Robust Model Predictive Control of Uncertain DC Microgrids Based on Improved Adaptive Consensus Algorithm

DC microgrids (MGs) are one of the most critical components of smart grids, since they are responsible for providing high-quality power to dc consumers continuously. In this article, a novel robust model predictive control (RMPC) is proposed as the core element of the controller. The performance of the controller is highly dependent on the model of the power converter. The uncertainties in the parameters of the dc converters are taken into consideration to improve the microgrid's operational flexibility. The stability and robustness of the proposed RMPC strategy in the presence of uncertainty of microgrid elements are demonstrated. In order to coordinate numerous distributed generations in a microgrid, a distributed control approach based on an improved adaptive consensus (IAC) algorithm is recommended. This method requires a communication link among converters to transfer information. The systems are dynamically affected by many forms of communication topologies. The proposed IAC algorithm has the ability to be adaptively modified when the network topology changes by some operations such as plug-and-play (PnP) and link failure. The suggested RMPC and IAC methods are subjected to a robustness examination. To demonstrate the efficacy and resilience of the proposed control technique, many simulations of voltage tracking, load change, link failure, communication topology changes, and PnP operation in MATLAB/SimPowerSystems toolbox are performed. The statistics reveal that the proposed strategy outperforms other techniques.