Optimal day-ahead scheduling of microgrid with hybrid electric vehicles using MSFLA algorithm considering control strategies

Microgrids (MGs) have turned into vital components of the modern power system with the capability of efficiently accommodating renewable energies and electric vehicles (EVs) with high flexibility. MGs can contribute to mitigating the operating cost and environmental emissions of power systems. Accordingly, the optimal operation of such systems is of very high significance. In this relation, the problem of optimal day-ahead scheduling of MGs is studied in this paper in the presence of renewable power generation, EVs, and storage systems. The problem is modeled as a scenario-based stochastic optimization problem, characterized using the Monte-Carlo simulation (MCS) method. The developed framework includes one objective function, defined as the total operating cost minimization and the presented single-objective optimization problem is tackled using an effective optimization technique, named ?modified shuffled frog leaping algorithm (MSFLA)?. The suggested optimization framework takes into consideration various charging/discharging patterns of EVs. Finally, the problem is simulated on a test MG and the obtained results are compared to those derived by other algorithms to verify the performance of the MSFLA algorithm.

Automated summary

This paper studies the optimal day-ahead scheduling of microgrids considering renewable power generation, electric vehicles, and storage systems. The problem is modeled as a scenario-based stochastic optimization problem using the Monte-Carlo simulation method and solved using the modified shuffled frog leaping algorithm. The framework considers various charging/discharging patterns of EVs and is verified against other algorithms on a test microgrid.