Researchers have developed a novel optimization framework that integrates fuzzy logic and genetic algorithms to manage energy storage and generation in active distribution networks. This approach models system uncertainties, such as weather variations and fluctuating user demands, using fuzzy set representations for renewable energy, load patterns, and market prices. The genetic algorithms then incorporate these fuzzy elements to enhance system stability and optimize operational costs, even under uncertain conditions. Simulations on the IEEE-69 power system demonstrate that this fuzzy genetic algorithm strategy effectively reduces technical constraints and avoids infeasible network adaptations compared to traditional deterministic methods. AI
IMPACT This research offers a more robust method for managing energy distribution and storage in power grids, particularly in the face of renewable energy integration and demand fluctuations.
RANK_REASON Academic paper detailing a new computational framework for power systems. [lever_c_demoted from research: ic=1 ai=0.4]
Read on arXiv cs.NE (Neural & Evolutionary) →
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