A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids

Reference

T. Pippia, J. Sijs, and B. De Schutter, "A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids," IEEE Transactions on Control Systems Technology, vol. 28, no. 6, pp. 2364-2376, Nov. 2020.

Abstract

A single-level rule-based Model Predictive Control (MPC) scheme is presented for optimizing the energy management of a grid-connected microgrid composed of local production units, renewable energy sources, local loads, and several types of energy storage systems. The single-level controller uses two different models that yield different descriptions of the microgrid and that use different sampling times. The model with a smaller sampling time provides a more detailed description of the microgrid, in order to keep track of the fast dynamics, while the model with a higher sampling time provides a less detailed description and is used for making long-term predictions when it is not needed anymore to track the fast dynamics. Moreover, we propose a novel rule-based MPC method that assigns the value to the binary decision variables in the hybrid microgrid model, e.g. ON or OFF status of generators, charging or discharging mode of energy storage systems, through if-then-else rules, which rely on the price of electricity and the local net imbalance. The standard method of applying MPC to a hybrid model results in a Mixed Integer Linear Programming (MILP) problem. Our proposed rule-based method is able to convert the standard MILP problem into a linear one. We compare our approach through simulations to the MILP approach and we show that our method yields almost no loss in performance while providing a significant reduction in the computation time.

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Bibtex entry

@article{PipSij:19-009,
author={T. Pippia and J. Sijs and B. {D}e Schutter},
title={A Single-Level Rule-Based Model Predictive Control Approach for Energy Management of Grid-Connected Microgrids},
journal={IEEE Transactions on Control Systems Technology},
volume={28},
number={6},
pages={2364--2376},
month=nov,
year={2020},
doi={10.1109/TCST.2019.2945023}
}

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