Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (1): 111-119.doi: 10.6040/j.issn.1672-3961.0.2020.424

• Electrical Engineering • Previous Articles     Next Articles

Method for generation planning with the temporal and spatial correlation of wind and solar power

Wensheng LI1(),Xian WANG1,Yuanze MI2,Yongji CAO2,*(),Xiaoming LIU1,Hengxu ZHANG2,Zihan LIU2   

  1. 1. Economic and Technological Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, Shandong, China
    2. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, Shandong, China
  • Received:2020-10-19 Online:2022-02-21 Published:2022-02-21
  • Contact: Yongji CAO E-mail:liwensheng@jyy.sd.sgcc.cn;caoyongji1991@163.com

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Abstract:

A generation planning method considering multi-dimensional spatial-temporal correlation of wind and solar energy was proposed. Based on Copula theory and probability distribution of wind power and photovoltaic, a wind power and photovoltaic output model considering multi-dimensional spatial-temporal correlation of wind power and photovoltaic was established. The output scenario was applied to the bi-level generation planning model, and a generation planning method considering the multi-dimensional spatial-temporal correlation of wind power and photovoltaic was proposed. Among them, the upper level was the investment decision-making model with the minimum total planning cost as the objective function, and the lower level was the short-term operation optimization model with the optimal operation economy and peak shaving characteristics as the objective. The effectiveness of the proposed method was verified by a case study in a certain area, and the influence of the spatial-temporal correlation of wind power and photovoltaic on the generation planning scheme was compared and analyzed.

Key words: wind power, photovoltaic, spatial and temporal correlation, Copula theory, generation planning

CLC Number: 

  • TM715

Fig.1

Flowchart of model solving method"

Table 1

Various power supply parameters"

电源类型 现有装机
容量/MW		 投建成本/
(万元·MW-1)		 固定成本/
(万元·MW-1)
火电 51 968 430 15.03
风电 1 920 834 24.42
光伏 4 470 936 21.00
水电 200 450 14.09
储能 0 390 9.50

Table 2

Fitting results of Copula functions"

Copula函数 模型参数 欧式距离
Gaussian-Copula -0.051 0 0.348 0
t-Copula -0.063 8 0.360 4
Gumbel-Copula 1.013 1 0.724 7
Clayton-Copula 0.013 2 0.534 1
Frank-Copula -0.369 5 0.394 1

Fig.2

Comparison between original wind power output scenario and forecast scenario"

Fig.3

Comparison of photovoltaic original output scenario and forecast scenario"

Table 3

Correlation coefficient"

方法 风光出力
互相关系数		 风电出力12 h
自相关系数		 光伏出力12 h
自相关系数
原始出力 -0.385 1 0.339 0 0.320 1
考虑多维时空
相关性		 -0.341 3 0.313 6 0.332 6
仅考虑空间
相关性		 -0.325 4 0.002 4 0.125 4

Fig.4

Investment decision with 15% penetration of renewable energy"

Table 4

Comparison of generation planning schemes  MW"

方案 新建
水电		 新建
储能		 新建
火电		 新建
风光		 总装机
容量
考虑多
维时空
相关性		 6980 600 13 750 4900 84 788
仅考虑
空间相
关性		 6420 600 14 862 3680 84 210

Table 5

Comparison of generation planning cost"

方案 建造
费用/
亿元		 运行
费用/
亿元		 其他
费用/
亿元		 剩余
价值/
亿元		 总费用/
亿元
考虑多
维时空
相关性		 46.681 1 450.614 0.862 16.492 1 480.653
仅考虑
空间相
关性		 45.537 1 452.942 0.931 17.021 1 482.281

Fig.5

Short-term optimal scheduling considering multidi- mensional spatiotemporal correlation"

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