A novel high-resolution time float demand response for peer-to-peer electricity trading using a BGA-driven triple-state pricing framework

Conventional demand response scheduling for household appliances typically relies on a fixed one-hour time resolution, dividing the day into 24 intervals. This paper introduces a novel approach by transitioning to higher resolutions of 30, 20 and 15 min, a method not addressed in existing literature. This increased precision, implemented using a Binary Genetic Algorithm (BGA), maintains all operational constraints. Although Demand Response (DR) and Peer-to-Peer (P2P) energy trading have been extensively studied independently, their synergistic integration remains a critical research gap. This paper also bridges that gap by proposing a two-stage framework. In Stage 1, a BGA using high resolution and time float schedules appliances to shift flexible loads from high to low-tariff periods, considering pricing signals, user preferences, and available DG, and storages. The second stage uses a triple-state dynamic pricing mechanism including Supply–Demand Ratio (SDR), Dynamic Supply–Demand Ratio (DSDR) and Time of Use Supply–Demand Ratio (TOU-SDR) to set real-time P2P prices that serve as DR signals. In this stage 2, P2P energy trading is introduced. Prosumers trade surplus energy from DGs, and batteries based on their power deficit or excess in specific intervals, enhancing local utilization and reducing grid dependency. Therefore, this study evaluates this high-resolution framework under three novel pricing schemes, integrating it with P2P energy. Results demonstrate that finer resolutions leading to decreased peak demand and grid purchases. This temporal flexibility enables more effective battery utilization and shifts consumption to off-peak hours. Unlike existing studies that rely on fixed 60-min resolution, our approach enables local self-consumption and hourly trading without battery storage or grid injection. The economic impact is substantial: net profits for all prosumers increase considerably, validating the proposed method’s effectiveness.