基于BP-MIV的风电调峰受阻电量影响因素贡献度分析

Hua XIE; Xiaoxi LYU; Pei ZHANG

doi:10.16513/j.2096-2185.DE.191076

PDF(1109 KB)

分布式能源 ›› 2019, Vol. 4 ›› Issue (6) : 9-14. DOI: 10.16513/j.2096-2185.DE.191076

基于先进信息通信技术的分布式能源系统

基于BP-MIV的风电调峰受阻电量影响因素贡献度分析

作者信息 +

Contribution Analysis of Influential Factors for Wind Power Curtailment Caused by Lack of Load-Following Capability Based on BP-MIV

Author information +

文章历史 +

摘要

随着风电装机容量的不断增长，弃风电量也随之增加，研究弃风电量的影响因素势在必行。为此，提出了一种风电调峰受阻电量影响因素贡献度计算方法。以影响因素为输入，风电调峰受阻电量为输出，构建了反向(back propagation, BP)神经网络模型；应用平均影响值(mean impact value, MIV)算法，计算各影响因素的贡献度；利用我国西北某电网2018年运行数据进行案例分析。结果表明，本文所提方法能对风电调峰受阻电量影响因素进行量化分析，明确影响因素的重要程度，为促进风电消纳提供理论基础。

Abstract

In recent years, wind power curtailment has been a big concern with the rapid growth of wind power installed capacity in China. It is essential to investigate influential factors of wind power curtailment and how much these factors affect wind power curtailment. A novel method to quantify influential factors of wind power curtailment caused by lack of load-following capability is proposed. First, this paper applies back propagation(BP) neural network to model the nonlinear relationship between influential factors and wind power curtailment caused by lack of load-following capability. Then, this paper utilizes mean impact value(MIV) to compute contribution of influential factors. Finally, case study on a provincial power grid in northwest region of China is carried out to validate the proposed method. The study result indicates that the proposed method can quantify the importance of each influential factor.

导出引用

Hua XIE, Xiaoxi LYU, Pei ZHANG. 基于BP-MIV的风电调峰受阻电量影响因素贡献度分析[J]. 分布式能源. 2019, 4(6): 9-14 https://doi.org/10.16513/j.2096-2185.DE.191076

Contribution Analysis of Influential Factors for Wind Power Curtailment Caused by Lack of Load-Following Capability Based on BP-MIV[J]. Distributed Energy Resources. 2019, 4(6): 9-14 https://doi.org/10.16513/j.2096-2185.DE.191076

中图分类号： TK81

参考文献

列表( 原文顺序 | 文献年度倒序 | 文中引用次数倒序 ) 可视化分析

[1]	SHU Yinbiao, ZHANG Zhigang, GUO Jianbo, et al. Study on key factors and solution of renewable energy accommodation[J]. Proceedings of the CSEE, 2017, 37(1): 1-9. 舒印彪，张智刚，郭剑波，等. 新能源消纳关键因素分析及解决措施研究[J]. 中国电机工程学报，2017, 37(1): 1-9. 本文引用 [1]

[2]	ZHU Lingzhi, CHEN Ning, HAN Hualing. Key problems and solution of wind power accommodation[J]. Automation of Electric Power Systems, 2011, 35(22): 29-34. 朱凌志，陈宁，韩华玲. 风电消纳关键问题及应对措施分析[J]. 电力系统自动化，2011, 35(22): 29-34.

[3]	WEI Lei, JIANG Ning, YU Guangliang, et al. Research on Ningxia power grid's ability of admitting new energy resources[J]. Power System Technology, 2010, 34(11): 176-181. 魏磊，姜宁，于广亮，等. 宁夏电力系统接纳新能源能力研究[J]. 电网技术，2010, 34(11): 176-181.

[4]	LI Jindong. Analysis of key factors of new energy absorption and study of solutions[J]. Intelligent City, 2018, 4(1): 66-67. 李金东. 新能源消纳关键因素分析及解决措施研究[J]. 智能城市，2018, 4(1): 66-67.

[5]

Jixian

, LIU

Chun

, SHI

Wenhui

, et al. A fast evaluation method for wind power curtailment ratio based on regression model of electric energy of wind power accommodating space in power grid[J]. Power System Technology, 2017, 41(1): 75-81.

屈姬贤，刘纯，石文辉，等. 基于风电接纳空间电量回归模型的弃风率快速计算方法[J]. 电网技术，2017, 41(1): 75-81.

本文引用 [1]

[6]

LIU

Xinyuan

, SONG

Shuyong

, ZHENG

Huiping

, et al. Research and application of the multivariable comprehensive evaluation model on the regional grid for renewable energy accommodation[J]. Electrical & Energy Management Technology, 2018(1): 39-43.

刘新元，宋述勇，郑惠萍，等. 区域电网新能源消纳多变量综合评估模型研究及应用[J]. 电器与能效管理技术，2018(1): 39-43.

[7]	LI Hai, ZHANG Ning, KANG Chongqing, et al. Analytics of contribution degree for renewable energy accommodation factors[J]. Proceedings of the CSEE, 2019, 39(4): 1009-1018. 李海，张宁，康重庆，等. 可再生能源消纳影响因素的贡献度分析方法[J]. 中国电机工程学报，2019, 39(4): 1009-1018.

[8]	ZHANG Weidong. Analysis of wind power abandoning reasons and countermeasures in China[J]. China power enterprise management, 2013(7): 26-29. 张卫东. 我国风电弃风原因及对策分析[J]. 中国电力企业管理，2013(7): 26-29. 本文引用 [1]

[9]	HAN Xiaoqi, SUN Shouguang, QI Qingru. Evaluation of wind power penetration limit from peak regulation[J]. Electric Power, 2010, 43(6): 16-19. 韩小琪，孙寿广，戚庆茹. 从系统调峰角度评估电网接纳风电能力[J]. 中国电力，2010, 43(6): 16-19. 本文引用 [1]

[10]	ZHANG Hongyu, YIN Yonghua, SHEN Hong, et al. Peak-load regulating adequacy evaluation associated with large-scale wind power integration[J]. Proceedings of the CSEE, 2011, 31(22): 26-31. 张宏宇，印永华，申洪，等. 大规模风电接入后的系统调峰充裕性评估[J]. 中国电机工程学报，2011, 31(22): 26-31. 本文引用 [1]

[11]	LIN Li, TIAN Xinyu. Analysis of deep peak regulation and its benefit of thermal units in power system with large scale wind power integrated[J]. Power System Technology, 2017, 41(7): 2255-2263. 林俐，田欣雨. 基于火电机组分级深度调峰的电力系统经济调度及效益分析[J]. 电网技术，2017, 41(7): 2255-2263. 本文引用 [1]

[12]	XIAO Chuangying, WANG Ningbo, DING Kun, et al. System power regulation scheme for Jiuquan wind power base[J]. Proceedings of the CSEE, 2010, 30(10): 1-7. 肖创英，汪宁渤，丁坤，等. 甘肃酒泉风电功率调节方式的研究[J]. 中国电机工程学报，2010, 30(10): 1-7. 本文引用 [1]

[13]	RUMELHART D E, MCCLELLAND J L. Parallel distributed processing[M]. MIT Press, 1986. 本文引用 [2]

[14]	GAO Ning. Crop pest prediction based on bp neural network and its realization by MATLAB [D]. Hefei: Anhui Agricultural University, 2013. 高宁. 基于BP神经网络的农作物虫情预测预报及其MATLAB实现[D]. 合肥：安徽农业大学，2003. 本文引用 [1]

[15]	HAN Zhe, CHEN Zhiping, XIONG Siyi, et al. Phisical neural networks and applied research on the power short-term load forecast[J]. Science Technology and Engineering, 2009, 9(5): 1136-1141. 韩哲，陈治平，熊斯毅，等. 人工神经网络及其在电力短期负荷预测中的应用研究[J]. 科学技术与工程，2009, 9(5): 1136-1141. 本文引用 [1]

[16]	HU Hui, YANG Hua, HU Bin. Application of artificial ANN to short-term load forecasting in power system[J]. Journal of Hunan University (Natural Sciences), 2004, 31(5): 51-53. 胡晖，杨华，胡斌. 人工神经网络在电力系统短期负荷预测中的应用[J]. 湖南大学学报(自然科学版), 2004, 31(5): 51-53. 本文引用 [1]

[17]	DING Ming, WANG Lei, BI Rui. A short-term prediction model to forecast output power of photovoltaic system based On improved BP neural network[J]. Power System Protection and Control, 2012, 40(11): 93-99, 148. 丁明，王磊，毕锐. 基于改进BP神经网络的光伏发电系统输出功率短期预测模型[J]. 电力系统保护与控制，2012, 40(11): 93-99, 148. 本文引用 [1]

[18]	LI Zhibin, CHEN Chengyou, LI Qiben, et al. Fault diagnosis of power transformer based on bp neural network[J]. Control and Instruments in Chemical Industry, 2013, 40(4): 494-496, 533. 李志斌，陈成优，李启本，等. 基于BP神经网络的变压器故障诊断[J]. 化工自动化及仪表，2013, 40(4): 494-496, 533. 本文引用 [1]

[19]	WEI Sun, QI Gao. Exploration of energy saving potential in China power industry based on adaboost back propagation neural network[J]. Journal of Cleaner Production, 2019, 217: 257-266. 本文引用 [1]

[20]	SUN Wenbin, LIU Xiliang, WANG Hongbin, et al. Weight analysis of cast blasting effective factors based on MIV method[J]. Journal of China University of Mining & Technology, 2012, 41(6): 993-998. 孙文彬，刘希亮，王洪斌，等. 基于MIV的抛掷爆破影响因子权重分析[J]. 中国矿业大学学报，2012, 41(6): 993-998. 本文引用 [1]

[21]	YANG Bin, LI Jingyang, WEN Lei. Weight analysis of atmospheric aging of polycarbonate impact factors based on MIV method[J]. Journal of Materials Engineering, 2018, 46(12): 101-109. 杨斌，李敬洋，文磊. 基于MIV的聚碳酸酯大气老化影响因子权重分析[J]. 材料工程，2018, 46(12): 101-109. 本文引用 [1]