Abstract
The traditional power grid is inadequate to overcome modern day challenges. As the modern era demands the traditional power grid to be more reliable, resilient, and cost-effective, the concept of smart grid evolves and various methods have been developed to overcome these demands which make the smart grid superior over the traditional power grid. One of the essential components of the smart grid, home energy management system (HEMS) enhances the energy efficiency of electricity infrastructure in a residential area. In this aspect, we propose an efficient home energy management controller (EHEMC) based on genetic harmony search algorithm (GHSA) to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort. We consider EHEMC for a single home and multiple homes with real-time electricity pricing (RTEP) and critical peak pricing (CPP) tariffs. In particular, for multiple homes, we classify modes of operation for the appliances according to their energy consumption with varying operation time slots. The constrained optimization problem is solved using heuristic algorithms: wind-driven optimization (WDO), harmony search algorithm (HSA), genetic algorithm (GA), and proposed algorithm GHSA. The proposed algorithm GHSA shows higher search efficiency and dynamic capability to attain optimal solutions as compared to existing algorithms. Simulation results also show that the proposed algorithm GHSA outperforms the existing algorithms in terms of reduction in electricity cost, PAR, and maximize user comfort.
Highlights
The traditional grid is facing numerous challenges, including old infrastructure, lack of communication, increasing demand for energy, and security issues
We have proposed a heuristic algorithm genetic harmony search algorithm (GHSA) for single home (SH) and multiple homes (MHs) to reduce electricity expense, peak to average ratio (PAR), and maximize user comfort
The proposed algorithm is tested in the presence of real-time electricity pricing (RTEP)
Summary
The traditional grid is facing numerous challenges, including old infrastructure, lack of communication, increasing demand for energy, and security issues. Authors in [16], present a generic model of DSM in order to optimize energy consumption in the residential sector. A recent work in [19] proposes a novel approach of DSM with the integration of RESs. The energy provider inspects the load profile and the price of the electricity. In [20], authors demonstrate the electricity load scheduling problem for multi-resident and multi-class appliances using problem ladson generalized bender algorithm while considering energy consumption constraint. The economic model is applied based on GA to the microgrid with traditional power plants and RESs. The work in [24], provides an improved HEMS architecture considering various categories of appliances in the home.
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