Date of Completion
Malaquias Peña, Marina Astitha, Diego Cerrai
Field of Study
Master of Science
The use of all-sky cameras and ancillary sensor equipment to monitor and to adjust fine resolution short range predictions of cloud cover offers an opportunity to develop efficient energy management systems. Solar photovoltaic (PV) energy is a variable renewable energy resource since solar irradiance is highly sensitive to the intermittent nature of cloud cover. Thus, tracking and characterizing clouds passing over PV areas is a critical factor when predicting the available energy of solar sources at any given time. Capital and operational costs associated with solar PV implementation are affected when inaccurate predictions are carried out. This research uses a pilot study to analyze the error and uncertainty of cloud forecasts from a numerical weather prediction (NWP) model called the High Resolution Rapid Refresh (HRRR). The study then attempts to quantify cost reductions associated with increasing forecast accuracy through simulation of a decision support scheme. Utility-scale PV farms and residential roof top PV panels continue to grow in response to lowering prices and government incentives. Energy Management Systems (EMS), which traditionally considered only power loads, must now integrate the variability of solar energy. This study provides a proof of concept of Decision Support System (DSS) that could be adapted to more realistic scenarios.
Haegele, Aaron, "Cloud Cover and PV Intermittence: Monitoring, Forecasting and its Economical Value" (2020). Master's Theses. 1534.