Comprehensive Review of Carbon-Aware Load Shedding Strategies for Power Distribution Networks: Foundations, Models, and Future Directions

the power sector, which contributes roughly 40% of global energy-related CO₂ emissions, sits at the center of the decarburization challenge. Yet despite this prominence, prevailing operational strategies continue to treat emission reduction as a secondary concern, leaving a meaningful and largely unaddressed optimization gap. This review synthesizes findings from 38 studies spanning carbon-aware power system operation, marginal emission factors, emission modelling, load shedding methodologies, stability constraints, and smart grid integration. Carbon emission flow theory emerges as the most powerful allocation framework, offering 97% accuracy alongside a 40% reduction in computational overhead. Marginal emission factors consistently exceed average factors by 30–50% during peak periods, underscoring the case for temporally granular operational approaches. Adaptive and intelligent shedding schemes demonstrate 28–42% performance gains over conventional methods, while carbon-optimal power flow formulations have achieved 15.8% verified emission reductions without degrading grid stability. The IEEE 33-bus radial distribution system is identified as the most appropriate validation platform for future work. Research directions include machine learning driven marginal emission factor prediction, life cycle emission accounting, and the development of deployable carbon-aware control frameworks for real distribution networks.