Cross-Domain Knowledge Retrieval Framework for Power Dispatching Based on Large Language Models and Hybrid Vector-Relational Databases

The paper introduces a new Cross-Domain Knowledge Retrieval Framework that is aimed at optimizing dispatching of power in the current electricity grids. The framework combines Large Language Models (LLMs) with hybrid vectors-relational databases to use structured and unstructured data to make better decisions. Conventional grid management systems usually operate with limited datasets and invariable models and this may lead to inefficient power generation, high operation cost and poor grid stability. Conversely, the proposed system works with various sources of data, such as live grid load data, energy prices, and weather projections to provide adaptable and cost-efficient solutions to dynamic power dispatching. The framework is able to maximize power production and distribution by integrating advanced machine learning algorithms with real-time data, with a particular emphasis on maximizing the use of renewable sources and reducing the use of costly, non-renewable ones. It uses deep reinforcement learning, semantic search to process unstructured data, including weather report and operational logs, in addition to conventional structured data (e.g., market data and grid status). This system is integrated to enable it to make real-time changes depending on the changing conditions of demand and generation so as to achieve the best grid stability and efficient use of the available resources. The numerical analysis system performance shows that the system has significantly improved compared to the literature methods. It is shown that the Proposed System will result in a 90% reduction in costs, a 95% decrease in energy consumption, and a 95% improvement in grid stability and a response time of 0.05 seconds. Compared to such methods as Knowledge Graph-based Dispatch, Deep Reinforcement Learning (DRL), Multi-agent Reinforcement Learning (MARL), and Distributed Optimization, the Proposed System is better in all the most important performance metrics. It is the best at real-time decision-making, dynamic optimization, and the overall efficiency of the grid, providing the highest performance regarding the reduction of costs, energy efficiency, and grid stability. The suggested structure is a scaled and flexible approach to managing the power grid in the future, especially in situations when it is crucial to combine renewable energy, make the grid run in real-time, and achieve cost reduction at the same time to keep the grid balanced and cause the least harm to the environment.