This study presents a smart grid architecture addressing challenges in renewable energy integration, including limited data use, fragmented analysis, and weak real-time control. The proposed multi-layer framework combines data input, multidimensional analysis, AI optimization, and output evaluation to improve performance. A MATLAB simulation models interactions among load demand, solar and wind generation, battery storage, and grid supply over 24 hours. Results show renewables contribute 60–75% of power, reducing reliance on fossil fuels. Battery storage balances supply and demand, while real-time monitoring, demand response, and automation enhance stability. Energy efficiency reaches about 92%, with lower transmission and distribution losses. Reliability indices (SAIDI 0. 02–0. 05 hours per customer and SAIFI 0. 01–0. 03 interruptions) indicate strong system resilience. Economic analysis shows savings of 60, 000–80, 000 from reduced fuel use despite higher upfront costs. Environmental benefits include major carbon emission reductions. Socioeconomic impacts include job creation and improved energy access. Overall, integrating renewable energy with advanced smart grid technologies offers a sustainable, efficient, and economically viable solution that supports long-term energy resilience and climate objectives. This approach also enhances system flexibility by enabling adaptive control strategies under varying load and generation conditions, ensuring scalability and future integration of emerging energy technologies and digital grid innovations worldwide adoption.
Owusu-Nyarko et al. (2026) studied this question.