Distributed Model Predictive Control for Energy Systems in Microgrids
Paul Stadler, Araz Ashouri, Francois Marechal
TL;DR
This paper introduces a distributed model predictive control scheme for optimizing the operation of decentralized energy resources in microgrids, enhancing coordination and efficiency in smart grid systems.
Contribution
It proposes a novel modular DMPC framework that integrates global and local objectives for microgrid energy management, demonstrated through simulation results.
Findings
Improved coordination of distributed energy resources
Enhanced microgrid performance and efficiency
Validation through simulation on a benchmark low voltage microgrid
Abstract
This paper presents a flexible and modular control scheme based on distributed model predictive control (DMPC) to achieve optimal operation of decentralized energy systems in smart grids. The proposed approach is used to coordinate multiple distributed energy resources (DERs) in a low voltage (LV) microgrid and therefore, allow virtual power plant (VPP) operation. A sequential and iterative DMPC formulation is shown which incorporates global grid targets along with the local comfort requirements and performance indices. The preliminary results generated by the simulation of a studied case proves the benefits of applying such a control scheme to a benchmark low voltage microgrid.
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