Aligning adjustable resource flexibility with spot markets using hierarchical model predictive control for park integrated energy systems

The increasing volatility in electricity spot markets necessitates a systematic method to align the heterogeneous adjustment capabilities of energy resources with the temporal characteristics inherent to market timescales. This paper proposes a hierarchical coordinated dispatch framework based on Model Predictive Control (MPC) to address this challenge for park integrated energy systems (PIES). The core innovation is a hierarchical principle that quantitatively classifies resource flexibility using a multi-dimensional indicator system, which evaluates each resource's power regulation range, energy shifting capacity, and dynamic response speed. These indicators are synthesized into dynamic participation ratios using a weighted aggregation method, where weighting coefficients are adaptively adjusted according to the level of forecast uncertainty to balance economic efficiency and robustness. These ratios are then embedded as penalty-based soft constraints within the MPC optimization model, ensuring that resources are allocated to market layers commensurate with their intrinsic physical characteristics. Comparative case studies based on a real-world PIES under varying forecast uncertainties demonstrate the framework’s effectiveness.