Day-ahead scheduling of isolated microgrid integrated demand side management

This paper suggests day-ahead scheduling of microgrid with and without demand side management considering hydrogen storage and plug-in electric vehicles. This is a highly constrained mixed integer nonlinear programming problem which is solved by using quasi-oppositional fast convergence evolutionary programming (QOFCEP) technique. The concept of the quasi-opposition-based learning is incorporated in fast convergence evolutionary programming (FCEP) to improve the efficiency and quality of the solution. QOFCEP employs quasi-oppositional based learning (QOBL) for population initialization and generation jumping. The studied microgrid comprises three diesel generators, one mini-hydro power plant, two wind turbine generators, two solar PV plants, one hydrogen storage system and plug-in electric vehicles. Simulation outcomes of the suggested QOFCEP technique have been compared with those obtained by FCEP and differential evolution (DE). It is seen from numerical results that the cost obtained with DSM is about 0.81% lower than the cost obtained without DSM. It is also seen that the cost obtained from QOFCEP is about 0.52% and 0.78% lower than the cost obtained from FCEP and DE, respectively. The suggested QOFCEP technique has the ability to bestow superior-quality solution.

Automated summary

This paper suggests a new approach for day-ahead scheduling of microgrid, considering hydrogen storage and plug-in electric vehicles, while comparing the cost effectiveness of demand side management. The results show that demand side management reduces costs, and the use of the QOFCEP technique further reduces costs and provides superior-quality solutions.