Robust optimal energy management of data center equipped with multi-energy conversion technologies.

The growing concerns about environmental impacts, electricity costs, and energy required of data centers are mounting. To overcome these challenges, the data center needs to integrate high-efficiency and environmentally friendly technologies to cover these issues. The combined cooling, heating, and power (CCHP) system refers to the synchronous generation of electricity and valuable heating and cooling energy through multi-energy conversion, which can address many of the data center's challenges. Hence, this paper focuses on the optimal energy management of the CCHP-based data center integrated with renewable energy, multi-energy storage, and multi-energy conversion to minimize daily electricity fees. The presence of multiple electrical and thermal consumptions in the data center creates an ideal opportunity to implement integrated demand response (IDR) for both types of demands. Besides the local workload in the data center, the operator seeks to optimize the resource scheduling to serve the electrical, heating, and cooling demands in nearby buildings and participate in the gas and power markets. Hence, the robust framework is extended in the proposed energy management to control the market price uncertainty, which denotes the decision-maker's conservatism level. The proposed model is examined in the sample data center structure. Simulation and results are carried out in the GAMS software environment and solved via CPLEX solver, which is discussed for several cases studies. Under the coordinated robust energy management with the incentive-based IDR and multi-energy conservation technologies, a 13.2% cost-saving can be achieved for the data center. • Proposing the optimal energy management strategy for the CCHP-based data center. • Providing the IDR for both electrical and heat demands in the data center to enhance the system's flexibility. • Introducing robust optimization in data center's energy management model to capture price uncertainty. • Investigating multi-energy technologies to serve data center's workloads, besides consumptions of nearby buildings. [ABSTRACT FROM AUTHOR]