Temporal Interpolation of Gridded Solar Radiation Data for Evaluation of PV Fluctuations

Abstract As the solar photovoltaic systems (PV) are massively installed into power system on an urban scale, short-term (about 10 seconds) fluctuations of PV output can negatively effect on the power system. Battery storage systems are capable of alleviating the effect of PV fluctuations; power flow simulation of such systems is also meaningful to discuss the practical operation and required capacity of those storages. Recent weather satellites provide quasi real time distribution data of solar radiation which is quite suitable for the power flow simulation. However, they do not equip enough temporal resolution to observe the short-term fluctuations of radiation. This paper proposes a temporal interpolation method for the radiation data derived from weather satellite, and aims to obtain plausible fluctuations in radiation values with high temporal resolution. The proposed method is mainly composed of two steps. The first step is an estimation of cloud movements. An object tracking technique is used to obtain plausible cloud movements by considering characteristics of clouds. The second step is an interpolation based on the estimated cloud movements. The experimental results show that the proposed method can produce more plausible radiation values than the other naive methods. We also compared radiation data collected by a satellite and those collected by ground measurement, and discuss the usefulness of interpolated radiation values for the power flow simulation.

[1]  Dazhi Yang,et al.  Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging , 2015 .

[2]  Fei Wang,et al.  Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters , 2012 .

[3]  T. Hoff,et al.  Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance , 2012 .

[4]  Thomas Reindl,et al.  Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage , 2014 .

[5]  A. Okuyama,et al.  An Introduction to Himawari-8/9— Japan’s New-Generation Geostationary Meteorological Satellites , 2016 .

[6]  Juan Gonzalez,et al.  Battery Energy Storage for Enabling Integration of Distributed Solar Power Generation , 2012, IEEE Transactions on Smart Grid.

[7]  Serge J. Belongie,et al.  Cloud motion and stability estimation for intra-hour solar forecasting , 2015 .

[8]  T. Hoff,et al.  Parameterization of site-specific short-term irradiance variability , 2011 .

[9]  R. Belmans,et al.  Voltage fluctuations on distribution level introduced by photovoltaic systems , 2006, IEEE Transactions on Energy Conversion.

[10]  K. M. Muttaqi,et al.  A Novel Approach for Ramp-Rate Control of Solar PV Using Energy Storage to Mitigate Output Fluctuations Caused by Cloud Passing , 2014, IEEE Transactions on Energy Conversion.

[11]  Carlos F.M. Coimbra,et al.  Hybrid solar forecasting method uses satellite imaging and ground telemetry as inputs to ANNs , 2013 .

[12]  Zhengming Zhao,et al.  Grid-connected photovoltaic power systems: Technical and potential problems—A review , 2010 .

[13]  A. Hammer,et al.  Short-term forecasting of solar radiation: a statistical approach using satellite data , 1999 .

[14]  Sangram Ganguly,et al.  DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution , 2017, KDD.