch for a power system containing wind power and energy storage with Carbon tax considered[J].Journal of Electric Power Science and Technology,2019,34[(1)]:37?46.

[5] 鲁鹏,田浩,武伟鸣,等.需求侧能量枢纽和储能协同提升风电消纳和平抑负荷峰谷模型[J].电力科学与技术学报,2021,36[(1)]:42?51. LU Peng,TIAN Hao,WU Weiming,et al.Demand side energy hub and energy storage cooperate to smooth peak and valley and improve wind power consumption model[J].Journal of Electric Power Science and Technology,2021,36[(1)]:42?51.

[6] 任大伟,金晨,肖晋宇,等.计及灵活性基于时序的“十四五”储能需求分析[J].中国电力,2021,54[(8)]:190?198. REN Dawei,JIN Chen,XIAO Jinyun,et al.Demand analysis of energy storage for the 14th five?year plan period based on time series considering power system flexibility[J].Electric Power,2021,54[(8)]:190?198.

[7] HOWLADER A M,URASAKI N,PRATAP A,et al.A fuzzy control strategy for power smoothing and grid dynamic response enrichment of a grid connected wind energy conversion system[J].Wind Energy,2014,17[(9)]:1347?1363.

[8] 刘春燕,晁勤,魏丽丽.基于实证数据和模糊控制的多时间尺度风储耦合实时滚动平抑波动[J].电力自动化设备,2015,35[(2)]:35?41. LIU Chunyan,CHAO Qin,WEI Lili.Real?time rolling suppression of fluctuation based on multi?time?scale wind storage coupling based on empirical data and fuzzy control[J].Electric Power Automation Equipment,2015,35[(2)]:35?41.

[9] CHENG S,SUN W B,LIU W L.Multi?objective configuration optimization of a hybrid energy storage system[J].Applied Sciences,2017,7[(2)]:163.

[10] 蒋小平,彭朝阳,魏立彬,等.基于模糊控制的混合储能平抑风电功率波动[J].电力系统保护与控制,2016,44[(17)]:126?132. JIANG Xiaoping,PENG Chaoyang,WEI Libin,et al.Hybrid energy storage based on Fuzzy control to smooth wind power fluctuation[J].Power System Protection and Control,2016,44[(17)]:126?132.

[11] 章竹耀,郭晓丽,张新松,等.储能电池平抑风功率波动策略[J].电力系统保护与控制,2017,45[(3)]:62?68. ZHANG Zhuyao,GUO Xiaoli,ZHANG Xinsong,et al.Strategies for wind power fluctuation of energy storage batteries[J].Power System Protection and Control,2017,45[(3)]:62?68.

[12] 刘颖明,王晓东,彭朝阳.计及储能出力水平的平滑风电功率模型预测控制策略[J].电网技术,2020,44[(5)]:1723?1731. LIU Yingming,WANG Xiaodong,PENG Chaoyang.Model predictive control strategy for smoothing wind power with energy storage output level[J].Power System Technology,2020,44[(5)]:1723?1731.

[13] 侯力枫.风电功率波动平抑下储能出力与平滑能力的动态优化控制策略[J].热力发电,2020,49[(8)]:134?142. HOU Lifeng.Dynamic optimization control strategy for energy storage output power and smoothing ability considering smoothing wind power fluctuation[J].Thermal Power Generation,2020,49[(8)]:134?142.

[14] 万筱钟,康耀元,呼斯乐,等.西北地区风电功率波动特性概率密度及波动统计[J].电网与清洁能源,2021,37[(4)]:107?115. WAN Xiaozhong,KANG Yaoyuan,HU Sile,et al.Probability density and fluctuation statistical analysis of wind power fluctuation characteristics in Northwest China[J].Power System and Clean Energy,2021,37[(4)]:107?115.

[15] 焦东东,陈洁,方圆,等.基于变分模态分解下利用混合储能平抑风电出力波动的控制策略[J].电测与仪表,2021,58 [(5)]:14?19+30. JIAO Dongdong,CHEN Jie,FANG Yuan,et al.Control strategy of hybrid energy storage for suppressing fluctuation of wind power output based on variational mode decomposition[J].Electrical Measurement ＆ Instrumentation,2021,58[(5)]:14?19+30.

[16] 李浩博,邹海荣,朱建红.考虑风电计划跟踪的储能调度模糊控制系统研究[J].电力系统保护与控制,2021,49[(1)]:125?132. LI Haobo,ZOU Hairong,ZHU Jianhong.Research on a fuzzy control system of energy storage dispatch considering wind power plan tracking[J].Power System Protection and Control,2021,49[(1)]:125?132.

[17] 朱显辉,于越,师楠,等.BP神经网络的分层优化研究及其在风电功率预测中的应用[J].高压电器,2022,58 [(2)]:158?163+170. ZHU Xianhui,YU Yue,SHI Nan,et al.Research on hierarchical optimization of BP neural network and its application in wind power prediction[J].High Voltage Apparatus,2022,58[(2)]:158?163+170.

[18] 杨银国,李博,谭嫣,等.改进型小波神经网络在风光功率预测中的应用研究[J].信息技术,2020,44[(2)]:98?102. YANG Yinguo,LI Bo,TAN Yan,et al.Application of improved wavelet neural network in landscape power prediction[J].Information Technology,2020,44[(2)]:98?102.

[19] 张明龙,张振宇,罗翔,等.基于多核支持向量机的混合扰动波形辨识算法研究[J].电力系统保护与控制,2022,50(15):43?49. ZHANG Minglong,ZHANG Zhenyu,LUO Xiang,et al.Complex disturbance waveform recognition based on a multi?kernel support vector machine[J].Power System Protection and Control,2022,50(15):43?49.

[20] 罗珂珂.基于回归支持向量机的风功率预测误差分析[J].技术与市场,2020,27[(4)]:62?64. LUO Keke.Wind power prediction error analysis based on regression support vector machine[J].Technology and Market,2020,27[(4)]:62?64.

[21] 程杉,许林峰,孙伟斌,等.基于电压稳定性的电动汽车充电站最优规划[J].电力科学与技术学报,2020,35[(4)]:3?12. CHENG Shan,XU Linfeng,SUN Weibin,et al.Optimal planning of charging stations for electric vehicles based on voltage stability index[J].Journal of Electric Power Science and Technology,2020,35[(4)]:3?12.

引用本文

严潇,程杉,左先旺,等.基于目标优选和模型预测控制的风储优化策略[J].电力科学与技术学报,2023,38(1):1-10.
YAN Xiao, CHENG Shan, ZUO Xianwang, et al. Optimally selected objective and model predictive control based optimal strategy of wind power with energy storage[J]. Journal of Electric Power Science and Technology,2023,38(1):1-10.

复制

文章指标

点击次数:566
下载次数: 1272
HTML阅读次数: 0
引用次数: 0

历史

收稿日期:
最后修改日期:
录用日期:
在线发布日期: 2023-04-10
出版日期:

引用本文

分享

文章指标

历史

文章二维码

引用本文

分享

微信扫一扫：分享

文章指标

历史

文章二维码