考虑风电消纳及潮流均衡度的分散式风电选址定容

doi:10.3969/j.issn.1000－7229.2018.01.013

摘要/Abstract

摘要： 摘要：结合我国当前严峻的弃风形势和分散式风电场良好的发展趋势，提出了一种分散式风电场多目标规划方法。引入潮流熵描述系统潮流均衡度，以系统整体运行成本和潮流熵最小作为目标函数。采用拉丁超立方抽样(Latin hypercube sampling，LHS)处理风速、负荷的不确定性和相关性，在建立传统电力系统静态约束模型的同时，对弃风率及限电次数进行机会约束。将智能优化算法应用到风电场的选址定容中，并研究了不同因素对规划结果的影响。IEEE 9节点系统算例表明：所提方法能够兼顾经济效益、风电利用率，同时使电网具备对负荷变化更强的适应性，结果同时体现了常规机组爬坡能力和风速相关性对风电规划容量的重要影响。

关键词:

关键词：风电消纳, 潮流熵, 拉丁超立方抽样, 机会约束, 分散式风电, 选址定容

Abstract: ABSTRACT： Based on the severe wind curtailment situation and promising development trend of dispersed wind farm, this paper proposes a multi-objective dispersed wind farm planning method. The power flow entropy is introduced to describe the balance degree of system flow, and the planning objective is to minimize both the overall operation cost and power flow entropy. This paper applies Latin hypercube sampling (LHS) to deal with the uncertainty and correlation of wind speed and load, and uses the chance constrained programming to restrict the curtailment rate and power rationing after establishing the static constraint model of traditional power system. Besides, an intelligent optimization algorithm is employed in wind farm locating and sizing, the influence of different factors on planning result is studied as well. The simulation of IEEE 9 system shows that the proposed method can guarantee the economic benefit and wind power utilization, and makes the power grid more adaptable to load changes. The result also indicates the great impact of unit climbing and wind speed correlation on the planning capacity of wind power.

Key words:

KEYWORDS： wind power consumption, power flow entropy, Latin hypercube sampling (LHS), chance-constrained programming, dispersed wind farm, locating and sizing

中图分类号:

TM 715

许沛东，方华亮，黄烁. 考虑风电消纳及潮流均衡度的分散式风电选址定容[J]. 电力建设, 2018, 39(1): 99-.

XU Peidong，FANG Hualiang，HUANG Shuo. Dispersed Wind Farm Locating and Sizing Considering Wind Power Consumption and Power Flow Balance[J]. Electric Power Construction, 2018, 39(1): 99-.

参考文献

［1］国家发展和改革委员会新能源研究所.中国2050高比例可再生能源发展情景暨路径研究［R/OL］.(2015-11-02)［2017-07-21］.https://wenku.baidu.com/view/33339597763231126fdb1199.html.

［2］中国产业信息网. 2016年中国风电装机容量、各区域风电新增装机容量占比及风电制造商新增装机容量前十名分析［EB/OL］. (2017-03-01) ［2017-07-21］.http://www.chyxx.com/industry/201703/499478.html.

［3］中国能源报.“弃风“怪圈如何破［EB/OL］. (2017-06-21) ［2017-07-21］.http://news.byf.com/hangye/189767.html.

［4］北极星风力发电网.分散式风电开发可缓解弃风限电［EB/OL］. (2017-07-05) ［2017-07-21］.http://news.bjx.com.cn/html/20170705/835299.shtml.

［5］甘磊, 吴臻, 谷纪亭,等. 风电集群接入系统规划的混合整数线性模型［J］. 电工技术学报, 2017, 32(5):165-175.

GAN Lei, WU Zhen, GU Jiting, et al. A MILP model for grid-connection transmission system planning of wind farm clusters［J］. Transactions of China Electrotechnical Society, 2017, 32(5):165-175.

［6］彭超锋, 袁铁江, 魏喜慧. 基于机会约束规划的分散式风电场优化规划［J］. 可再生能源, 2016, 34(3):427-436.

PENG Chaofeng, YUAN Tiejiang, WEI Xihui. Dispersed wind farm optimal planning based on chance constrained programming［J］. Renewable Energy Resources, 2016, 34(3):427-436.

［7］杨俊友, 崔嘉, 邢作霞,等. 考虑风电功率预测的分散式风电场无功控制策略［J］. 电力系统自动化, 2015, 39(13):8-15.

YANG Junyou, CUI Jia, XING Zuoxia, et al.Reactive power control strategy for dispersed wind farm considering wind power forecasting［J］. Automation of Electric Power Systems, 2015, 39(13):8-15.

［8］张沈习,程浩忠, 邢海军,等. 配电网中考虑不确定性的分布式电源规划研究综述［J］. 电力自动化设备, 2016, 36(8):1-9.

ZHANG Shenxi, CHENG Haozhong, XING Haijun, et al. Review of DG planning considering uncertainties for distribution network［J］. Electric Power Automation Equipment, 2016, 36(8):1-9.

［9］张沈习,李珂, 程浩忠,等. 考虑相关性的间歇性分布式电源选址定容规划［J］. 电力系统自动化, 2015, 39(8):53-58，140.

ZHANG Shenxi, LI Ke, CHENG Haozhong, et al. Optimal siting and sizing of intermittent distributed generator considering correlations［J］. Automation of Electric Power Systems, 2015, 39(8):53-58, 140.

［10］吕冰, 颜伟, 赵霞,等. 考虑能量随机性的分布式风电和联络线协调规划［J］. 中国电机工程学报, 2013, 33(34):145-152.

LYU Bing, YAN Wei, ZHAO Xia, et al.Coordinated allocation of tie lines and DWGs considering random energy［J］. Proceedings of the CESS, 2013, 33(34):145-152.

［11］ ZHANG J, FAN H, TANG W, et al. Planning for distributed wind generation under active management mode［J］. International Journal of Electrical Power & Energy Systems, 2013, 47: 140-146.

［12］赵冬梅, 尹颢涵, 王建锋. 间歇性能源出力特性综合分析体系及其应用［J］. 南方电网技术, 2015, 9(5):7-14.

ZHAO Dongmei, YIN Haohan, WANG Jianfeng. Comprehensive analysis system of intermittent energy output characteristics and its application［J］. Southern Power System Technology, 2015, 9(5):7-14.

［13］张新松, 袁越, 陈哲,等. 考虑电能质量约束的含风电场电网规划［J］. 电网技术, 2012, 36(6):195-199.

ZHANG Xinsong, YUAN Yue, CHEN Zhe, et al.Planning of power grid containing wind farm considering power quality constaint［J］. Power System Technology, 2012, 36(6):195-199.

［14］秦文萍, 任琛, 韩肖清, 等. 考虑负荷波动极限的电力系统电压稳定性风险评估［J］. 中国电机工程学报, 2015, 35(16):4102-4111.

QIN Wenping, REN Chen, HAN Xiaoqing, et al. Power system voltage stability risk assessment considering the limit of load fluctuation［J］. Proceedings of the CSEE, 2015, 35(16):4102-4111.

［15］张沈习,程浩忠, 李珂. 考虑相关性的风力发电机组多阶段选址定容规划［J］. 电网技术, 2014, 38(1):53-59.

ZHANG Shenxi, CHENG Haozhong, LI Ke. Multistage planning for wind turbine generator considering correlations［J］. Power System Technology, 2014, 38(1):53-59.

［16］孙鹏, 罗明武, 孙朝霞, 等. 采用改进杜鹃搜索算法的主动配电网双层分布式风电规划方法［J］. 电网技术, 2016, 40(9): 2743-2749.

SUN Peng, LUO Mingwu, SUN Zhaoxia, et al.An improved approach based on cuckoo search algorithm for bi-level distributed wind generation planning in active distribution network［J］. Power System Technology, 2016, 40(9):2743-2749.

［17］ZHANG Y, DONG Z Y, LUO F, et al. Optimal allocation of battery energy storage systems in distribution networks with high wind power penetration［J］. IET Renewable Power Generation, 2016, 10(8): 1105-1113.

［18］曹一家, 王光增, 曹丽华, 等. 基于潮流熵的复杂电网自组织临界态判断模型［J］. 电力系统自动化, 2011, 35(7):1-6.

CAO Yijia, WANG Guangzeng, CAO Lihua, et al. An identification model for self-organized criticality of power grids based on power flow entropy［J］. Automation of Electric Power Systems, 2011, 35(7):1-6.

［19］吴耀武, 蔡芝菁, 娄素华,等. 考虑电网负载均衡度的输电网规划模型［J］. 华中科技大学学报(自然科学版), 2013, 41(12):96-100.

WU Yaowu, CAI Zhijing, LOU Suhua, et al.Transmission network expansion planning model considering network load balance［J］. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2013, 41(12):96-100.

［20］YANG X S. Flower pollination algorithm for global optimization［C］// International Conference on Unconventional Computation and Natural Computation. Orleans, France ：Springer, 2012:240-249.

[1]	张浩禹，邱晓燕，周晟锐，赵有林，李凌昊，张楷 . 基于机会约束目标规划的多电-气互联综合能源系统分布式优化模型[J]. 电力建设, 2020, 41(7): 82-91.
[2]	王李龑，许强，黄开艺，吴健，艾芊，张亚新，贾轩. 计及新能源及负荷不确定性的多能微网系统两阶段随机规划方法[J]. 电力建设, 2020, 41(4): 100-108.
[3]	朱文广，张长生，熊宁，路志英，张华，肖园，熊云. 考虑综合能源站用电需求不确定性的配电网变电站机会约束规划[J]. 电力建设, 2019, 40(8): 19-25.
[4]	傅守强，陈翔宇，王畅，杨林. 基于机会约束规划的多PET互联的交直流配电网日前优化调度[J]. 电力建设, 2019, 40(12): 96-103.
[5]	刘伟佳，文福拴，马莉，薛松. 计及电动汽车和可控负荷的需求侧能源交易机制[J]. 电力建设, 2019, 40(11): 24-30.
[6]	程瑜，王望. 产消型智能社区储能系统配置与运行联合优化[J]. 电力建设, 2019, 40(10): 118-125.
[7]	杨智宇，刘俊勇，刘友波，温丽丽 . 基于时序运行数据的输电网线路重要度评估[J]. 电力建设, 2019, 40(1): 77-85.
[8]	黄裕春，王宏，王珂，文福拴. 考虑电动汽车充电设施接入的居民配电系统供电容量最优规划[J]. 电力建设, 2018, 39(6): 1-.
[9]	李刚, 孟亚云，董王英，王天军3. 基于协同理论的信息物理能源系统多流建模与分析 [J]. 电力建设, 2018, 39(5): 1-.
[10]	张岩, 文福拴, 熊文, 牛振勇, 赵宏伟, 尚慧玉. 基于广域测量信息的电力系统故障诊断解析模型 [J]. 电力建设, 2018, 39(5): 10-.
[11]	傅守强1，高杨1，陈翔宇1，李红建1，祁欢欢2，徐彭亮3，许文秀1 . 基于柔性变电站的交直流配电网技术研究与工程实践 [J]. 电力建设, 2018, 39(5): 46-.
[12]	王聪1，徐晓贤1，沙广林2，段青2，郭靖3，张岚1 . 计及灵活配电单元的交直流混合配电系统潮流计算方法 [J]. 电力建设, 2018, 39(5): 56-.
[13]	李军徽1，张嘉辉1，胡达珵1，李建林2，马会萌2. 多属性多目标储能系统工况适用性对比分析方法[J]. 电力建设, 2018, 39(4): 2-.
[14]	曹弘飞，朱新坚. BP神经网络在线优化卡尔曼滤波算法在钒电池SOC估算中的应用[J]. 电力建设, 2018, 39(4): 9-.
[15]	刘舒1，张宇1，薛花2，王育飞2，何阳2，李杨3. 基于Laguerre-NAR双谱估计的储能系统工况识别方法[J]. 电力建设, 2018, 39(4): 15-.