A Hybrid Flower-Grey Wolf Optimizer Based Home Energy Management in Smart Grid
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Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to the power usage pattern of the electricity users and assists the utility in minimizing peak power demand in the duration of high energy demand slots. Where, this ultimately leads to carbon emission reduction, total electricity cost minimization and maximization of grid efficiency and sustainability. Nowadays, many DSM strategies are available in existing literature concentrate on house hold appliances scheduling to decrease electricity cost. However, they ignore peak to average ratio (PAR) and consumer’s delay minimization. In this paper, a load shifting strategy of DSM is considered, to decrease PAR and waiting time. To gain aforementioned objectives, the flower pollination algorithm (FPA), grey wolf optimizer (GWO) and their hybrid i.e., flower grey wolf optimizer (FGWO) are used. Simulations were conducted for a single home consist of 15 appliances and critical peak pricing (CPP) tariff is used for computing user’s electricity payment. The results show and validate that load is successfully transferred to low price rate hours using our proposed FGWO technique, which ultimately leads to 50.425% reduction in PAR, 2.4148 h waiting time and with 54.654% reasonable reduction in cost.
KeywordsFlower pollination algorithm Grey wolf optimizer Metaheuristic Techniques Heuristic techniques Appliances scheduling Home energy management Demand side management Smart grid
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