A Comprehensive Review of Incentive‐Based Demand Response in Energy Markets: Techniques, Challenges, Emerging Trends, and Future Research Directions

ABSTRACT

This paper presents a comprehensive and systematic review of the Incentive‐Based Demand Response (IBDR) program, highlighting its development, design, and implementation in modern Renewable Energy (RE)‐based hierarchical Energy Markets (EMs). The study categorizes the existing body of research into four key domains: EM modeling, optimization techniques, Social Welfare (SW), and uncertainty management, and provides an in‐depth examination of recent advancements and limitations of IBDR programs. Multiple incentive mechanisms are critically examined, which include game‐theoretic frameworks, dynamic incentive schemes, optimization‐based scheduling, and coupon‐driven participation models. The review highlights their comparative effectiveness in reducing load, lowering operational costs, enhancing market efficiency, and stabilizing grid operations under varying demand and RE. The work reviewed recent literature and identified persistent challenges in consumer privacy, scalability, interoperability, and regulatory heterogeneity that continue to obstruct the large‐scale deployment of IBDR programs. By systematically linking IBDR strategies to wind power uncertainty and SW optimization, the review establishes a unified analytical framework largely absent from the existing literature. The future research roadmap highlights emerging technologies, particularly Artificial Intelligence (AI) for predictive optimization, blockchain for transparent and secure transactions, and multi‐energy system integration for coordinated Ancillary Services (AS). The study found that IBDR, along with wind uncertainty approaches, is an important program for developing sustainable, resilient, and socially optimized energy systems that align technical innovation with environmental and economic objectives.