Multi-objective Optimal Scheduling of Data Center Industrial Parks with Hybrid Energy Storage via Hierarchical Optimization

Abstract

As high-performance computing and data centers become the backbones of the digital economy, their integration into "source-grid-load-storage" (SGLS) systems presents unique opportunities for grid flexibility. However, the conflict between operational expenditure and high-precision ancillary service provision remains a significant barrier. This paper proposes a hierarchical collaborative optimization (HCO) framework for data center-integrated SGLS parks. A dynamic optimization model is established to coordinate renewable energy, hybrid energy storage systems (HESS), and flexible computing loads. A two-stage Pareto-based algorithm is developed to quantify the trade-off between the total operational cost and service performance. Unlike traditional methods, we introduce the "Minimum Regret" criterion to objectively identify the optimal operating state by minimizing the geometric distance to the ideal solution. Numerical results demonstrate that the proposed strategy effectively balances conflicting objectives. Compared to a single storage configuration (SSC), the HESS strategy reduces annual costs by over 9 million CNY and significantly extends battery lifespan by shielding it from high-frequency power spikes, ensuring the stability of critical data center operations.