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The rising number of connected devices, energy management, and local generation facilities implies that the consumers have an increased responsibility to the energy sector. Demand-side management (DSM) techniques involve the consumer in assisting grid services, delay of capital expenses, and utility revenue streams through several behind the metre services. A variety of literature studies have presented many techniques for the implementation of DSM in smart grid systems. This study emphasizes the modelling of DSM utilizing a day-ahead load shifting approach as a minimization problem. The DSM is modelled as an optimization problem whose solution is attained through a nature-based adaptive moth flame (AMF) technique. The formulated work has been tested on three demand zones: residential, commercial, and industrial, with diverse controllable loads. A comparison of solutions based on reduced peak demand and operational costs is undertaken with the proposed AMF optimization algorithm. Finally, it is shown that the DSM technique based on the AMF technique exhibits better savings than the multi-agent and evolutionary approach in the residential and commercial sectors. However, the particle swarm technique proves a better alternative than the proposed technique in achieving cost savings.

Adaptive Moth Flame Optimization based Load Shifting Technique for Demand Side Management in Smart Grid