Journal of Shandong University(Engineering Science) ›› 2019, Vol. 49 ›› Issue (5): 44-51.doi: 10.6040/j.issn.1672-3961.0.2019.162

• Engineering—Special Topic on Artificial Intelligence Application • Previous Articles     Next Articles

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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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

CLC Number: 

  • TM61

Fig.1

Schematic diagram of a wind-PV hybrid system"

Fig.2

Flow chart of MOPSO"

Table 1

Parameters of WTG"

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

Fig.3

Meteorological data curves of study area"

Table 2

Comparison between the modified and traditionalmeteorological model"

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

Fig.4

The Pareto front when Pt=50 MVA of scenario 1"

Fig.5

Pareto front when Pt=100 MVA of scenario 2"

Table 3

The candidate solutions"

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

Table 4

Comparison of indexes of different generation systems"

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

Fig.6

Pareto fronts comparison of the proposed method withthe NSGA-Ⅱ in scenario 1"

Fig.7

Pareto fronts comparison of the proposed methodwith the NSGA-Ⅱ in scenario 2"

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