Demand response optimization incorporating thermal comfort in single-family houses with on-site generation: a systematic review

Demand response (DR) is a key strategy for enhancing energy flexibility, allowing buildings to dynamically adjust electricity demand and mitigate supply–demand mismatches—particularly in the context of rising renewable energy integration. Single-family houses (SFHs) are increasingly recognized as decentralized energy actors in advancing DR, owing to their suitability for integrating on-site generation systems such as photovoltaic (PV) panels. In such houses, an energy management system (EMS) coordinates local generation and consumption through DR optimization methods. Due to the high autonomy of single-family houses, effective DR optimization is critical for facilitating occupant participation, especially as thermal comfort significantly affects engagement. Although research in this domain is expanding, a systematic review focusing on DR optimization for SFHs with on-site generation and thermal comfort integration has yet to be conducted. To fill this gap, this review systematically synthesizes existing DR optimization methods in accordance with the PRISMA guidelines. DR optimization approaches are categorized into five groups: rule-based control, mathematical programming, metaheuristic optimization, model predictive control, and artificial intelligence-based methods. It also classifies thermal comfort integration approaches into four types: comfortable temperature zone (CTZ), comfortable temperature deadband (CTD), PMV–PPD, and adaptive thermal comfort models. A mechanistic framework integrating thermal comfort into DR optimization is developed, and a six-dimensional analysis reveals key methodological trade-offs and emerging trends. Finally, the review highlights key research gaps and outlines future directions, including refined thermal comfort metrics, occupant-centric and behavior-aware optimization frameworks, and uncertainty-aware strategies to ensure robust and scalable DR deployment in single-family houses. • Systematically analyzed 42 studies on demand response optimization. • Analyzed DR optimization: rule-based, MPC, mathematical, metaheuristic, AI. • Classified four main types of thermal comfort metrics used in DR optimization. • Future studies should focus on occupant centric, uncertainty aware DR strategies.