A cross-period optimization strategy for dynamic demand response: mitigating bidirectional deviations via advanced and delayed dispatching

Demand response (DR) techniques, such as load shifting and shedding, are essential for maintaining energy balance in renewable-based power systems. However, the reliability of DR is often affected by dynamic response times and power uncertainties, leading to bidirectional under or over responses that deviate from static DR dispatch commands. To address this challenge, this paper proposes a cross-period optimization strategy for DR that mitigates these deviations through advanced and delayed load dispatching. First, the sources of bidirectional response deviations, which arise from dynamic response time, speed and power fluctuations, are quantitatively analyzed from both deterministic and stochastic perspectives. Then, the deviation tolerance of a virtual power plant (VPP) is modeled based on flexibility dispatch costs under a tightly balanced power grid, considering the deviation's direction and magnitude. Moreover, an enhanced load scheduling method is developed to improve the VPP's deviation tolerance by proactively adjusting response timing and regulating DR plan. Finally, a two-layer cross-period optimization algorithm is established, comprising four DR stages: day-ahead VPP flexibility planning, dynamic DR trajectories optimization, robust scenario generation, and proactive deviation compensation.