An Efficient Demand Side Management System with a New Optimized Home Energy Management Controller in Smart Grid

TLDR

The traditional power grid is inadequate for modern demands, prompting the evolution of smart grids and the development of home energy management systems that improve residential energy efficiency. The study proposes an efficient home energy management controller (EHEMC) using a genetic harmony search algorithm to lower electricity costs, reduce peak‑to‑average ratio, and enhance user comfort. The controller models single and multiple homes under real‑time and critical peak pricing, classifies appliance operation modes, and solves the constrained optimization problem with heuristic algorithms such as wind‑driven optimization, harmony search, genetic algorithm, and the proposed GHSA. Simulation results demonstrate that the proposed GHSA algorithm achieves higher search efficiency and dynamic capability, outperforming existing algorithms in reducing electricity cost, peak‑to‑average ratio, and improving user comfort.

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.