TY - JOUR
T1 - Real-time monitoring and operation of microgrid using distributed cloud–fog architecture
AU - Dabbaghjamanesh, Morteza
AU - Moeini, Amirhossein
AU - Kavousi-Fard, Abdollah
AU - Jolfaei, Alireza
PY - 2020/12
Y1 - 2020/12
N2 - In this paper, a new distributed multi-agent framework based on the three layers’ fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs), and microturbines (MT) are considered in the proposed technique. To overcome the complexity of the problem, by using advantages of fog computing, a new fast consensus-based optimization algorithm is used, which is modified based on the fuzzy adaptive leader technique. Finally, the proposed technique is simulated and tested on microgrids with 6 and 14 buses, respectively. Simulation results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.
AB - In this paper, a new distributed multi-agent framework based on the three layers’ fog computing architecture is developed for real-time microgrid economic dispatch and monitoring. To this end, the changes of load at any time will be tracked by the proposed technique, considering unit sudden exits and entries. Moreover, to make the system more realistic, different renewable energies, including photovoltaics (PVs), wind turbines (WTs), fuel cells (FCs), and microturbines (MT) are considered in the proposed technique. To overcome the complexity of the problem, by using advantages of fog computing, a new fast consensus-based optimization algorithm is used, which is modified based on the fuzzy adaptive leader technique. Finally, the proposed technique is simulated and tested on microgrids with 6 and 14 buses, respectively. Simulation results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.
KW - Consensus
KW - Distributed optimization
KW - Energy management
KW - Fog computing
KW - Microgrid
UR - http://www.scopus.com/inward/record.url?scp=85088396418&partnerID=8YFLogxK
U2 - 10.1016/j.jpdc.2020.06.006
DO - 10.1016/j.jpdc.2020.06.006
M3 - Article
AN - SCOPUS:85088396418
SN - 0743-7315
VL - 146
SP - 15
EP - 24
JO - Journal of Parallel and Distributed Computing
JF - Journal of Parallel and Distributed Computing
ER -