TY - JOUR
T1 - Experimental and developed DC microgrid energy management integrated with battery energy storage based on multiple dynamic matrix model predictive control
AU - Sepehrzad, Reza
AU - Ghafourian, Javid
AU - Hedayatnia, Atefeh
AU - Al-Durrad, Ahmed
AU - Khooban, Mohammad Hassan
N1 - Publisher Copyright:
© 2023
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
KW - DC micro-grid
KW - Dynamic matrix control
KW - Multiple-model predictive control
KW - Secondary controller
UR - https://www.scopus.com/pages/publications/85174450248
U2 - 10.1016/j.est.2023.109282
DO - 10.1016/j.est.2023.109282
M3 - Journal article
AN - SCOPUS:85174450248
SN - 2352-152X
VL - 74
JO - Journal of Energy Storage
JF - Journal of Energy Storage
IS - Part A
M1 - 109282
ER -