Intra-hour solar power forecasts using a real-time irradiance monitoring network

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Monday, 5 January 2015
Antonio T. Lorenzo, University of Arizona, Tucson, AZ; and W. F. Holmgren, M. Leuthold, C. K. Kim, A. D. Cronin, and E. A. Betterton

Intra-hour solar power forecasts are important for electric utility operations. Solar resource variability in this time window stresses automatic generation control (AGC) systems at power plants. With more solar power on the grid, AGC systems may no longer be able to handle large solar power ramp events requiring intervention by grid operators. If operators know in advance how solar power plants will behave, they can ensure that the proper reserves are ready to maintain grid stability. We use a network of real-time irradiance sensors to make these intra-hour solar power forecasts which give operators advanced notice of potential problems.

Our real-time irradiance network consists of approximately 50 sensors in and around Tucson, AZ. Some sensors are custom-made irradiance sensors that are powered via solar panel and communicate via a cellular data connection, which means they can be placed anywhere with cell reception. We also receive monitoring data from rooftop solar power systems, which we previously used to make retrospective forecasts. We now use our real-time network to make operational forecasts.

To make forecasts, we first gather the data from our central database. We then use pre-generated clear-sky profiles to compute the clearness index for each sensor. Next, we make an interpolated clearness index map and we take a weighted average of this new map and previous maps that are appropriately translated based on cloud motion vectors. To estimate the cloud motion vectors, we can use output from the Univ. of Arizona high-resolution WRF model, upper-air soundings, or correlation between sensor pair time-series. Finally, we use the cloud motion vector to translate the map to make forecasts of clearness index for any location in the domain. We can multiply this clearness index by a power plant's clear-sky profile to obtain a power forecast. We've shown that our forecasts can outperform persistence forecasts at short time horizons. Error statistics and comparison to other forecasting methods will be discussed.