scholarly journals On real-time robust model predictive control

Automatica ◽  
2014 ◽  
Vol 50 (3) ◽  
pp. 683-694 ◽  
Author(s):  
Melanie N. Zeilinger ◽  
Davide M. Raimondo ◽  
Alexander Domahidi ◽  
Manfred Morari ◽  
Colin N. Jones
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Robust Model Predictive Control ◽  
Robust Model

scholarly journals Robust Model Predictive Control for Energy Management of Isolated Microgrids Based on Interval Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Huihui He ◽  
Shengjun Huang ◽  
Yajie Liu ◽  
Tao Zhang
Keyword(s):  
Model Predictive Control ◽  
Real Time ◽  
Predictive Control ◽  
Prediction Error ◽  
Optimal Operation ◽  
Robust Model Predictive Control ◽  
Interval Prediction ◽  
Robust Model ◽  
Time Operation ◽  
Prediction Approach

With the integration of Renewable Energy Resources (RERs), the Day-Ahead (DA) scheduling for the optimal operation of the integrated Isolated Microgrids (IMGs) may not be economically optimal in real time due to the prediction errors of multiple uncertainty sources. To compensate for prediction error, this paper proposes a Robust Model Predictive Control (RMPC) based on an interval prediction approach to optimize the real-time operation of the IMGs, which diminishes the influence from prediction error. The rolling optimization model in RMPC is formulated into the robust model to schedule operation with the consideration of the price of robustness. In addition, an Online Learning (OL) method for interval prediction is utilized in RMPC to predict the future information of the uncertainties of RERs and load, thereby limiting the uncertainty. A case study demonstrates the effectiveness of the proposed with the better matching between demand and supply compared with the traditional Model Predictive Control (MPC) method and Hard Charging (HC) method.

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