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- Title
Green energy management in DC microgrids enhanced with robust model predictive control and muddled tuna swarm MPPT.
- Authors
Buchibabu, Prathikantham; Somlal, Jarupula
- Abstract
In recent years, extreme focus on renewable energy has intensified due to environmental concerns and the depletion of fossil fuel supplies. In a DC microgrid that includes AC grid, photovoltaic (PV), wind, and battery storage systems, there are some problems such as intermittency and variability, mismatched generation and demand, inefficient energy utilization by storage batteries, and unstable DC link voltage. To overcome these problems, an integrated strategy for better energy management and a new MPPT technique is proposed in this research. The robust model predictive control (RMPC) method is proposed to deal with uncertainties and disturbances while offering the best possible control options. A comparison of the two algorithms reveals that the RMPC performs better than the conventional model predictive control (MPC) method. To harvest the maximum solar electricity from the PV system, a sophisticated MPPT optimization technique called muddled tuna swarm optimization (MTSO) is applied. Drone squadron optimization (DSO) and slime mould optimization (SMO) are outperformed by MTSO in terms of dynamic performance, effectively tracking the maximum power point (MPP) of the PV system even under various irradiance conditions. The suggested RMPC approach and MTSO technique are effective in achieving optimal energy and battery management as well as maximum solar power extraction, according to the simulation findings. On the OPAL-RT platform, real-time simulation is used to test the control strategy.
- Subjects
CLEAN energy; BATTERY storage plants; ENERGY management; PREDICTION models; ENERGY consumption; MAXIMUM power point trackers; MICROGRIDS
- Publication
Electrical Engineering, 2024, Vol 106, Issue 3, p2799
- ISSN
0948-7921
- Publication type
Article
- DOI
10.1007/s00202-023-02127-4