@article {pub4621,
title = {The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing
},
author = {Thomas Schmitt AND Tobias Rodemann AND J{\"u}rgen Adamy},
year = {2021},
month = {December},
abstract = {Model predictive control (MPC) is widely used for microgrids or unit commitment due to its capability of respecting forecasts of loads and generation of renewable energies. However,
while there are lots of approaches to account for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. Analysis shows that if the PV generation is known at the current time step, the costs increase linearly with mean average error of the predictions. If the predicted value is used for the current time step, the relationship can be described by a piece-wise linear function, where the slope is significantly higher for lower errors. Furthermore, despite a time horizon of 24 h, for the presented setting, only the prediction accuracy of the first 75 min is relevant.},
publisher = {MDPI},
journal = {Energies},
volume = {14},
number = {9}
}