Stochastic optimization of combined energy and computation task scheduling strategies of hybrid system with multi-energy storage system and data center

Hybrid energy systems, integrating renewable energies, offer a sustainable and low-carbon solution for energy-intensive data centers, addressing the challenges posed by the variability of renewable sources and computational demands. This study proposes a stochastic optimization model of combined energy and computation scheduling of hybrid system and data center, in which a multi-energy storage system of electricity, hydrogen, natural gas, and heat is integrated to increase the flexibility and reliability of system. A scenario generation method for both renewable energy sources and computation loads is developed to characterize their uncertainties, which includes scenario identification, scenario sampling, and scenario generation and clustering steps. An optimization model, considering different objectives of operation cost, penalty cost of renewable power curtailment, and stepped carbon trade cost, is constructed to obtain the best energy and computation task coordinated scheduling strategies. A case study confirms the effectiveness of the proposed strategy for coordinating energy and computation scheduling in data centers. It compares the impact of key variables such as storage configurations and operational objectives. The strategy achieves a 15.26 % cost reduction and a 10.79 % carbon emission decrease versus traditional methods. Accounting for carbon allowances further cuts emissions by 59.04 %, albeit at a 7.67 % higher cost.