基于统计分析的风-气能源基地远距离直流输电方案规划

doi:10.12204/j.issn.1000-7229.2020.10.008

电力建设 ›› 2020, Vol. 41 ›› Issue (10): 71-80.doi: 10.12204/j.issn.1000-7229.2020.10.008

基于统计分析的风-气能源基地远距离直流输电方案规划

段勉¹, 李勇汇¹, 杨军¹, 常喜强², 赵强³, 刘佩娴¹, 阿部¹

1.武汉大学电气与自动化学院,武汉市 430072
2.国网新疆电力有限公司乌鲁木齐供电公司,乌鲁木齐市 830011
3.中国电力科学研究院有限公司,北京市 100192

收稿日期:2020-02-20 出版日期:2020-10-01 发布日期:2020-09-30
作者简介:段勉(1996),女,硕士研究生,主要研究方向为输电网规划及评价;|李勇汇(1974),男,博士,副教授,主要研究方向为电网规划、高压直流输电;|杨军(1977),男,教授,博士生导师,主要研究方向为电力系统规划与运行、新能源接入技术;|常喜强(1976),男,高级工程师,主要研究方向为电力系统调度自动化;|赵强(1978),男,硕士,教授级高级工程师,主要研究方向为电力系统分析与规划、能源战略;|刘佩娴(1997),女,硕士研究生,主要研究方向为电力系统分析与运行;|阿部(1974),男,副教授,博士,主要研究方向为电力系统可靠性。
基金资助:
国家电网有限公司总部科技项目“源端基地综合能源系统关键技术及发展模式研究”(SGXJWL00JYJS1900575)

Long Distance DC Transmission Scheme Planning for Energy Base of Wind-Gas Based on Statistical Analysis

DUAN Mian¹, LI Yonghui¹, YANG Jun¹, CHANG Xiqiang², ZHAO Qiang³, LIU Peixian¹, Ghamgeen Izat RASHED¹

1. School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
2. Urumqi Power Supply Company, State Grid Xinjiang Electric Power Co.,Ltd., Urumqi 830011, China
3. China Electric Power Research Institute, Beijing 100192, China

Received:2020-02-20 Online:2020-10-01 Published:2020-09-30
Supported by:
research program “Research on the Key Technologies and Development Models of the Integrated Energy System at the Source Terminal”of State Grid Corporation of China(SGXJWL00JYJS1900575)

摘要/Abstract

摘要：

风电资源的大规模开发及天然气在一次能源的消费比重不断提高,给电力系统规划和运行带来新的挑战。为将大容量风电输送到数千千米外的负荷中心,采用具有远距离、大容量、低损耗输电能力的特高压直流输电系统。针对含丰富风电和天然气资源的能源基地远距离输电,提出了基于置信水平的风-气输电容量统计分析方法,综合考虑源端风-气综合能源出力的随机性、输电方案的经济技术环境及受端系统承受能力等方面的因素,构建了风-气能源基地的远距离直流输电评估指标体系,并基于改进前景理论法对风-气远距离直流输电系统候选方案进行综合优选。在算例分析中,通过灵敏度方法分析了不同输电方案的适用范围,并验证了所提规划方案的优越性,为大容量能源远距离直流输送规划的科学决策提供参考。

关键词: 统计分析, 随机性, 风-气能源基地, 输电规划, 方案比选

Abstract:

The large-scale development of wind power resources and the increasing proportion of natural gas consumption in primary energy have brought new challenges to the planning and operation of power systems. In order to transmit large-capacity wind power to load centers thousands of kilometers away, UHV DC transmission systems with long-distance, large-capacity, and low-loss power transmission capabilities are adopted. This paper proposes a statistical analysis method of wind-gas transmission capacity based on the confidence level for long-distance transmission of energy bases with abundant wind power and natural gas resources. Considering comprehensive various factors such as randomness of energy output, economic, technical and environmental nature of transmission scheme, and endurance capacity of receiving end system, a long-distance DC transmission evaluation index system is established for the wind-gas energy base. According to improved prospect theory, candidate schemes for HVDC transmission systems are comprehensively optimally selected. In the example analysis, the application scope of different transmission schemes is analyzed by the sensitivity method, and the superiority of the proposed planning scheme is verified, which provides a reference for scientific decision-making of large-capacity long-distance DC transmission planning.

Key words: statistical analysis, stochasticity, wind-gas energy base, transmission planning, scheme comparison

中图分类号:

TM715

段勉, 李勇汇, 杨军, 常喜强, 赵强, 刘佩娴, 阿部. 基于统计分析的风-气能源基地远距离直流输电方案规划[J]. 电力建设, 2020, 41(10): 71-80.

DUAN Mian, LI Yonghui, YANG Jun, CHANG Xiqiang, ZHAO Qiang, LIU Peixian, Ghamgeen Izat RASHED. Long Distance DC Transmission Scheme Planning for Energy Base of Wind-Gas Based on Statistical Analysis[J]. ELECTRIC POWER CONSTRUCTION, 2020, 41(10): 71-80.

图/表 15

图1 置信水平σ下的风-气混合出力分布拟合

Fig.1 Fitting of wind-gas mixed output distribution at σ confidence level

图2 综合评价指标体系

Fig.2 Comprehensive evaluation index system

图3 风-气出力爬坡量累积曲线

Fig.3 Cumulative curve of climbing amount of wind-gas output

图4 风-气持续出力曲线

Fig.4 Continuous output curve of wind-gas

图5 换流阀IGBT寿命消耗分布

Fig.5 Life consumption of IGBT in the converter

图6 不同因素影响的受端容量可信度

Fig.6 Credibility of the end capacity affected by different factors

图7 改进的前景价值函数

Fig.7 Improved foreground value function

图8 风-气混合出力分布

Fig.8 Distribution of wind-gas mixed output

表1 风-气混合出力各分布拟合优度

Table 1 Fitting situation of wind-gas mixed output distribution

图9 95%置信水平规划的输电容量

Fig.9 Transmission capacity indication under 95% confidence level

表2 不同决策者类型的综合前景值

Table 2 Comprehensive outlook value for different types of decision makers

表3 不同类型决策者前景值从小到大排序后的方案号

Table 3 Project number of different types of decision makers in ascending order of foreground value

图10 方案的前景值排序随灵敏度变化的几种情况

Fig.10 Several situations in which foreground value ranking varies with sensitivity

表A1 基于不同置信水平的输电方案各指标值

Table A1 Index values of transmission schemes based on different confidence levels

表A2 灵敏度为80%时22种方案7个指标的主客观综合赋权

Table A2 Subjective and objective comprehensive weighting of 7 indicators of 22 schemes at a sensitivity of 80%

参考文献 26

[1]	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):1-8.
	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):1-8.
[2]	孙天奎, 李志民 . 一种考虑电压稳定约束的风-燃互补发电系统容量规划与并网控制策略[J]. 电网技术, 2018,42(2):503-508.
	SUN Tiankui, LI Zhimin . A capacity planning and grid-connected control strategy for wind-gas coordinating generation system considering voltage stability[J]. Power System Technology, 2018,42(2):503-508.
[3]	包能胜, 徐军平, 倪维斗 . 以大型风电场为核心的多能源互补发电系统[J]. 中国能源, 2006,28(8):29-33.
	BAO Nengsheng, XU Junping, NI Weidou . Multi-energies hybrid power systems centered on large-scale wind farm[J]. Energy of China, 2006,28(8):29-33.
[4]	包能胜 . 风电-燃气轮机互补发电系统若干关键问题的研究[D]. 北京: 清华大学, 2007.
	BAO Nengsheng . Research on the several key issues of hybrid power system combining wind farm with small gas turbine power plants[D]. Beijing: Tsinghua University, 2007.
[5]	HITTINGER E, WHITACRE J F, APT J . Compensating for wind variability using co-located natural gas generation and energy storage[J]. Energy Systems, 2010,1(4):417-439. doi: 10.1007/s12667-010-0017-2 URL
[6]	邓晓洋 . 计及大规模风电的电力系统及综合能源系统概率能流研究[D]. 北京: 北京交通大学, 2018.
	DENG Xiaoyang . Probabilistic energy flow of power systems with large-scale wind power and integrated electricity and natural gas systems[D]. Beijing: Beijing Jiaotong University, 2018.
[7]	穆钢, 崔杨, 严干贵 . 确定风电场群功率汇聚外送输电容量的静态综合优化方法[J]. 中国电机工程学报, 2011,31(1):15-19.
	MU Gang, CUI Yang, YAN Gangui . A static optimization method to determine integrated power transmission capacity of clustering wind farms[J]. Proceedings of the CSEE, 2011,31(1):15-19.
[8]	杨林, 杨修宇, 涂娇娇 , 等. 风电出力特性及其对电力系统运行的影响分析[J]. 智能电网, 2014,2(5):18-22.
	YANG Lin, YANG Xiuyu, TU Jiaojiao , et al. Analysis of wind power output characteristics and its impact on operation of power system[J]. Smart Grid, 2014,2(5):18-22.
[9]	张宁, 康重庆, 周宇田 , 等. 风电与常规电源联合外送的受端可信容量研究[J]. 中国电机工程学报, 2012,32(10):72-79.
	ZHANG Ning, KANG Chongqing, ZHOU Yutian , et al. Evaluation of receiving-end credible capacity for joint delivery of wind power and conventional electricity generation[J]. Proceedings of the CSEE, 2012,32(10):72-79.
[10]	夏俊丽, 毛荀, 柯德平 , 等. 基于综合评价的交、直流输电适用范围研究[J]. 电力自动化设备, 2015,35(3):120-126.
	XIA Junli, MAO Xun, KE Deping , et al. Applicable scope of AC and DC power transmission based on comprehensive evaluation[J]. Electric Power Automation Equipment, 2015,35(3):120-126.
[11]	徐政, 程斌杰 . 不同电压等级直流输电的适用性研究[J]. 电力建设, 2015,36(9):22-29. doi: 10.3969/j.issn.1000-7229.2015.09.004 URL
	XU Zheng, CHENG Binjie . Applicability study on DC transmission with different voltage levels[J]. Electric Power Construction, 2015,36(9):22-29. doi: 10.3969/j.issn.1000-7229.2015.09.004 URL
[12]	耿庆申, 卢玉, 樊海荣 , 等. 特高压和超高压交流输电系统运行损耗比较分析[J]. 电力系统保护与控制, 2016,44(16):72-77.
	GENG Qingshen, LU Yu, FAN Hairong , et al. Comparative analysis of operation losses of UHV AC and EHV AC transmission systems[J]. Power System Protection and Control, 2016,44(16):72-77.
[13]	WANG S S, TANG G F, HE Z Y . Comprehensive evaluation of VSC-HVDC transmission based on improved analytic hierarchy process [C]//2008 Third International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. IEEE, 2008: 2207-2211.
[14]	朱天曈, 丁坚勇, 郑旭 . 基于改进TOPSIS法和德尔菲—熵权综合权重法的电网规划方案综合决策方法[J]. 电力系统保护与控制, 2018,46(12):91-99.
	ZHU Tiantong, DING Jianyong, ZHENG Xu . A comprehensive decision-making method for power network planning schemes based on the combination of the improved TOPSIS method with Delphi-entropy weight method[J]. Power System Protection and Control, 2018,46(12):91-99.
[15]	肖帅, 张岩, 章德 , 等. 风电群输电规划评价指标体系和方法[J]. 中国电力, 2019,52(10):115-122.
	XIAO Shuai, ZHANG Yan, ZHANG De , et al. Comprehensive evaluation indexes framework and evaluation method for transmission planning of wind power cluster[J]. Electric Power, 2019,52(10):115-122.
[16]	王佳, 金秀, 王旭 , 等. 基于前景理论的跨市场状态转移多阶段资产配置研究[J]. 中国管理科学, 2018,26(12):44-55.
	WANG Jia, JIN Xiu, WANG Xu , et al. Research on cross market regime switching multi-period asset allocation based on prospect theory[J]. Chinese Journal of Management Science, 2018,26(12):44-55.
[17]	高山, 王深哲, 李海峰 , 等. 基于前景理论的电网规划方案综合决策方法[J]. 电网技术, 2014,38(8):2029-2036. doi: 10.13335/j.1000-3673.pst.2014.08.002 URL
	GAO Shan, WANG Shenzhe, LI Haifeng , et al. Prospect theory based comprehensive decision-making method of power network planning schemes[J]. Power System Technology, 2014,38(8):2029-2036. doi: 10.13335/j.1000-3673.pst.2014.08.002 URL
[18]	熊一, 查晓明, 秦亮 , 等. 风电功率爬坡气象场景分类模型及阈值整定研究[J]. 电工技术学报, 2016,31(19):155-162.
	XIONG Yi, ZHA Xiaoming, QIN Liang , et al. Study on wind power ramping weather scenario classification model and threshold setting[J]. Transactions of China Electrotechnical Society, 2016,31(19):155-162.
[19]	崔杨, 赵玉, 邱丽君 , 等. 改善受端电网调峰裕度的特高压直流外送风火协调调度[J]. 电力系统自动化, 2018,42(15):126-132.
	CUI Yang, ZHAO Yu, QIU Lijun , et al. Coordinated scheduling method of wind-thermal power transmitted by UHVDC system for improving peak-shaving margin of receiving-end power grid[J]. Automation of Electric Power Systems, 2018,42(15):126-132.
[20]	安婷, 乐波, 杨鹏 , 等. 直流电网直流电压等级确定方法[J]. 中国电机工程学报, 2016,36(11):2871-2879. doi: 10.13334/j.0258-8013.pcsee.2016.11.002 URL
	AN Ting, YUE Bo, YANG Peng , et al. A determination method of DC voltage levels for DC grids[J]. Proceedings of the CSEE, 2016,36(11):2871-2879.
[21]	杜雄, 李高显, 李腾飞 , 等. 风电变流器IGBT模块的多时间尺度寿命评估[J]. 中国电机工程学报, 2015,35(23):6152-6161. doi: 10.13334/j.0258-8013.pcsee.2015.23.023 URL
	DU Xiong, LI Gaoxian, LI Tengfei , et al. Multi-time scale lifetime evaluation of IGBT modules in the wind power converter[J]. Proceedings of the CSEE, 2015,35(23):6152-6161.
[22]	汤广福 . 基于电压源换流器的高压直流输电技术[M]. 北京: 中国电力出版社, 2010.
[23]	孙腾飞, 程浩忠, 张立波 , 等. 可靠性与经济性协调的输电线路最大利用率评估方法[J]. 电力建设, 2016,37(6):70-78.
	SUN Tengfei, CHENG Haozhong, ZHANG Libo , et al. a load-carrying capacity evaluation method of transmission line based on economy and reliability coordination[J]. Electric Power Construction, 2016,37(6):70-78.
[24]	MARTIN David, CAINES Daniel, SAYEE Libo . EirGrid system performance data[EB/OL]. [ 2020- 01- 10]. http://www.eirgrid.com/operations/ststemperformancedata/.
[25]	LI G Q, ZHANG R F, JIANG T , et al. Optimal dispatch strategy for integrated energy systems with CCHP and wind power[J]. Applied Energy, 2017,192:408-419. doi: 10.1016/j.apenergy.2016.08.139 URL
[26]	曾建敏 . 实验检验累积前景理论[J]. 暨南大学学报(自然科学与医学版), 2007,28(1):44-47.
	ZENG Jianmin . An experimental test on cumulative prospect theory[J]. Journal of Jinan University(Natural Science & Medicine Edition), 2007,28(1):44-47.

基于统计分析的风-气能源基地远距离直流输电方案规划

Long Distance DC Transmission Scheme Planning for Energy Base of Wind-Gas Based on Statistical Analysis

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 15

参考文献 26

相关文章 6

编辑推荐

Metrics

本文评价

[1]	范志成，朱俊澎，吴涵，袁越. 计及模糊随机性的主动配电网分布式电源规划模型[J]. 电力建设, 2019, 40(2): 37-44.
[2]	李琳，徐晶，林威，穆云飞，靳小龙，贾宏杰，余晓丹，杜丽佳，原凯. 考虑随机性的区域综合能源系统多目标日前优化调度方法[J]. 电力建设, 2018, 39(12): 39-46.
[3]	王凯军，孙飞飞，兰洲，孙可. 全寿命周期成本管理在实际电力网络规划中的应用[J]. 电力建设, 2016, 37(4): 43-49.
[4]	张李明，齐先军. 考虑分布式电源随机性的配电网最大供电能力[J]. 电力建设, 2015, 36(11): 38-44.
[5]	张弓邱强华叶建云吴尧成孙伟军鲍庆. 舟山与大陆联网输电线路工程螺头水道大跨越架线施工方案[J]. 电力建设, 2011, 32(8): 116-121.
[6]	朱伟,周琪 . SPSS软件在工程质量控制中的应用[J]. 电力建设, 2003, 24(7): 61-0.