Scaling demand response readiness frameworks through NLP integration

The integration of renewable energy into power grids poses challenges in variability and stability, particularly in the residential sector, where demand response (DR) programmes are underutilised despite their potential to enhance grid resilience. This study introduces a novel framework that integrates a Demand Response Readiness Factor (DRRF) tool with Natural Language Processing (NLP) to assess the implementability of DR programmes across diverse contexts. By automating the selection of literature and extraction of relevant insights, NLP reduces the need for extensive manual effort, increases model robustness, and expands the scope of information considered. The DRRF employs a Variable Correlation Index (VCI) to identify factors influencing DR success and a Country Similarity Index (CSI) to provide tailored programme recommendations for varied global contexts. Preliminary findings indicate that integration of NLP in the framework can enhance the scalability and adaptability of DR programmes, which can ultimately improve grid stability, increase consumer engagement, and support the integration of renewable energy. While the initial phase focuses on model development, validation of the tool through pilot studies will be performed to expand capabilities to include more complex simulations and real-time adjustments.