A Method for Estimating Available Power of Wind Farms by Considering the Power Generation Conditions and Station Losses

doi:10.12096/j.2096-4528.pgt.21135

Abstract

Abstract:

Optimization of direct-regulated wind power automatic generation control (AGC) system and grid online optimal dispatch system requires the establishment of an accurate wind farm available power estimation model. Based on the new energy stand-alone information management system, this paper put forward a method of accurate estimation of wind power plant considering power generation conditions and station losses. The Spearman correlation coefficient was used to determine the number of key historical moments in the estimation moment, and the theoretical power estimation model of wind turbines was established based on the long short-term memory (LSTM) network. The operating conditions of the wind turbines were subdivided into six types (named as waiting wind, power generation and outage etc.), and the equivalent circuit model of the loss in the wind farm was established. Finally, the actual data of a wind farm was used to perform simulation calculations. The calculation results show that the root mean square error is reduced by 40% when the theoretical power model of a single machine considers the historical wind speed;When the available power model of wind farm takes into account the power generation condition and in-station loss, the root square error is reduced by 76.9%. The available power estimation model for wind farms proposed in this paper will facilitate the optimization of online dispatching and direct-regulation wind power AGC system strategies, and improve the level of wind power consumption.

Key words: wind power, Spearman correlation coefficient, long short-term memory (LSTM) network, theoretical power, station loss, available power

CLC Number:

TK 81

Jian YANG, Yu LIU, Kunpeng HUANG, Yazhou LUO, Siqing NIU, Wei WANG, Jiafei HUAN, Lei ZHANG, Pei ZHANG, Huawei LI. A Method for Estimating Available Power of Wind Farms by Considering the Power Generation Conditions and Station Losses[J]. Power Generation Technology, 2023, 44(2): 235-243.

Figures/Tables 9

Fig. 1 Expanded view of LSTM unit in time

Fig. 2 LSTM unit structure

Tab. 1 Correlation coefficient between cabin wind speed at estimated time and cabin wind speed at historical time

估算时刻	历史时刻	相关系数
t时刻	t-5时刻	0.895 7
	t-4时刻	0.912 9
	t-3时刻	0.945 1
	t-2时刻	0.966 8
	t-1时刻	0.975 7

Tab.2 Hyperparameters of wind turbine theoretical generation power estimation model based on LSTM network

超参数	数值
输入层时间步数	5
输入层维数	1
隐藏层的数目	1
隐藏层维数	8
输出变量维数	1

Tab. 3 Errors in the test set of the theoretical power generation estimation model for wind turbines

模型	均方根误差/%	平均绝对误差/%
BP model-1	6.127	5.378
LSTM model-1	5.825	4.304
LSTM model-5	3.071	2.150

Fig. 3 Comparison of partial estimated power values and real power values in test set

Tab. 4 Model errors of wind farm available generation power estimation model

模型	均方根误差/%	平均绝对误差/%
1	18.077	16.663
2	5.708	4.590
3	2.850	1.888
4	2.877	2.511
5	1.320	1.091

Fig. 4 Comparison of estimated value and true value of wind farm available power generation

Fig. 5 Scatter diagram of loss change in the station

References 19

1	邬嘉雨，刘洋，许立雄，等．区块链技术下考虑风电不确定性的微网群鲁棒博弈交易模式[J]．电力建设，2021，42(9)：10-21． doi:10.12204/j.issn.1000-7229.2021.09.002
	WU J Y， LIU Y， XU L X，et al ．Robust game transaction model of multi-microgrid system applying blockchain technology considering wind power uncertainty[J]．Electric Power Construction，2021，42(9)：10-21． doi:10.12204/j.issn.1000-7229.2021.09.002
2	黄树帮，陈耀，金宇清．碳中和背景下多通道特征组合超短期风电功率预测[J]．发电技术，2021，42(1)：60-68． doi:10.12096/j.2096-4528.pgt.20103
	HUANG S B， CHEN Y， JIN Y Q ．A multi-channel feature combination model for ultra-short-term wind power prediction under carbon neutral background[J]．Power Generation Technology，2021，42(1)：60-68． doi:10.12096/j.2096-4528.pgt.20103
3	刘栋，魏霞，王维庆，等．基于SSA-ELM的短期风电功率预测[J]．智慧电力，2021，49(6)：53-59． doi:10.3969/j.issn.1673-7598.2021.06.009
	LIU D， WEI X， WANG W Q，et al ．Short-term wind power prediction based on SSA-ELM[J]．Smart Power， 2021，49(6)：53-59. doi:10.3969/j.issn.1673-7598.2021.06.009
4	李卫东，贺鸿鹏．考虑风电消纳的源-荷协同优化调度策略[J]．发电技术，2020，41(2)：126-130． doi:10.12096/j.2096-4528.pgt.18266
	LI W D， HE H P ．Source-load cooperative optimization dispatch strategy considering wind power accommodation[J]．Power Generation Technology，2020，41(2)：126-130． doi:10.12096/j.2096-4528.pgt.18266
5	朱丹丹，赵静波，李强，等．高载能负荷企业参与受阻风电消纳决策方法[J]．电力工程技术，2021，40(2)：121-127． doi:10.12158/j.2096-3203.2021.02.017
	ZHU D D， ZHAO J B， LI Q，et al ．Decision making method for energy-intensive enterprise in consuming curtailed wind power[J]．Electric Power Engineering Technology，2021，40(2)：121-127． doi:10.12158/j.2096-3203.2021.02.017
6	姜楠，戴赛，许丹，等．计及机组组合与线路重构协同的电热联合系统消纳弃风研究[J]．电力系统保护与控制，2022，50(14)：105-113．
	JIANG N， DAI S， XU D，et al ．Wind power consumption in a CHP system considering the collaboration of unit combination and line reconstruction[J]．Power System Protection and Control，2022，50(14)：105-113．
7	杨梓萤，姚李孝．梯级水库等效蓄能在风电消纳中的应用[J]．电网与清洁能源，2021，37(6)：134-138． doi:10.3969/j.issn.1674-3814.2021.06.017
	YANG Z Y， YAO L X ．Application of cascade reservoir equivalent energy storage in wind power accommodation[J]．Power System and Clean Energy，2021，37(6)：134-138． doi:10.3969/j.issn.1674-3814.2021.06.017
8	国家电网有限公司．风电理论功率及受阻电量计算方法： [S]．北京：中国电力出版社，2020．
	State Grid Corporation of China ． Calculation method of wind power theoretical power and blocked power： [S]．Beijing：China Electric Power Press，2020．
9	孙辉，徐箭，孙元章，等．基于混合整数线性规划的风电场有功优化调度[J]．电力系统自动化，2016，40(22)：27-33． doi:10.7500/AEPS20151214007
	SUN H， XU J， SUN Y Z，et al ．Wind farm active power optimal dispatch based on mixed integer linear programming[J]．Automation of Electric Power Systems， 2016， 40(22)： 27-33． doi:10.7500/AEPS20151214007
10	延平．风电场运行效能评价方法研究[D]．北京：华北电力大学，2017．
	YAN P ．Research on wind farm operation effectiveness evaluation method[D]．Beijing：North China Electric Power University，2017．
11	张华英. 风场特性机理分析及风电机组输出功率优化研究[D]．北京：华北电力大学，2019．
	ZHANG H Y ．Research on the mechanism analysis of wind field characteristics and the optimization of wind turbine output power[D]．Beijing：North China Electric Power University， 2019．
12	潘凯．含风电场电力系统协调优化调度及其评价[D]．武汉：华中科技大学，2014．
	PAN K ．Coordinated and optimized dispatch and evaluation of power system with wind farms[D]．Wuhan：Huazhong University of Science and Technology，2014．
13	国家能源局．风电场理论发电量与弃风电量评估导则 [S]．北京：中国电力出版社，2014． doi:10.3969/j.issn.1006-8910.2021.07.002
	National Energy Administration ． Guidelines for the evaluation of theoretical power generation and curtailment of wind farms [S]．Beijing：China Electric Power Press，2014． doi:10.3969/j.issn.1006-8910.2021.07.002
14	辛元庆，王国君，陈靖，等．风电场无功损耗特性及其补偿裕度分析研究[J]．青海电力，2020，39(1)：18-23．
	XIN Y Q， WANG G J， CHEN J，et al ．Research on the characteristics of reactive power loss and compensation margin of wind farms[J]．Qinghai Electric Power，2020，39(1)：18-23．
15	刘双，张建周，柏嵩．计及机组运行工况的风电场有功功率控制策略[J]．电气技术，2013(10)：15-18． doi:10.3969/j.issn.1673-3800.2013.10.004
	LIU S， ZHANG J Z， BAI S ．Wind farm active power control strategy considering unit operating conditions[J]．Electrical Technology，2013(10)：15-18． doi:10.3969/j.issn.1673-3800.2013.10.004
16	刘永前，邵振州，王铮，等．基于分段支持向量机的风电机组理论功率计算研究[J]．太阳能学报，2019，40(3)：673-680．
	LIU Y Q， SHAO Z Z， WANG Z，et al ．Research on theoretical power calculation of wind turbine based on segmented support vector machine[J]．Acta Solar Energy，2019，40(3)：673-680．
17	刘芳．基于改进BP神经网络的风电功率预测方法研究[D]．杭州：浙江大学，2020．
	LIU F ．Research on wind power prediction method based on improved BP neural network[D]．Hangzhou：Zhejiang University，2020．
18	刘军，汪继勇．基于风电机组健康状态的风电场功率分配研究[J]．电力系统保护与控制，2020，48(20)：106-113． doi:10.19783/j.cnki.pspc.191489
	LIU J， WANG J Y ．Research on wind farm power distribution based on the health status of wind turbines[J]．Power System Protection and Control，2020，48(20)： 106-113． doi:10.19783/j.cnki.pspc.191489
19	GRANGER C， LIN J ．Using the mutual information coefficient to identify lags in nonlinear models[J]．Journal of Time Series Analysis，2008，15(4)：371-384．

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