首页|考虑分时电价和充电利用率特征的大型电动汽车充电站负荷短期预测方法

考虑分时电价和充电利用率特征的大型电动汽车充电站负荷短期预测方法

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
考虑分时电价和充电利用率特征对电动汽车充电站负荷的影响,提出了融合长短记忆网络和支持向量回归(long short-term memory-support vector regression,LSTM-SVR)的大型电动汽车充电站负荷短期预测方法.首先,建立了分时电价、充电利用率、气象信息等影响充电负荷的因素以及历史充电负荷功率数据作为输入的特征矩阵.其次,运用自适应噪声完备经验模态分解(complete ensemble empirical mode decomposition with adaptive noise,CEEMDAN)方法将包含分时电价和充电利用率的特征矩阵序列进行分解,扩充了数据多样性,并采用组合相关系数方法实现了数据降维和特征选择.然后采用北方苍鹰优化(northern goshawk optimization,NGO)算法分别优化LSTM和SVR的超参数,求解权重系数并构建融合LSTM-SVR模型.最后采用某城市一座大型充电站数据进行验证,对比实验表明,考虑分时电价和充电利用率特征可有效提高电动汽车充电站负荷预测精度8%以上,同时采用所提出的融合LSTM-SVR预测方法能使预测精度进一步提高.
A Short-Term Load Forecasting Method for large scale Electric Vehicle Charging Stations Considering Characteristics of Charging Utilization Rate and Time-of-Use Electricity Price
Considering the impact of time-of-use electricity pricing and charging utilization characteristics on the load of electric vehicle charging stations,a short-term load forecasting method is proposed for large scale electric vehicle charging stations that integrates long short-term memory and support vector regression(LSTM-SVR).Firstly,a feature matrix is constructed that includes factors influencing the charging load such as time-of-use electricity pricing,charging utilization rates,meteorological information,and historical data on charging load power.Secondly,the complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)method is applied to decompose sequences of the feature matrix containing time-of-use electricity pricing and charging utilization rates,which enhances data diversity.This process also employs a combined correlation coefficient method for data reduc-tion and feature selection.Subsequently,the northern goshawk optimization(NGO)algorithm is used to optimize the hyperparameters of both LSTM and SVR,solving for weight coefficients and constructing the integrated LSTM-SVR model.Finally,the model is validated using data from a large scale charging station in a specific city.Comparative experiments demonstrate that considering time-of-use electricity pricing and charging utilization features can effectively improve the accuracy of electric vehicle charging station load forecasting by over 8%.Moreover,the proposed LSTM-SVR forecasting method further enhances prediction accuracy.

short-term load forecastingelectric vehicles charging stationscharge utilization ratetime-of-use electricity pricelong short-term memorysupport vector regressioncomplete ensemble empirical mode decomposition with adaptive noise

王长春、王果、赵倩宇、王守相

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兰州交通大学自动化与电气工程学院,兰州 730070

天津大学电气自动化与信息工程学院,天津 300072

短期负荷预测 电动汽车充电站 充电利用率 分时电价 长短期记忆网络 支持向量回归 自适应噪声完备经验模态分解

国家重点研发计划

2022YFB2403900

2024

10.13648/j.cnki.issn1674-0629.2024.05.008

南方电网技术
南方电网科学研究所有限责任公司

南方电网技术

CSTPCD北大核心
影响因子：1.42
ISSN：1674-0629
年,卷(期)：2024.18(5)
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