AI-Driven Solar Energy Optimization for Sustainable Development in Libya
DOI:
https://doi.org/10.65405/twqfzr73الكلمات المفتاحية:
Artificial Intelligence; Solar Energy; Photovoltaic Systems; Battery Energy Storage Systems; Energy Optimization; Grid-Aware Energy Management; Sustainable Development; Libyaالملخص
This study examines the potential of Artificial Intelligence (AI) to optimize solar-energy utilization and support sustainable development in Libya, considering the country's substantial solar resources alongside the operational and infrastructural constraints of its electricity system. The study adopted a descriptive, analytical, and comparative approach, critically synthesizing Libyan and international literature on solar photovoltaic (PV) potential, AI-based PV and load forecasting, Battery Energy Storage System (BESS) optimization, grid-aware energy management, reliability, resilience, and sustainability. The findings indicate that Libya's primary challenge is not the scarcity of solar resources, but the limited capacity to convert this potential into reliably forecasted, efficiently stored, and grid-compatible electricity. AI techniques can improve PV and load forecasting and provide predictive intelligence for BESS scheduling and energy management; however, forecasting accuracy alone does not ensure operational value unless it is integrated with storage and grid-management decisions. The analysis further demonstrates that BESS should be considered an AI-coordinated flexibility resource, with optimization accounting for state of charge, degradation, uncertainty, cost, reliability, and reserve requirements. Moreover, AI cannot substitute for physical grid-support infrastructure but can enhance the forecasting, scheduling, and coordination of available resources. Based on these findings, the study proposes an integrated pathway: Solar and Weather Data → AI Forecasting → PV/Load Prediction → BESS Optimization → Grid-Aware Energy Management → Reliability and Energy-Efficiency Improvement → Sustainable Development. The study concludes that AI-driven solar optimization could contribute to reducing fossil-fuel dependence, improving renewable-energy utilization, strengthening electricity-system reliability and resilience, and supporting Libya's environmental, economic, technological, and social sustainability. Nevertheless, successful implementation requires reliable energy data, digital and grid modernization, cybersecurity, skilled human resources, supportive regulation, and empirical validation using Libyan operational datasets.
التنزيلات
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