A Review of Control Techniques for Load Shedding in Power Distribution Systems

Load shedding is a vital mechanism in power distribution systems for maintaining grid stability during periods of supply-demand imbalance, equipment failure, or system disturbances. Traditional load shedding techniques, while effective in preventing system collapse, often rely on fixed schedules or static thresholds and lack the flexibility to respond to dynamic grid conditions. With the increasing integration of renewable energy sources, distributed generation, and the evolution of smart grids, there is a growing need for more intelligent and adaptive load shedding strategies. This review paper presents a comprehensive overview of control techniques used for load shedding in power distribution systems, ranging from conventional methods to advanced, real-time, and automated approaches. The study explores various control methodologies including priority-based schemes, optimization algorithms, artificial intelligence (AI), machine learning (ML), and fuzzy logic-based systems. Each technique is evaluated in terms of responsiveness, scalability, implementation complexity, and effectiveness in modern grid environments. The paper also discusses the challenges associated with communication infrastructure, system coordination, cybersecurity, and renewable energy variability. Finally, it highlights future research directions focused on decentralized control, edge computing, and predictive analytics to enhance the reliability and intelligence of load shedding mechanisms in next-generation power distribution networks.