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Optimized Load Shedding Strategy Using a Hybrid Neural Network and Genetic Algorithm Framework
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Keywords
Load Shedding
Artificial Neural Network
Genetic Algorithm
Power Systems
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Abstract
Load shedding remains a critical operation within power systems to maintain grid stability under contingency conditions, minimizing the risk of cascading failures and blackouts. Traditional approaches to load shedding often rely on static rules or heuristic methods, which lack adaptability and fail to optimize operational efficiency under dynamic conditions. This study proposes a hybrid framework integrating Artificial Neural Networks (ANNs) with Genetic Algorithms (GAs) to optimize load shedding strategies. The ANN component predicts system load demands and voltage stability under variable conditions, while the GA component determines optimal shedding sequences to minimize power disruption. The hybrid framework is evaluated through simulated scenarios reflecting both peak load and contingency events. Results demonstrate a significant reduction in load disruption compared to conventional methods, improved adaptability to real-time variations, and enhanced voltage stability. The framework provides a scalable and computationally efficient solution for modern smart grids, combining predictive intelligence and optimization heuristics. Key contributions include a novel integration methodology, validation through multi-scenario simulations, and a discussion of practical implications for grid operators.
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