<p>In the ever-evolving landscape of smart grid technology, the role of pricing strategies emerges as a critical determinant in shaping consumer behavior, managing peak demand, and optimizing the overall efficiency of the electrical grid. This research endeavors to push the boundaries of traditional flat pricing models by introducing a pioneering hybrid scheme that seamlessly integrates Critical Peak Pricing (CPP) with a Dynamic component. The adaptability of this hybrid pricing model to real-time responses, especially in scenarios deviating from conventional peak load patterns, demonstrates its unparalleled effectiveness. The study employs a dataset of 1000 consumers equipped with smart meters and harnesses a hybrid clustering approach, combining hierarchical clustering with K-Means, to unveil distinctive consumer groups. These clusters undergo rigorous statistical analysis, laying the groundwork for tailoring pricing strategies. The hybrid pricing model’s performance is rigorously evaluated against conventional flat pricing, utilizing the Representative Load Profile (RLP) of each cluster for a thorough analysis of daily, monthly, and yearly cost implications. Results reveal substantial power price savings for consumers in Cluster 1, 2 and 3, quantified at 2.16%, 0.61%, and 1.80%, respectively. This underscores the accuracy and cost-effectiveness of the hybrid pricing model, offering consumers responsive and economically advantageous energy consumption frameworks.</p>