Dynamic Demand-Aware Power Grid Intelligent Pricing Algorithm Based on Deep Reinforcement Learning

Abstract

The increasing integration of renewable energy sources and the growing complexity of modern power grids demand innovative solutions for efficient energy management. This paper introduces a novel dynamic demand-aware Power Grid Intelligent Pricing (PGIP) algorithm based on Deep Reinforcement Learning (DRL). The proposed PGIP algorithm aims to optimize energy consumption and pricing in real time by leveraging the capabilities of DRL to adapt to dynamic demand patterns and evolving grid conditions. PGIP employs a sophisticated neural network architecture to model the intricate relationships between various grid parameters, user demand, and pricing strategies. Through continuous learning and adaptation, the algorithm dynamically adjusts pricing structures to incentivize demand-side flexibility while ensuring grid stability. The reinforcement learning framework enables the algorithm to discover optimal policies for pricing in response to changing environmental factors and user behaviors. We used real-world data sets to assess its performance in diverse scenarios. Results demonstrate the algorithm’s ability to optimize energy consumption, reduce peak demand, and enhance overall grid efficiency. Moreover, comparisons with traditional pricing models highlight the superior adaptability and responsiveness of PGIP in addressing the challenges posed by the evolving landscape of power grids. PGIP presents a promising approach to address the dynamic nature of power grids and the increasing demand for efficient energy management.