Chronological Probability Model of Photovoltaic Generation System

doi:10.3969/j.issn.1000－7229.2016.07.004

Abstract

Abstract:

With the growing use of photovoltaic （PV） generation in power system, establishing effective probabilistic model for PV generation becomes an urgent problem to be settled. Conventional chronological probability models of PV generation are based on parametric estimation, which require to assume the probability distribution type of irradiance, and cannot consider the additive constraint between days and hours irradiance sequence. In order to overcome the drawbacks of conventional models, this paper proposes a new photovoltaic sequence probabilistic model based on disaggregation theory and conditional kernel density estimation. Without limiting the probability distribution type of irradiance, the proposed nonparametric model is the non-parametric model, and can capture not only the chronological correlation, but also the additive constraint between days and hours irradiance sequence, which can more accurately reflect the random fluctuation law of photovoltaic generation. The example analysis shows that the model can reflect the change rule of irradiance with higher precision, which has obvious superiority and practicality.

Key words: photovoltaic generation, probabilistic model, additive constraint, chronological correlation, conditional kernel density estimation

CLC Number:

TM 732

JIN Liming, ZHOU Ning, FENG Li, FAN Fei,ZHAO Yuan. Chronological Probability Model of Photovoltaic Generation System[J]. Electric Power Construction, 2016, 37(7): 27-32.

References

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