您的位置：山东大学 -> 科技期刊社 -> 《山东大学学报（工学版）》

山东大学学报 (工学版) ›› 2019, Vol. 49 ›› Issue (5): 44-51.doi: 10.6040/j.issn.1672-3961.0.2019.162

• 电气工程———人工智能应用专题 • 上一篇    下一篇

并网型风电场扩展光伏互补发电容量优化配置

杨冬1(),王世文2,王勇3,陈博1,郑天茹1,周宁1,肖天4,*(),赵雅文4   

  1. 1. 国网山东省电力公司电力科学研究院, 山东 济南 250003
    2. 国网山东省电力公司威海供电公司, 山东 威海 264200
    3. 国网山东省电力公司, 山东 济南 250001
    4. 电网智能化调度与控制教育部重点实验室(山东大学), 山东 济南 250061
  • 收稿日期:2019-04-15 出版日期:2019-10-20 发布日期:2019-10-18
  • 通讯作者: 肖天 E-mail:yangdong_epri@163.com;xt.icey@qq.com
  • 作者简介:杨冬(1984—)，男，山东日照人，高级工程师，博士，主要研究方向为电力系统运行与控制.E-mail:yangdong_epri@163.com
  • 基金资助:
    国网山东省电力公司科技项目(2018A-101)

Optimal complementary photovoltaic capacity configuration for grid-connected wind farms expansion

Dong YANG1(),Shiwen WANG2,Yong WANG3,Bo CHEN1,Tianru ZHENG1,Ning ZHOU1,Tian XIAO4,*(),Yawen ZHAO4   

  1. 1. State Grid Shandong Electric Power Research Institute, Jinan 250003, Shandong, China
    2. Weihai Power Supply Company, State Grid Shandong Electric Power Company, Weihai 264200, Shandong, China
    3. State Grid Shandong Electric Power Company, Jinan 250001, Shandong, China
    4. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, Shandong, China
  • Received:2019-04-15 Online:2019-10-20 Published:2019-10-18
  • Contact: Tian XIAO E-mail:yangdong_epri@163.com;xt.icey@qq.com
  • Supported by:
    国网山东省电力公司科技项目(2018A-101)

RichHTML

13

PDF (PC)

585

摘要：

风、光资源具有天然的互补性,在已建成的风电场中扩展光伏发电,组建互补发电系统,有利于平抑出力波动并提高运行经济性。提出一种并网型风电场扩展光伏容量优化配置方法。基于风速和太阳辐射在时间尺度上的互补性,建立修正的气象概率分布模型。以电气设备利用率最大,输出功率波动性和弃风、弃光电量最小为目标构建多目标优化模型,充分考虑3个目标之间的矛盾以及不同升压主变容量的影响。采用蒙特卡罗方法仿真生成风、光气象数据,基于多目标粒子群算法求取Pareto最优解集合,结合工程要求和经济性指标从集合中确定最终方案。结合算例,对本研究方法的有效性进行了验证。

关键词: 风光互补, 多目标优化, 容量配置, 概率模型

Abstract:

According to the complementarity of wind and solar energy sources, expanding photovoltaic panels in wind farms into wind-PV hybrid generation systems was helpful to smooth power fluctuation and improve operation economy. An approach to solve the optimization of the PV capacity for grid-connected wind farm was presented. Based on the complementarity of wind speed and solar radiation in time scales, modified meteorological probability models were established. A multi-objective optimization model was proposed with three objectives: maximizing the utilization of electrical equipment, minimizing the power fluctuation and the loss of renewable energy. The contradiction of three objectives and influence of the step-up transformer were incorporated. Meteorological data were simulated based on the Monte Carlo method. And the multi-objective particle swarm optimization was used to search the Pareto optimal solution set, from which an ultimate planning scheme was selected considering the engineering requirements and economic index. A numerical example was provided to validate the effectiveness of proposed approach.

Key words: wind-PV hybrid, multi-objective optimization, capacity configuration, probability model

中图分类号: 

  • TM61

图1

风光互补发电系统示意图"

图2

MOPSO执行流程图"

表1

风力发电机组主要参数"

风机型号 轮毂高度/m 额定功率/kW 切入风速/(m·s-1) 额定风速/(m·s-1) 切出风速/(m·s-1)
Gamesa G58-850 65 850 3.0 13.5 25.0

图3

研究地区气象数据曲线"

表2

修正气象模型与传统气象模型仿真性能对比"

气象模型 风速跟踪率rw/% 风光互补模式跟踪率rp/%
改进气象模型 58.61 73.61
传统气象模型 49.44 48.89

图4

场景1 Pt=50 MW时的Pareto前端"

图5

场景2 Pt=100 MVA时的Pareto前端"

表3

待选方案"

方案编号 光伏安装面积/hm2 是否扩展升压主变 输出功率变异系数Cv/% 升压主变利用率Ru/% 清洁能源浪费率Rl/% 经济性指标kb/%
S1 38.25 否 79.73 37.47 2.31 83.12
S2 36.42 否 79.81 36.80 2.12 85.17
S3 37.05 否 79.77 37.04 2.18 84.44
S4 37.94 否 79.74 37.36 2.28 83.45
S5 34.47 否 79.85 36.08 1.93 87.59

表4

不同发电系统间性能指标比较"

系统类型 风电装机容量/MW 风力发电机台数/台 光伏装机容量/MW 光伏安装面积/hm2 光伏电池数量/片 总装机容量/MW 年输出电量/(kWh) 输出功率变异系数Cv/% 升压主变利用率Ru/% 清洁能源浪费率Rl/% 年售电收益/万元
风光互补发电系统 49.30 58 53.70 34.47 210 577 103.0 158 026 043 79.95 36.08 1.93 118.650
风电场 103.7 122 0 0 0 103.7 174 648 611 87.91 39.87 15.64 104.789

图6

提出方法与NSGA-Ⅱ在场景1下的Pareto前端对比"

图7

提出方法与NSGA-Ⅱ在场景2下的Pareto前端对比"

1 王飞, 宋士瞻, 曹永吉, 等. 基于连续小波变换的风光发电资源多尺度评估[J]. 山东大学学报(工学版), 2018, 48 (5): 124- 130.
WANG Fei , SONG Shizhan , CAO Yongji , et al. Multi-scale assessment of wind-solar generation resources based on continuous wavelet transform[J]. Journal of Shandong University (Engineering Science), 2018, 48 (5): 124- 130.
2 KATSIGIANNIS Y A , GEORGILAKIS P S , KARAPIDAKIS E S . Hybrid simulated annealing-tabu search method for optimal sizing of autonomous power systems with renewables[J]. IEEE Transactions on Sustainable Energy, 2012, 3 (3): 330- 338.
3 ZHANG H X , CAO Y J , ZHANG Y , et al. Quantitative synergy assessment of regional wind-solar energy resources based on MERRA reanalysis data[J]. Applied Energy, 2018, 216, 172- 182.
doi: 10.1016/j.apenergy.2018.02.094
4 CHENG L , LIU M , SUN Y , et al. A multi-state model for wind farms considering operational outage probability[J]. Journal of Modern Power Systems and Clean Energy, 2013, 1 (2): 177- 185.
5 LIU C , CHAU K T , ZHANG X . An efficient wind-photovoltaic hybrid generation system using doubly excited permanent-magnet brushless machine[J]. IEEE Transactions on Industrial Electronics, 2010, 57 (3): 831- 839.
doi: 10.1109/TIE.2009.2022511
6 AL-MASRI H M , EHSANI M . Feasibility investigation of a hybrid on-grid wind photovoltaic retrofitting system[J]. IEEE Transactions on Industry Applications, 2016, 52 (3): 1979- 1988.
doi: 10.1109/TIA.2015.2513385
7 XU L , RUAN X , MAO C , et al. An Improved optimal sizing method for wind-solar-battery hybrid power system[J]. IEEE Transactions on Sustainable Energy, 2013, 4 (3): 774- 785.
doi: 10.1109/TSTE.2012.2228509
8 HONG Y Y , LIAN R C . Optimal sizing of hybrid Wind/PV/Diesel Generation in a Stand-Alone Power System Using Markov-Based Genetic Algorithm[J]. IEEE Transactions on Power Delivery, 2012, 27 (2): 640- 647.
doi: 10.1109/TPWRD.2011.2177102
9 BANSAL A K , KUMAR R , GUPTA R A . Economic analysis and power management of a small autonomous hybrid power system (SAHPS) using biogeography based optimization (BBO) algorithm[J]. IEEE Transactions on Smart Grid, 2013, 4 (1): 638- 648.
10 KATSIGIANNIS Y A , GEORGILAKIS P S , KARAPIDAKIS E S . Multiobjective genetic algorithm solution to the optimum economic and environmental performance problem of small autonomous hybrid power systems with renewables[J]. IET Renewable Power Generation, 2010, 4 (5): 404- 419.
doi: 10.1049/iet-rpg.2009.0076
11 YUAN Y , CAO Y , ZHANG X , et al. Optimal proportion of wind and PV capacity in provincial power systems based on bilevel optimization algorithm under low-carbon economy[J]. Journal of Modern Power Systems and Clean Energy, 2015, 3 (1): 33- 40.
12 ABBES D , MARTINEZ A , CHAMPENOIS G . Eco-design optimisation of an autonomous hybrid wind-photovoltaic system with battery storage[J]. IET Renewable Power Generation, 2012, 6 (5): 358- 371.
doi: 10.1049/iet-rpg.2011.0204
13 ZHU Y, JIANG P, YANG S. An optimal capacity allocation scheme for the wind-PV hybrid power system based on probabilistic production simulation[C]//Energy Conversion (CENCON), 2014 IEEE Conference on. Johor Bahru, Malaysia: IEEE, 2014: 277-282.
14 叶承晋, 黄民翔, 王焱, 等. 基于离散概率模型的风光互补供电系统优化配置[J]. 电力系统自动化, 2013, (6): 48- 54.
YE Chengjin , HUANG Minxiang , WANG Yan , et al. A hybrid wind/photovoltaic power supply system based on discrete probabilistic methodology[J]. Automation of Electric Power Systems, 2013, (6): 48- 54.
15 JANGAMSHETTI S H , RAN V G . Optimum siting of wind turbine generators[J]. IEEE Transactions on Energy Conversion, 2001, 16 (1): 8- 13.
16 ZOU K , AGALGAONKAR A P , MUTTAQI K M , et al. Distribution system planning with incorporating DG reactive capability and system uncertainties[J]. IEEE Transactions on Sustainable Energy, 2012, 3 (1): 112- 123.
doi: 10.1109/TSTE.2011.2166281
17 BOROWY B S , SALAMEH Z M . Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system[J]. IEEE Transactions on Energy Conversion, 1996, 11 (2): 367- 375.
18 WANG L , SINGH C . Multi-criteria Design of hybrid power generation systems based on a modified particle swarm optimization algorithm[J]. IEEE Transactions on Energy Conversion, 2009, 24 (1): 163- 172.
19 成驰, 许杨, 杨宏青. 并网风光互补资源评价与系统容量优化配置[J]. 水电能源科学, 2014, (6): 193- 196.
CHENG Chi , XU Yang , YANG Hongqing . Resources evaluation of grid hybrid wind/pv power complementary and optimization allocation of system capacity[J]. Water Resources and Power, 2014, (6): 193- 196.
20 HAZRA J , SINHA A K . Congestion management using multi-objective particle swarm optimization[J]. IEEE Transactions on Power Systems, 2007, 22 (4): 1726- 1734.
[1] 邵孟伟,袁世飞,周宏志,王乃华. 基于BP神经网络和遗传算法的翅片管结构优化[J]. 山东大学学报 (工学版), 2025, 55(6): 76-82.
[2] 赵康,田浩,马欢,杨冬. 基于复杂网络理论的多直流馈入受端电网优化分区方法[J]. 山东大学学报 (工学版), 2022, 52(1): 128-134.
[3] 武慧虹,钱淑渠,刘衍民,徐国峰,郭本华. 精英克隆局部搜索的多目标动态环境经济调度差分进化算法[J]. 山东大学学报 (工学版), 2021, 51(1): 11-23.
[4] 孙润稼,朱海南,刘玉田. 基于偏好多目标优化和遗传算法的输电网架重构[J]. 山东大学学报 (工学版), 2019, 49(5): 17-23.
[5] 钱淑渠,武慧虹,徐国峰,金晶亮. 计及排放的动态经济调度免疫克隆演化算法[J]. 山东大学学报(工学版), 2018, 48(4): 1-9.
[6] 王飞,徐健,李伟,汪新浩,施啸寒. 基于分布式储能系统的风储滚动优化调度方法[J]. 山东大学学报(工学版), 2017, 47(6): 89-94.
[7] 裴小兵,陈慧芬,张百栈,陈孟辉. 改善式BVEDA求解多目标调度问题[J]. 山东大学学报(工学版), 2017, 47(4): 25-30.
[8] 邓冠龙,杨洪勇,张淑宁,顾幸生. 零等待flow shop多目标调度的混合差分进化算法[J]. 山东大学学报(工学版), 2016, 46(5): 21-28.
[9] 刘淳安. 基于核分布估计的动态多目标优化进化算法[J]. 山东大学学报(工学版), 2011, 41(1): 167-172.
[10] 曲延鹏 陈颂英 杨新振 解富超 李文峰 宋秀琴. 低比转速离心泵叶轮几何参数多目标优化[J]. 山东大学学报(工学版), 2009, 39(3): 103-105.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 王素玉,艾兴,赵军,李作丽,刘增文 . 高速立铣3Cr2Mo模具钢切削力建模及预测[J]. 山东大学学报(工学版), 2006, 36(1): 1 -5 .
[2] 李 侃 . 嵌入式相贯线焊接控制系统开发与实现[J]. 山东大学学报(工学版), 2008, 38(4): 37 -41 .
[3] 孔祥臻,刘延俊,王勇,赵秀华 . 气动比例阀的死区补偿与仿真[J]. 山东大学学报(工学版), 2006, 36(1): 99 -102 .
[4] 陈瑞,李红伟,田靖. 磁极数对径向磁轴承承载力的影响[J]. 山东大学学报(工学版), 2018, 48(2): 81 -85 .
[5] 李可,刘常春,李同磊 . 一种改进的最大互信息医学图像配准算法[J]. 山东大学学报(工学版), 2006, 36(2): 107 -110 .
[6] 季涛,高旭,孙同景,薛永端,徐丙垠 . 铁路10 kV自闭/贯通线路故障行波特征分析[J]. 山东大学学报(工学版), 2006, 36(2): 111 -116 .
[7] 浦剑1 ,张军平1 ，黄华2 . 超分辨率算法研究综述[J]. 山东大学学报(工学版), 2009, 39(1): 27 -32 .
[8] 王丽君,黄奇成,王兆旭 . 敏感性问题中的均方误差与模型比较[J]. 山东大学学报(工学版), 2006, 36(6): 51 -56 .
[9] 孙殿柱,朱昌志,李延瑞 . 散乱点云边界特征快速提取算法[J]. 山东大学学报(工学版), 2009, 39(1): 84 -86 .
[10] 赵然杭,陈守煜 . 水资源数量与质量联合评价理论模型研究[J]. 山东大学学报(工学版), 2006, 36(3): 46 -50 .
版权所有 © 2018 《山东大学学报(工学版)》编辑部　
地址: 济南市山大南路27号（250100）　 电话: 0531-88366735　 E-mail: xbgxb@sdu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发