Probabilistic coupled EV-PV hosting capacity analysis in LV networks with spatio-temporal modelling and copula theory

IF 2.4 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
IET Smart Grid Pub Date : 2024-09-10 DOI:10.1049/stg2.12189
Chathuranga D. W. Wanninayaka Mudiyanselage, Kazi N. Hasan, Arash Vahidnia, Mir Toufikur Rahman
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Abstract

The authors present an innovative approach for probabilistic coupled electric vehicle (EV) and solar photovoltaics (PV) hosting capacity analysis in low-voltage (LV) distribution networks. The challenges posed by system uncertainties and correlations between different parameters, such as PV generation and EV charging demand, are addressed using probabilistic modelling. To appropriately incorporate the geographical distribution and time-variant patterns of EV charging demand, a comprehensive spatio-temporal (ST) model is developed to capture the trip distance, EV arrival, and charging time. The correlation between the PV generation and EV charging demand is effectively captured by copula theory. The proposed models have been validated using actual EV charging and PV generation data from 36 Australian EV users over 1 year. Power flow simulation with actual data and modelled data have identified EV-only and coupled EV-PV hosting capacities in an Australian LV test network. The coupled EV-PV model presents a higher level of accuracy, having an average mean absolute percentage error (MAPE) of 5.97% compared to independent EV profiles having a MAPE of 10.12%. A voltage profile analysis with the EV and PV profiles also validates the same trend, having MAPE of 1.5% and 1.95%, respectively, for coupled EV-PV and independent EV profiles.

Abstract Image

基于时空建模和copula理论的LV网络EV-PV承载容量概率耦合分析
本文提出了一种低压配电网中电动汽车和太阳能光伏发电承载容量概率耦合分析的创新方法。系统不确定性和不同参数(如光伏发电和电动汽车充电需求)之间的相关性所带来的挑战,使用概率模型来解决。为了恰当地考虑电动汽车充电需求的地理分布和时变模式,建立了一个综合的时空(ST)模型来捕捉出行距离、电动汽车到达和充电时间。利用copula理论有效地捕捉了光伏发电与电动汽车充电需求之间的关系。所提出的模型已经通过36个澳大利亚电动汽车用户在一年内的实际电动汽车充电和光伏发电数据进行了验证。通过实际数据和建模数据的潮流模拟,确定了澳大利亚低压测试网络中纯电动汽车和耦合电动汽车-光伏托管容量。耦合EV- pv模型具有更高的精度,平均绝对百分比误差(MAPE)为5.97%,而独立EV模型的MAPE为10.12%。对EV和PV曲线的电压分布分析也验证了相同的趋势,对于耦合EV-PV曲线和独立EV曲线,MAPE分别为1.5%和1.95%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IET Smart Grid
IET Smart Grid Computer Science-Computer Networks and Communications
CiteScore
6.70
自引率
4.30%
发文量
41
审稿时长
29 weeks
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