IEEE Access · 2018 · 192 citations · 28 references
EngineeringEnergy EfficiencyReal-time PricingLoad ControlIntelligent Energy SystemEnergy OptimizationEnergy Consumption PatternsSystems EngineeringDemand Side ManagementEnergy ControlEnergy Demand ManagementSmart BuildingDesignSmart HomeTowards Dynamic CoordinationSmart GridEnergy ManagementSmart BuildingsDemand Response
The paper proposes a home energy management system that uses load shifting to optimize smart‑home energy consumption, aiming to reduce costs and peak‑to‑average ratios while preserving user comfort. The system schedules loads day‑ahead and in real time, employing a fitness criterion and a dynamic‑programming knapsack formulation to coordinate appliances and balance ON/OFF demand across peak and off‑peak periods under time‑of‑use, real‑time, and critical‑peak pricing. Simulations show that the optimization reduces appliance waiting time and achieves significant cost and peak‑to‑average ratio improvements, with results statistically significant at the 95 % confidence level.
In this paper, we propose a home energy management system that employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique, which helps in balancing the load during ON-peak and OFF-peak hours. Moreover, for realtime rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our real-time rescheduling problem as knapsack problem and solve it through dynamic programming. This paper also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing, and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.
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