Residential Demand Side Management in Smart Grid Using Meta-Heuristic Techniques
The rise of energy demand is an alarming situation for mankind as it can lead towards a crisis. This problem can be easily tackled by assimilating Demand Side Management (DSM) with traditional grid by means of bi-directional communication between utility companies and consumers. This study evaluates the performance of Home Energy Management System (HEMS) using meta-heuristic optimization techniques: Genetic Algorithm (GA) and Crow Search Algorithm (CSA). The appliances are classified in three sets on the basis of their electrical energy consumption pattern. Moreover, the Real Time Pricing (RTP) scheme is used for power bill control. The core aims of this paper are to minimize electrical energy cost and consumption by scheduling of appliances, decline in peak to average ratio, while getting the best out of user comfort. Besides, simulation results illustrate that there is a trade-off between waiting time and electricity cost. The outcomes also indicate that CSA perform better as compared to GA in relation to cost.
KeywordsDemand response Home energy management Optimization Smart grid User comfort
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