AI-DRIVEN SMART GRID OPTIMIZATION FOR RENEWABLE ENERGY INTEGRATION AND LOAD FORECASTING UNDER POWER SYSTEM INSTABILITY IN PAKISTAN

Abstract

Pakistan’s power sector is facing persistent challenges related to energy shortages, system instability, and inefficient integration of renewable energy sources. In this context, artificial intelligence (AI)-driven smart grid optimization has emerged as a promising solution to enhance load forecasting accuracy, improve operational efficiency, and ensure stable renewable energy integration. This study investigated the role of AI-based smart grid systems in addressing power system instability and optimizing energy management in Pakistan. A quantitative explanatory research design was employed, and data were collected from 300 professionals working in the energy sector, including engineers, system operators, and policy experts. The data were analyzed using descriptive statistics, correlation, and regression analysis. The findings revealed that power system instability negatively affects renewable energy integration, while AI-based load forecasting and smart grid optimization significantly enhance grid efficiency and reliability. Among all predictors, smart grid optimization emerged as the strongest determinant of improved system performance. The results confirm that AI-driven technologies play a critical role in transforming traditional power systems into intelligent, adaptive, and efficient energy networks. The study contributes to the theoretical advancement of smart grid literature and provides practical insights for policymakers and energy authorities in developing countries.