从Engle & Granger模型到Johansen模型对光伏发电输出进行了更准确的预测

Ramenah Harry
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引用次数: 0

摘要

法国政府最近决定,到2028年,将法国所有领土、欧洲领土以及印度洋留尼旺岛等海外领土的光伏(PV)容量增加到35GW。然而,将越来越多的光伏发电装置和微电网并入电网可能导致电网频率的波动大于预期。这是由于光伏发电功率输出不仅与工作温度和太阳辐射有关,还与其他环境参数有关。本文仅考虑欧洲地区的两个环境参数,采用Engle & Granger统计方法,得到了不同水平下光伏发电量与太阳辐照度等变量之间的关系。最终确定了没有可疑异方差的关系。该模型是在光伏实际情况统计方法的基础上建立的,比稳定方法模型更符合实际情况。当考虑更多变量时,恩格尔&格兰杰方法不能区分几种协整关系。对于海外地区,我们增加了其他测量的环境变量,并应用了一种更稳健的统计方法,即约翰森向量误差校正模型(VECM)协整方法。在VECM模型中,对于N个解释变量和N > 2,我们建立了一个长期均衡关系,该关系已经过检验,并且在将模型与实测数据进行比较时,结果更加可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
From Engle & Granger model to Johansen model for a more accurate photovoltaic power output forecast
The French government has recently decided to increase the Photovoltaic (PV) capacities to reach 35GW by 2028 in all french territories, the European territory, and overseas territories such as Reunion Island in the Indian Ocean. However, integrating growing numbers of PV power installations and microgrids onto the grid can result in larger-than-expected fluctuations in grid frequency. This is due to PV power output that is not only a function of the operating temperature and solar irradiation but also of other environmental parameters. In this paper, only two environmental parameters are considered in the European zone and when the Engle & Granger statistical method is used, a relationship between variables such as photovoltaic power output and solar irradiation at a different level is obtained. The final relationship without suspicious heteroscedasticity is determined. The model is formulated on the basis of photovoltaic real conditions statistical approach and is more realistic than steady approach models. The Engle & Granger method does not distinguish several cointegration relationships when more variables are considered. For the overseas zone, we added other measured environmental variables and applied a more robust statistical method known as the Johansen vector error correction model (VECM) cointegration approach. In the VECM model, for N explanatory variables and for N > 2, we established a long-run equilibrium relationship that has been tested and the outcome is more than reliable when comparing the model to measured data.
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