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2020 | OriginalPaper | Chapter

7. Uncertainties in Microgrids

Authors : Carlos Bordons, Félix Garcia-Torres, Miguel A. Ridao

Published in: Model Predictive Control of Microgrids

Publisher: Springer International Publishing

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Abstract

Uncertainties in the supply or load is an important issue that must be tackled in Energy Management Systems (EMS) of a microgrid. Renewable generation (solar or wind) and consumer loads typically are not controllable but a forecast of their time evolution is of great interest, especially if control techniques as MPC are applied, where the prediction in a future time horizon plays a crucial role. Prediction of renewable production is an active field of research, based on weather forecast and historical data, analyzed by a range of statistical methods or alternatives as neural networks, machine learning, etc. Nevertheless, uncertainty in these values is unavoidable, and the approach in this chapter is the explicit characterization and introduction in the control problem of those uncertainties, that is, the deterministic decision-making of conventional controllers is replaced by a stochastic process. MPC is essentially a deterministic approach, and can be troublesome in systems where uncertainty is an important topic. This chapter is devoted to the application of Stochastic MPC (SMPC) to the EMS problem. SMPC is based on an explicit statistical representation of the uncertainties, i.e., probabilistic distribution, and including it in the optimization problem formulation. Also, constraints can be defined stochastically and some violations are allowed with a determined probability criteria. Next sections describe some of these stochastic MPC algorithms and its application to a laboratory-scale microgrid.

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Footnotes
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Metadata
Title
Uncertainties in Microgrids
Authors
Carlos Bordons
Félix Garcia-Torres
Miguel A. Ridao
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-24570-2_7