Abstract
The conventional power system has been evolving towards a smart grid system for the past few decades. An integral step in successful realization of smart grid is to deploy renewable energy resources, particularly rooftop photovoltaic systems, at smart homes. With demand response opportunities in smart grid, residential customers can manage the utilization of their demand responsive appliances for getting economic benefits and incentives in return. In this regard, this paper proposes an effective home energy management system for residential customer to optimally schedule the demand responsive appliance in the presence of local photovoltaic and energy storage systems. For efficient home-to-grid energy transactions in home energy management system, the stochastic nature of photovoltaic power generation is modeled with the beta probability distribution function for solar irradiance. The main contribution of this paper is the application of polar bear optimization (PBO) method for optimally solving the scheduling problem of demand responsive appliances in home energy management system to minimize electricity consumption cost as well as peak-to-average ratio. The effectiveness of the proposed metaheuristic optimization technique is proven by performing different case studies for a residential consumer with different base load, uninterruptible deferrable, and interruptible deferrable appliances under a real-time energy price program. Comparative results with different metaheuristic techniques available in the literature show that the electricity consumption cost and peak-to-average ratio are effectively optimized using the proposed PBO algorithm.
Highlights
The current electric power system is moving toward smart grid system due to increasing integration of distributed energy resources, renewable energy resources (RERs) and energy storage systems (ESSs), smart sensors, and smart meters [1]
It can be noted that the standard deviation in execution time of the proposed polar bear optimization (PBO) was better for all cases in comparison with the genetic algorithm (GA) and grey wolf optimization (GWO), which validates the performance of the proposed technique
To evaluate the performance and effectiveness of the proposed technique, three scenarios were considered, and the obtained results were compared with grey wolf optimization, genetic algorithm, and the unscheduled methods
Summary
The current electric power system is moving toward smart grid system due to increasing integration of distributed energy resources, renewable energy resources (RERs) and energy storage systems (ESSs), smart sensors, and smart meters [1]. In [34], a fuzzy logic-based scheduling model was proposed for maintaining supply demand balance by efficiently scheduling DRAs, RERs, and ESSs. An optimal HEMS was developed in [35] for optimized DRAs, RERs, and ESS scheduling using enhanced differential evolution, GWO, and their hybrid evolutionary algorithm to minimize ECC and PAR under real-time energy prices. The objective was minimization of ECC by shifting DRAs from high to low energy price periods according to defined preferences It can be summarized from the above-discussed literature that HEMS has been optimally solved using different algorithms: mixed-integer linear and non-linear programming, electron drifting algorithm, PSO, convex programming, dynamic programming, GA, cuckoo search algorithm, scorebased HEMS algorithm, Dijkstra algorithm, and integer linear programming.
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