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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)

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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"

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