Soft Computing Approaches for Sustainable Renewable Energy Planning in Smart Grids with Decentralized Generation

Abstract :

Sustainable renewable energy plays a vital role in mitigating environmental impacts and ensuring long-term energy security. Smart grids facilitate the efficient integration of renewable energy sources while enabling optimized distribution and management. This research investigates the application of soft computing approaches, including fuzzy logic and evolutionary algorithms, to optimize renewable energy planning in smart grids. These techniques enhance decision-making under uncertainty by improving resource allocation, demand forecasting, and grid stability. The incorporation of decentralized generation within the proposed framework enhances flexibility, scalability, and resilience in grid operations. By leveraging these soft computing methods, the grid can adapt to dynamic conditions, ensuring continuous energy supply and reducing dependence on centralized power systems. This study supports the development of sustainable and efficient energy systems, promoting broader adoption of renewable energy sources. The findings demonstrate the potential of soft computing to address the challenges of modern energy systems and to facilitate the transition toward greener, more robust smart grids.