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"

1 VOROPAI N , PODKOVALNIKOV S , OSINTSEV K . From interconnections of local electric power systems to Global Energy Interconnection[J]. Global Energy Interconnection, 2018, 1 (1): 4- 10.
2 WU C , ZHANG X . Economic analysis of energy interconnection between Europe and China with 100% renewable energy generation[J]. Global Energy Interconnection, 2018, 1 (5): 528- 536.
3 王建学, 李清涛, 王秀丽, 等. 大规模新能源并网系统电源规划方法[J]. 中国电机工程学报, 2020, 40 (10): 3114- 3124.
WANG Jianxue , LI Qingtao , WANG Xiuli , et al. Generation planning method for large scale new energy grid connected system[J]. Chinese Journal of Electrical Engineering, 2020, 40 (10): 3114- 3124.
4 孙东磊, 李山, 李雪亮, 等. 适应源荷不确定性的参考电网区间优化方法[J]. 山东大学学报(工学版), 2017, 47 (6): 26- 31.
SUN Donglei , LI Shan , LI Xueliang , et al. Interval optimization method for reference network adaptable to the uncertainties of power sources and electric loads[J]. Journal of Shandong University(Engineering Science), 2017, 47 (6): 26- 31.
5 王俊, 蔡兴国, 季峰. 基于Copula理论的相关随机变量模拟方法[J]. 中国电机工程学报, 2013, 36 (22): 75- 82.
WANG Jun , CAI Xingguo , JI Feng . Simulation method of correlated random variables based on Copula theory[J]. Chinese Journal of Electrical Engineering, 2013, 36 (22): 75- 82.
6 王小红, 周步祥, 张乐, 等. 基于时变Copula函数的风电出力相关性分析[J]. 电力系统及其自动化学报, 2015, 27 (1): 43- 48.
doi: 10.3969/j.issn.1003-8930.2015.01.008
WANG Xiaohong , ZHOU Buxiang , ZHANG Le , et al. Correlation analysis of wind power output based on time-varying copula function[J]. Journal of Power System and Automation, 2015, 27 (1): 43- 48.
doi: 10.3969/j.issn.1003-8930.2015.01.008
7 叶林, 赵永宁. 基于空间相关性的风电功率预测研究综述[J]. 电力系统自动化, 2014, 38 (14): 126- 135.
doi: 10.7500/AEPS20130911004
YE Lin , ZHAO Yongning . A review of wind power prediction based on spatial correlation[J]. Power System Automation, 2014, 38 (14): 126- 135.
doi: 10.7500/AEPS20130911004
8 夏泠风, 黎嘉明, 赵亮, 等. 考虑光伏电站时空相关性的光伏出力序列生成方法[J]. 中国电机工程学报, 2017, 37 (7): 1982- 1992.
XIA Lingfeng , LI Jiaming , ZHAO Liang , et al. Generation method of photovoltaic output series considering spatiotemporal correlation of photovoltaic power stations[J]. Chinese Journal of Electrical Engineering, 2017, 37 (7): 1982- 1992.
9 高赐威, 吴天婴, 何叶, 等. 考虑风电接入的电源电网协调规划[J]. 电力系统自动化, 2012, 36 (22): 35- 40.
GAO Ciwei , WU Tianying , HE Ye , et al. Power grid coordination planning considering wind power access[J]. Power System Automation, 2012, 36 (22): 35- 40.
10 张节潭, 苗淼, 范宏, 等. 含风电场的双层电源规划[J]. 电网技术, 2011, 35 (11): 49- 55.
ZHANG Jietan , MIAO Miao , FAN Hong , et al. Two level power supply planning including wind farms[J]. Grid Technology, 2011, 35 (11): 49- 55.
11 张玥, 王秀丽, 曾平良, 等. 基于Copula理论考虑风电相关性的源网协调规划[J]. 电力系统自动化, 2017, 41 (9): 102- 108.
ZHANG Yue , WANG Xiuli , ZENG Pingliang , et al. Source network coordination planning considering wind power correlation based on Copula theory[J]. Power System Automation, 2017, 41 (9): 102- 108.
12 赵书强, 索璕, 马燕峰, 等. 基于复杂适应系统理论的可再生能源广域互补规划方法[J]. 电网技术, 2020, 44 (10): 3671- 3681.
ZHAO Shuqiang , SUO Li , MA Yanfeng , et al. A wide area complementary planning method for renewable energy based on complex adaptive system theory[J]. Power Grid Technology, 2020, 44 (10): 3671- 3681.
13 秦潇璘. 基于相关性分析的间歇性电源规划[D]. 北京: 华北电力大学, 2017.
QIN Xiaozhen. Intermittent power generation planning based on correlation analysis[D]. Beijing: North China Electric Power University, 2017.
14 张恒, 袁铁江, 车勇, 等. 兼顾新能源穿透功率和风险的风光火打捆外送电源规划[J]. 电力系统自动化, 2018, 42 (19): 71- 76.
doi: 10.7500/AEPS20170714001
ZHANG Heng , YUAN Tiejiang , CHE Yong , et al. Wind, wind and fire bundled power supply planning considering penetration power and risk of new energy[J]. Power System Automation, 2018, 42 (19): 71- 76.
doi: 10.7500/AEPS20170714001
15 ABDIN I F , ZIO E . An integrated framework for operational flexibility assessment in multi-period power system planning with renewable energy production[J]. Applied Energy, 2018, 222 (15): 898- 914.
16 KOLTSAKLIS N E , GEORGIADIS M C . A multi-period, multi-regional generation expansion planning model incorporating unit commitment constraints[J]. Applied Energy, 2015, 158 (15): 310- 331.
17 ZHANG Y , HE Y , WANG X , et al. Modeling of fast charging station equipped with energy storage[J]. Global Energy Interconnection, 2018, 1 (2): 145- 152.
18 NELSEN R B . An Introduction to Copulas[J]. Technometrics, 2000, 42 (3): 129- 185.
19 BRINKERINK M , DEANE P , COLLINS S , et al. Developing a global interconnected power system model[J]. Global Energy Interconnection, 2018, 1 (3): 330- 343.
20 丁明, 宋晓皖, 孙磊, 等. 考虑时空相关性的多风电场出力场景生成与评价方法[J]. 电力自动化设备, 2019, 39 (10): 39- 47.
DING Ming , SONG Xiaowan , SUN Lei , et al. Generation and evaluation method of multi wind farm output scenarios considering temporal and spatial correlation[J]. Power Automation Equipment, 2019, 39 (10): 39- 47.
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