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Towards real-time opportunistic energy efficient scheduling of the home appliances for demand side management using evolutionary techniques

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Demand side management has empowered the consumers to shift their load to off-peak hours from on-peak hours in response to the varying electricity prices. In this regard, we propose a real-time opportunistic optimization scheme for home appliances keeping in mind the end goal which is to implement the control module in an energy management controller. This controller proficiently manages the residential demand response based on real-time pricing signal. In this paper, we design a controller for home energy management based on following meta-heuristic algorithms: teaching learning-based optimization, genetic algorithm, firefly algorithm and optimal stopping rule theory. The principal goal of designing this controller is to reduce the energy consumption of residential sectors while reducing buyer electricity bill thus, maximizing user comfort. Additionally, the proposed scheme performance is analyzed to show a remarkable impact on residential energy management. Numerical simulations show that proposed scheduling scheme shifts consumer load demand exceeding a predefined threshold to the hours where the pricing of electricity is low. This helps to reduce electricity cost while considering the comfort of a user by minimizing delay and peak to average ratio. In addition, we formulate a chance-constrained optimization problem and evaluate its performance in terms of load curtailment. Chanceconstrained optimization is used to ensure the scheduling of appliances while considering the uncertainties of a load. The major focus is to keep the appliances power consumption within in the power constraint, whilst keeping power consumption below a pre-defined acceptable level.
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