Experimental and developed DC microgrid energy management integrated with battery energy storage based on multiple dynamic matrix model predictive control

This study presents the energy management and control strategy in the islanded DC microgrid structure in the presence of renewable energy sources (RES) and battery storage units (BU). The BU control structure is planned by considering the state of charge (SOC) indicator of each BU. The proposed model based on sequential distributed energy management and multiple dynamic matrix model predictive control algorithm (MDMMPC) is developed. The MDMMPC algorithm is implemented for power control and management by local controllers. The energy management strategy is formulated by considering generation prioritization and minimal communication based on primary and secondary control objectives. The simulation results have been analyzed in different scenarios such as power generation changes, load changes, disconnection between participating units in energy supply and battery discharge. Also, a hardware-in-the-loop (HIL) environment along with an experimental setup based on the Micro Lab box and dSPACE control desk (DS1202) is presented. In experimental environment, by creating suitable coordination between the converter's behavior and the ESS unit inertia, it not only reduces the undesirable converter's fluctuations but also the converter's behavior is associated with the least overshoot. Simplicity, rapidity, ease of operation, and distributed control scheme are the important features of the experimental structure.

Automated summary

This study presents the energy management and control strategy in an isolated DC microgrid with renewable energy sources and battery storage units. The proposed model is based on sequential distributed energy management and multiple dynamic matrix model predictive control algorithm (MDMMPC). The algorithm is implemented for power control and management, considering generation prioritization and minimal communication. Simulation results show the effectiveness of the strategy in different scenarios. The experimental setup demonstrates the simplicity, rapidity, ease of operation, and distributed control scheme.