Cloud Motion Vector System to Monitor and Predict Output Power of a Photovoltaic System in Real Time

Pacheco, Kyle M.; McManus, Jacob P.; Carpinello, Michael J.; Moreira, Matthew C.

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Cloud Motion Vector System to Monitor and Predict Output Power of a Photovoltaic System in Real Time

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When a PV system is covered by a cloud shadow, the energy supply becomes less than the demand, and therefore, the backup generator needs to turn on to bring the supply back up. To ensure that the demand is always met, it is important for utility companies to provide alternate backup energy sources, such as backup generators, to supplement the remaining demand. Since the switch from solar to backup power is not instantaneous, it is important to predict the PV system output power. We propose a cloud motion vector system (CMVS) model that will operate in real-time at a frequency of 10 kHz and utilizes a cluster of ambient light sensors to detect a cloud rolling over a PV system and to determine size, speed, and direction of the moving cloud. This data is fed into a PV system model to compute power reduction due to change in irradiance from the cloud cover and turn on the backup power supply. In turn, this endeavor aims to create a sustainable electrical grid network with the majority of power coming from renewable energy resources. Furthermore, this model will prevent frequent switching of voltage-controlled devices, which may otherwise shorten their life expectancy and ultimately increase operating costs.

This report represents the work of one or more WPI undergraduate students submitted to the faculty as evidence of completion of a degree requirement. WPI routinely publishes these reports on its website without editorial or peer review.

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