The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

Thomas Schmitt, Tobias Rodemann and Jürgen Adamy
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Thomas Schmitt: Control Methods & Robotics Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany
Tobias Rodemann: Honda Research Institute Europe GmbH, 63073 Offenbach am Main, Germany
Jürgen Adamy: Control Methods & Robotics Lab, Technical University of Darmstadt, 64289 Darmstadt, Germany

Energies, 2021, vol. 14, issue 9, 1-13

Abstract: Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting 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. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24 h , only the prediction accuracy of the first 75 min was relevant for the presented setting.

Keywords: energy management; model predictive control; PARODIS (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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