Demand response has emerged as a transformative strategy for integrating renewable energy into smart grids, playing a pivotal role in the transition to low-carbon energy systems. Despite the continuous evolution of innovative demand response techniques, a significant gap remains in their large-scale application, limiting their benefits in smart grids Advancing towards a low-carbon smart grid infrastructure requires a comprehensive incorporation of demand response methodologies, ensuring consumer participation across all scales. This research topic aims to explore advanced methodologies for effective demand response management and design, essential for enhancing the functionality of smart grids. By emphasizing the synergy between demand response, renewable energy integration, and market-driven models, this initiative seeks to improve energy efficiency and maintain cost-effectiveness, highlighting the critical role of demand response in the evolution of smart grids.

The main objectives of this research topic are:
1. Bridge the application gap in demand response methodologies to enhance grid efficiency and sustainability.
2. Explore innovative strategies for demand response that support the transition to low-carbon energy systems.
3. Examine policy, market, and technological advancements in demand response to improve energy system resilience.
4. Foster the development of demand response mechanisms that can adapt to dynamic market conditions and energy landscapes.
5. Promote research on artificial intelligence and dynamic pricing models to improve demand response effectiveness.
6. Guide strategic directions for smart grid development and policy formulation towards a sustainable energy future.

Topics to be covered include, but are not limited to:
1. Policies, market strategies, and tariff impacts on demand response programs.
2. Comparative analysis of incentive-based versus price-based demand response approaches.
3. The role of peer-to-peer energy trading within distributed networks and microgrids.
4. Harnessing flexible resources via demand response for enhanced smart grid performance.
5. Applying dynamic pricing and price elasticity estimation for effective load management and demand response.
6. Optimizing the integration of distributed renewable energy sources and storage solutions.
7. Implementing artificial intelligence in demand response strategies for smarter energy use.
8. Innovating demand response baselines for dynamic grid environments.

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Frontiers in Energy Research

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