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A sample day can be seen in the following image:Įnergy costs for the various control cases were compared. Sk圜am Vision enables to bring the generators online right on time by the camera for absorbing strong production drops caused by GHI variability. This scenario represents standard hybrid plant control where no forecasts are used. Furthermore the Baseline case assumes that all the power produced by the PV plant is used. The Ideal Forecast case is hypothetical (if the future PV production was exactly known), the Diesel Only case assumes no PV. The Sk圜am Vision case was compared to the other test cases.

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Using our forecasting tool InstaCast™ to predict upcoming GHI time series and make a decision on the next priority genset ignition. Our sky-imager Sk圜am Vision was installed at the site and provides forecasts every 30 seconds.

It consists of a 3.3MWp capacity PV power plant and a 12.5MW capacity thermal plant, where the gensets are assumed being 10 identical 1250kW diesel generators. Our test case was a power plant for rural electrification in Oiapoque, Brazil.

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The camera includes an onboard mini-PC together with a data storage device to perform a local processing of the images and a global horizontal irradiance (GHI) sensor, used for verification of the forecasted data. The purpose of this study was to quantify the economical benefits of using the Sk圜am Vision sky imager to perform hybrid PV-diesel plant control through genset start/stop scheduling.

In our research project, the benefits of forecast-integrated control using the Sk圜am Vision sky imager have been investigated, using data from a hybrid power plant in Brazil, and the simulated scenarios of diesel only, a baseline case without forecasts and an ideal forecast case. PV energy reduces the costs while diesel compensates the PV variability risk. PV-Diesel hybrid plants have been established as one choice for the electrification of isolated and remote communities and off-grid plants for C&I applications. Our colleague Olivier Liandrat recently presented our advances in short-term forecasting for microgrid applications at the Hybrid Power Systems Workshop 2019 in Crete, Greece.