Published: Jan. 1, 2025
Language: Английский
Published: Jan. 1, 2025
Language: Английский
Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 1744 - 1744
Published: Feb. 8, 2025
The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social technical criteria to enhance system performance resilience. Using comprehensive methodologies, examines state-of-the-art algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO) Non-Dominated Sorting Genetic Algorithm II (NSGA-II), alongside Crow Search (CSA), Grey Wolf Optimizer (GWO), Levy Flight-Salp (LF-SSA), Mixed-Integer Linear Programming (MILP) tools HOMER Pro 3.12–3.16 MATLAB 9.1–9.13, have been instrumental in optimizing HRESs. Key findings highlight role advanced, multi-energy storage technologies stabilizing HRESs addressing intermittency sources. Moreover, integration metaheuristic with machine learning enabled dynamic adaptability predictive optimization, paving way real-time management. HRES configurations cost-effectiveness, environmental sustainability, operational reliability while also emphasizing transformative potential emerging quantum computing are underscored. provides critical insights into evolving landscape offering actionable recommendations future research practical applications achieving global sustainability goals.
Language: Английский
Citations
1Energy Economics, Journal Year: 2025, Volume and Issue: unknown, P. 108378 - 108378
Published: March 1, 2025
Language: Английский
Citations
0Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110274 - 110274
Published: March 19, 2025
Language: Английский
Citations
0Published: Jan. 1, 2025
Language: Английский
Citations
0