With the continuous increase in the proportion of volatile power sources such as wind power and photovoltaics

the scheduling of multi-region integrated energy systems (MR-IES) faces prominent challenges

including the imbalance between regional autonomy and global synergy

insufficient adaptability of physical modeling

difficulties in multi-source data privacy protection

and complex uncertainty regulation. Traditional scheduling methods based on physical models can no longer meet the requirements of high precision

real-time performance

and multi-objective optimization. To address these dilemmas

this paper proposes a data-driven energy storage collaborative distributed optimal scheduling method. Firstly

it systematically sorts out the core characteristics of MR-IES

namely multi-source heterogeneity

collaborative coupling

and interest diversity

and clarifies its operational challenges in cross-regional coordination

multi-constraint compatibility

and fluctuation suppression. Subsequently

a full-process preprocessing system of "data cleaning-heterogeneous conversion-feature fusion" is constructed

integrating multi-dimensional massive data such as source-load prediction

equipment operation

and market prices to form a high-quality training dataset. Innovatively integrating federated learning and deep reinforcement learning technologies

a multi-agent distributed scheduling model is designed. The scheduling problem is transformed into a Markov decision process

and the consistent coordination algorithm is used to realize the parallel advancement of regional energy storage independent optimization and global collaborative optimization. Under the premise of strictly ensuring regional data privacy

it dynamically outputs equipment power generation plans

cross-regional energy transmission schemes

and energy storage charging/discharging strategies. This data-driven scheduling method can not only ensure regional autonomy and data privacy but also significantly improve the renewable energy absorption capacity and global operational efficiency of MR-IES. It provides a practical and innovative technical path for the collaborative optimization of regional integrated energy systems under the access of high-penetration renewable energy.