Hybrid Energy Microgrids: A Comparative Study of Optimization Techniques

¹ Peter the Great St. Petersburg Polytechnic University, Saint Petersburg 195251, Russian Federation
² Lovely Professional University, Phagwara, Punjab, India
³ Department of EEE, GRIET, Bachupally, Hyderabad, Telangana, India
⁴ Uttaranchal University, Dehradun 248007, India
⁵ Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh 174103 India
⁶ Centre of Research Impact and Outcome, Chitkara University, Rajpura 140417, Punjab, India

^* Corresponding author: darya0690@mail.ru
dsnm.rao@gmail.com
mansinautiyal@uumail.in
rishabh.bhardwaj.orp@chitkara.edu.in
sorabh.sharma.orp@chitkara.edu.in

Abstract

This study examines the relative effectiveness of Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), and Linear Programming (LP) in optimizing hybrid energy microgrids. Drawing upon empirical data derived from the study, the research explores many facets, including economic efficacy, environmental viability, and microgrid robustness. The use of GA showcases a significant 10% decrease in overall system expenses, highlighting its efficacy in augmenting economic feasibility. PSO diligently tracks, attaining an 8% decrease, while SA and LP make significant contributions but provide somewhat lesser cost reductions at 7% and 6%, correspondingly. Within the domain of renewable energy integration, GA and PSO have emerged as frontrunners, with remarkable advancements of 12% and 10%, respectively. SA and LP provide commendable contributions, demonstrating their effectiveness in optimizing the usage of renewable energy sources inside the microgrid, as seen by their respective increases of 8% and 7%. The environmental factor, as quantified by the decrease of carbon emissions, highlights the commendable efficacy of GA and PSO, resulting in significant reductions of 15% and 12% respectively. SA and LP provide praiseworthy environmental efforts, achieving reductions of 10% and 8% respectively. The resilience index highlights the strength of GA and PSO in assessing the resilience of the microgrid, with GA showing an increase of 0.05 and PSO showing an increase of 0.04. SA and LP make a significant contribution, with increments of 0.03 and 0.02, underscoring the potential of evolutionary and swarm-based methodologies to bolster the microgrid’s resilience against disturbances. Scenario analysis effectively brings unpredictability into the operational environment of the microgrid, continually showcasing the remarkable flexibility of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) over a wide range of situations. SA and LP demonstrate consistent efficacy but with somewhat reduced flexibility. Statistical evaluations provide compelling evidence confirming the exceptional efficacy of GA and PSO in improving microgrid metrics. Ultimately, this research provides valuable perspectives on the intricate trade-offs between various optimization techniques, empowering decision-makers to choose strategies that align with specific microgrid objectives. Moreover, it contributes to the wider discussion on resilient, sustainable, and economically feasible energy infrastructures.