BALANCING THE SURGES: HOW AI SOLVES THE RENEWABLE INTERMITTENCY PROBLEM FOR UZBEKISTAN’s GRID
DOI:
https://doi.org/10.5281/zenodo.20600687Keywords:
artificial intelligence, smart grid, renewable energy, intermittency, load balancing, LSTM, battery energy storage systems, Uzbekistan energy reform, digital transformation, predictive analytics.Abstract
This article examines the role of artificial intelligence (AI) in addressing the intermittency challenge of renewable energy sources (RES) within Uzbekistan’s national power system. As Uzbekistan advances toward its strategic target of achieving 25 GW of installed renewable energy capacity by 2030, the stochastic generation characteristics of solar photovoltaic and wind power systems pose significant risks to grid stability, frequency regulation, and dispatch efficiency. The study analyses AI-based forecasting systems, real-time load balancing algorithms, smart grid technologies, battery energy storage systems (BESS), and digital energy management infrastructure. A comparative assessment is conducted using international experiences from Germany, China, the UAE, South Korea, and the European Union. The findings demonstrate that Long Short-Term Memory (LSTM) neural networks, Deep Q-Learning algorithms, and AI-driven digital twin technologies can reduce renewable energy forecasting errors to below 5% under Uzbekistan’s climatic and operational conditions. The article further proposes practical recommendations, institutional reform measures, and policy directions aimed at accelerating the transition toward an AI-optimised and digitally managed energy system.
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