Classified modeling and day-ahead optimal scheduling of multi-type adjustable industrial loads in industrial microgrid using improved approximate dynamic programming

Industrial loads (ILs), characterized by their large scale and high automation levels, offer significant potential to mitigate supply-demand imbalances in smart grids with high penetration of renewable energy generation. However, research on modeling the controllable characteristics of industrial loads remains relatively limited. Existing models are often overly simplistic, failing to account for transient processes-which are non-negligible during regulation-as well as potential parameter variations, leading to substantial regulation errors and an inability to meet precision requirements. This paper focuses on adjustable industrial loads and establishes precise regulation response models based on their production characteristics and transient processes, including continuously adjustable industrial load models, discrete parameter-fixed adjustable industrial load models, and discrete parameter-variable adjustable industrial load models. Building on these models, an improved approximate dynamic programming (IADP) algorithm is proposed, which transforms the traditional iteration-based value function approximation method into a numerical fitting approach. This method is utilized to derive a day-ahead optimal scheduling strategy. Finally, the effectiveness of the proposed approach is validated through multiple case studies, where comparisons with optimal scheduling strategies from other modeling approaches and optimization techniques further demonstrate its superiority.