Abstract
This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load.
Highlights
Energy utilization efficiency is increasing with increased use of technology and smart appliances in every field of life in the residential, commercial and industrial sectors
We have shown that using TG-Moth-Flame Optimization (MFO), the energy cost can be reduced up to
The results show that the electricity cost of the meta-heuristic algorithms-based scheduled load was very low as compared to the unscheduled load cost
Summary
Energy utilization efficiency is increasing with increased use of technology and smart appliances in every field of life in the residential, commercial and industrial sectors. Simulation results show that our proposed algorithm reduced the end-user discomfort in terms of appliance waiting time being nearly equal to zero, as compared to the bio-inspired optimization algorithms, along with minimization of total energy cost and minimum PAR. Renewable energy sources are integrated for further minimization of the total load and its cost To achieve this goal, the smart electric grid is modelled as a residential sector comprised of homes having different sizes, different Lengths of Operational Time (LOTs) and appliance power ratings. According to the appliances’ ON/OFF status, the Energy Management Controller (EMC) checks the availability of RES and BSU to fulfill the appliances’ power demand If it is available, the appliance will be ON, and the consumer will not wait for appliance scheduling.
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