Antonio T. Lorenzo

Comparing ramp rates from large and small PV systems, and selection of batteries for ramp rate control

We compare the AC power fluctuations from a 1.6 MW and a 2 kW photovoltaic (PV) system. Both of these PV generating stations exhibit fluctuations exceeding 50% of their rated capacity in under 10 seconds. The smaller system can fluctuate more rapidly, exhibiting 50% dropouts in 3 seconds. Although the MW-scale system covers 4000 times as much ground area, the bandwidth of the fluctuations is remarkably similar. We explore explanations for this observation, and we discuss the impact of this on battery sizing.

PVLIB Python 2015

We describe improvements to the open source PVLIB-Python modeling package. PVLIB-Python provides most of the functionality of its parent PVLIB-MATLAB package and now follows standard Python design patterns and conventions, has improved unit test coverage, and is installable. PVLIBPython is hosted on GitHub.com and co-developed by GitHub contributors. We also describe a roadmap for the future of the PVLIB-Python package.

Short-term PV power forecasts based on a real-time irradiance monitoring network

We built an irradiance sensor network that we are now using to make operational, real-time, intra-hour forecasts of solar power at key locations. We developed reliable irradiance sensor hardware platforms to enable these sensor network forecasts. Using 19 of the 55 irradiance sensors we have throughout Tucson, we make retrospective forecasts of 26 days in April and evaluate their performance. We find that that our network forecasts outperform a persistence model for 1 to 28 minute time horizons as measured by the root mean squared error. The sensor hardware, our network forecasting method, error statistics, and future improvements to our forecasts are discussed.

The economic value of forecasts for optimal curtailment strategies to comply with ramp rate rules

We present a method to calculate the economic value of forecasts, based on the use of forecasts to optimize curtailment strategies in scenarios with a ramp rate rule. We consider how and when to limit PV power output in order to comply with a ramp rate rule to avoid penalties, but also calculate how curtailment will reduce revenue from energy yields. This framework provides a way to assess the value of forecasts.

An operational, real-time forecasting system for 250 MW of PV power using NWP, satellite, and DG production data

We developed a real-time PV power forecasting system for Tucson Electric Power using a combination of high-resolution numerical weather prediction, satellite imagery, distributed generation (DG) production data, and irradiance sensors. The system provides forecasts with 10 second resolution for the first 30 minutes and 3 minute resolution out to 3 days. Forecasts out to 30 minutes are updated every 60 seconds based on new data from DG installations and irradiance sensors.

Optimal interpolation of satellite derived irradiance and ground data

We describe how Bayesian data assimilation can be used to improve nowcasts of irradiance over small, city-scale, spatial areas. Specifically, we use optimal interpolation (OI) to improve satellite derived estimates of global horizontal irradiance (GHI) using ground truth data that was collected sparsely over Tucson, AZ. Our results show that the local data indeed improves the satellite derived estimates of GHI. A key to success with OI in this context is to prescribe correlations based on cloudiness, rather than spatially. OI can be used with a variety of data, e.g., rooftop photovoltaic production data or irradiance data, as well as with several different satellite derived irradiance models.