Metaheuristic Algorithms for Optimal Sizing of Renewable Energy Systems in Smart Grids

¹ Lovely Professional University, Phagwara, Punjab, India,
² Department of AIMLE, GRIET, Hyderabad, Telangana, India.

^* Corresponding author: plml@mail.ru

Abstract

This research thoroughly examines the effectiveness of several metaheuristic algorithms in optimizing the dimensions of renewable energy systems in smart grids. It focuses on the urgent need for sustainable and efficient integration of energy. This study examines several optimization strategies by analyzing simulated datasets that represent renewable energy production profiles, energy consumption patterns, and battery storage performance. The data illustrates variations in the availability of renewable energy, with solar energy fluctuating between 500 kWh and 600 kWh, wind energy ranging from 280 kWh to 320 kWh, and hydro energy varying from 180 kWh to 220 kWh. Moreover, energy consumption patterns remain stable sectors, with throughout consumption levels ranging from 400 kWh to 430 kWh, 450 kWh to 480 kWh, and 600 kWh to 630 kWh, respectively. The examination of battery storage performance indicates that the charging efficiency ranges from 90% to 94% and the discharging efficiency ranges from 85% to 89%. Additionally, the depth of discharge ranges from 80% to 84% and the cycle life spans from 2000 cycles to 2400 cycles. By using several metaheuristic algorithms, the research produces a wide range of ideal size arrangements for solar panels, wind turbines, hydro turbines, and battery capacity. These suggested solutions exhibit variances that span from 3.23% to 20%. The results highlight the susceptibility of these algorithms to optimization goals, underlining the need of selecting appropriate algorithms that align with particular limitations and aims. The study's results illuminate the potential of metaheuristic algorithms in attaining effective and sustainable integration of renewable energy systems inside smart grids. This paves the path for informed decision-making and future developments in renewable energy management methods.