Demand Response

Demand response (DR) aims to incentivize flexible adjustments in electricity consumption to improve grid stability and efficiency, primarily by shifting load away from peak demand periods. Current research focuses on developing sophisticated algorithms, including reinforcement learning (RL), inverse optimization, and various machine learning models (e.g., neural networks, support vector regression), to predict and optimize consumer responses to dynamic pricing and other incentives, often considering the complexities of diverse energy resources (e.g., EVs, solar). These advancements are crucial for integrating renewable energy sources, managing increasing electricity demand, and enhancing the overall resilience and economic operation of power grids.