Sustainable Energy Management in Smart Cities: Optimizing Smart Grids Using Metaheuristic Algorithms

To address the simultaneous issues of running out of fossil fuels and increasing pollution, we must transition to renewable energy sources. This study shows that a hybrid system with 1,287 solar panels and two wind turbines can meet the energy needs of 500 homes, 500 electric vehicles, and a 50-bed hospital. It also reduces CO2 emissions by 92 percent compared to systems that use fossil fuels. The study employs sophisticated optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Reinforcement Learning (RL). The results demonstrate that renewable energy sources can alleviate the detrimental environmental effects of fossil fuels and substantially reduce energy production expenses. This study is confined to solar and wind energy sources, and the proposed algorithms have been validated exclusively through simulations, lacking real-world experimental validation. This study demonstrates that combining modern optimization algorithms with renewable energy sources can reduce pollution and enhance the sustainability of energy systems in cities. But it needs to be tested in the lab and have a wider range of energy sources.