Dr. Nikita Ramachandra

Prestige Institute of Engineering Management and Research, Indore, Madhya Pradesh
452010, India

Abstract

Rising energy demands lead to higher electricity costs, overburdened power grids, and increased carbon emissions. Implementing load scheduling through home energy management systems (HEMS) offers a solution, but existing systems have limitations, especially in optimization algorithms, objective functions, and integration across households. This study introduces an innovative Kubernetes and IoT-based HEMS for residential clusters. The goal is to optimize energy consumption by reducing electricity bills, peak-to-average ratio (PAR), and user inconvenience applying a Multi-Objective PUMA Optimization (MPO) technique. Thus, the proposed HEMS model innovatively adapts the PUMA algorithm into a multi-objective framework, deploys Kubernetes, and validates through a prototype. IoT-enabled data collection and user preferences are processed by smart scheduler hub (SSH) to generate optimal appliance schedules, executed via smart plugs for convenience and costeffectiveness. Simulations show that the MPO technique reduces daily electricity expenses by 11.89% and PAR by 54.58%. A techno-economic analysis compares HEMS alone, HEMS with Photovoltaic (PV) systems, and HEMS with PV and Battery Energy Storage Systems (BESS), with payback periods of 1.60, 1.75, and 11 years, respectively. The study also evaluates carbon footprint reductions, with HEMS alone achieving an 8.4% reduction in emissions. Furthermore, the proposed solution is designed to be scalable across multiple clusters, thereby enhancing smart grid advancements.

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