针对高比例光伏接入电网时,光伏出力的波动性会严重影响电力系统稳定运行的问题提出一种基于平均影响值与改进粒子群优化神经网络的组合式光伏出力短期预测模型。首先,采用直接预测法,选取总辐射量、直接辐射量、散射量、相对湿度、气温、风速和降雨量7个影响光伏出力的因素,构建MIV-PSO-BPNN模型,基于Rapid Miner数据挖掘得出降雨量对光伏出力平均影响值为0.0099,影响较小,不作为模型输入变量。然后,用改进PSO优化算法对BPNN的权值与阈值进行优化。最后,利用上海浦东国际机场T2-2光伏电站数据进行验证,结果表明MIV-PSO-BPNN模型对光伏出力预测有效,在实际中有一定应用价值。
Abstract
Aiming at the fluctuation of photovoltaic output will seriously affect the stable operation of power system when a high proportion of PV is connected to the power grid, a combined photovoltaic output short-term prediction model based on mean impact value and improved particle swarm optimization neural network was proposed. Firstly, the MIV-PSO-BPNN model was developed by selecting the total radiation, direct radiation, scattering radiation, relative humidity, temperature, wind speed and rainfall, as the main factors affecting on the PV power generation. Based on the Rapid Miner data mining, the mean impact value of rainfall on PV output is 0.0099, the influence is small and is not considered as the model input variables. Then, the improved particle swarm optimization （PSO） was applied to optimize the weights and thresholds of BP neural networks. Finally, the data of photovoltaic power station of Shanghai T2-2 Pudong International Airport was used for verification. The comparison results show that the MIV-PSO-BP neural network model can used effectively for PV power generation prediction, and has application value in practice.
关键词
光伏发电 /
数据挖掘 /
粒子群优化算法 /
神经网络 /
平均影响值 /
权值与阈值 /
短期预测
Key words
PVpower /
data mining /
particle swarm optimization /
neural networks /
mean impact value /
weight and threshold /
short-term forecasting
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参考文献
[1] LI L L, WEN S Y, TSENG M L, et al.Renewable energy prediction: A novel short-term prediction model of photovoltaic output power[J]. Journal of cleaner production, 2019, 228: 359-375.
[2] 陈志宝, 丁杰, 周海, 等. 地基云图结合径向基函数人工神经网络的光伏功率超短期预测模型[J]. 中国电机工程学报, 2015, 35(3):561-567.
CHEN Z B, DING J, ZHOU H, et al.A model of very short-term photovoltaic power forecasting based on ground-based cloud images and RBF neural network[J]. Proceedings of the CSEE, 2015, 35(3): 561-567.
[3] ZHONG J Q, LIU L Y, SUN Q, et al.Prediction of photovoltaic power generation based on general regression and back propagation neural network[J]. Energy procedia, 2018, 152: 1224-1229.
[4] YANG D Z, DONG Z B.Operational photovoltaics power forecasting using seasonal time series ensemble[J]. Solar energy, 2018, 166: 529-541.
[5] LIU L Y, LIU D R, SUN Q, et al.Forecasting power output of photovoltaic system using a bp network method[J]. Energy procedia, 2017, 142: 780-786.
[6] 葛乐, 陆文伟, 袁晓冬, 等. 基于改进相似日和ABC-SVM的光伏电站功率预测[J]. 太阳能学报, 2018, 39(3): 775-782.
GE L, LU W W, YUAN X D, et al.Power forecasting of photovoltaic plant based on improved similar day and ABC-SVM[J]. Acta energiae solaris sinica, 2018, 39(3): 775-782.
[7] WILLIAM V D, ELMIRAL J, GOKUL S T, et al.Short-term PV power forecasting using hybrid GASVM technique[J]. Renewable energy, 2019,140: 367-379.
[8] 冬雷, 周晓, 郝颖, 等. 基于样本双重筛选的光伏发电功率预测[J]. 太阳能学报, 2018, 39(4): 1018-1025.
DONG L, ZHOU X, HAO Y, et al.Power prediction model of PV power generation based on double screening of samples[J]. Acta energiae solaris sinica, 2018, 39(4): 1018-1025.
[9] 杨洁, 成珂. 基于数据挖掘的神经网络光伏发电预测研究[J]. 激光杂志, 2018, 39(8): 59-62.
YANG J, CHENG K.Neural network photovoltaic power generation prediction based on data mining[J]. Laser journal, 2018, 39(8): 59-62.
[10] 姚宏民, 杜欣慧, 秦文萍. 基于密度峰值聚类及GRNN神经网络的光伏发电功率预测方法[J]. 太阳能学报, 2020, 41(9): 184-190.
YAO H M, DU X H, QIN W P.PV power forecasting approach based on density peaks clustering and general regression neural network[J]. Acta energiae solaris sinica, 2020, 41(9): 184-190.
[11] TAO Y, CHEN Y.Distributed PV power forecasting using genetic algorithm based neural network approach[C]// International Conference on Advanced Mechatronic System, Kumamoto, Japan, IEEE, 2014: 557-560.
[12] UTPAL K D, KOK S T, MEHDI S, et al.Forecasting of photovoltaic power generation and model optimization:A review[J]. Renewable and sustainable energy reviews, 2018, 81: 912-928.
[13] 张永礼, 董志良, 安海岗. 神经网络优化算法在技术经济领域中的应用[M]. 北京: 冶金工业出版, 2015: 23.
ZHANG Y L, DONG Z L, AN H G.Application of neural network optimization algorithm in the field of technical economy[M]. Beijing: Metallurgical Industry Press, 2015: 23.
[14] 王东华. 电动汽车入网对电网的影响及其优化调度[D]. 武汉: 华中科技大学, 2015.
WANG D H.Impact and optimal dispatch of electric vehicles access to power grid[D]. Wuhan: Huazhong University of Science and Technology, 2015.
[15] 黄予春, 曹成涛, 顾海. 基于IKFCM与多模态SSO优化SVR的光伏发电短期预测[J]. 电力系统保护与控制, 2018, 46(24): 96-103.
HUANG Y C, CAO C T, GU H.Short-term photovoltaic generation forecasting scheme based on IKFCM and multi-model social spider optimization SVR[J]. Power system protection and control, 2018, 46(24): 96-103.
基金
新能源与储能运行控制国家重点实验室（中国电力科学研究院有限公司）开放基金资助（NYB51202001608）
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