Smart energy management for revenue optimization and grid independence in an Indian RDS

Abstract

This study presents a novel smart energy management framework for the Indian 28-bus radial distribution system (RDS), optimizing energy consumption across residential, commercial, and industrial sectors. The framework employs the hunter-prey optimization algorithm (HPOA) to enhance appliance scheduling, renewable energy integration (PV, WT, EV, BESS), and dynamic tariff management while addressing uncertainties in electric vehicle (EV) usage and renewable distributed generation (RDG) output. By incorporating photovoltaic (PV) systems, wind turbines (WT), electric vehicles (EVs), and battery energy storage systems (BESS), the system maximizes renewable energy utilization, reducing grid dependency and improving cost-effectiveness. HPOA ensures efficient scheduling, balancing user comfort, cost savings, and revenue generation through real-time pricing (RTP) and feed-in tariffs. The system effectively manages EV and RDG uncertainties, optimizing surplus energy redirection to the grid, thereby enhancing economic viability. A comparative analysis with alternative optimization algorithms demonstrates HPOA’s superiority in convergence speed, computational efficiency, and energy cost reduction. Additionally, the study evaluates the levelized cost of energy (LCOE), confirming the economic feasibility of the proposed model. The results indicate a significant reduction in electricity costs and grid dependence, yielding a total revenue of ₹ 20,982.00—comprising ₹ 2,042.64 from residential, ₹ 4,780.98 from commercial, and ₹ 7,158.38 from industrial sectors. These findings underscore the financial and sustainability advantages of implementing smart energy management strategies in evolving energy landscapes.