A novel two-stage energy sharing model for data center cluster considering integrated demand response of multiple loads

The increasing energy demand of data centers highlights the necessity of exploring joint optimization strategies for scheduling and energy management within data centers. This study establishes a data center cluster (DCC) framework composed of a DCC operator (DCCO) and data center prosumers (DCPs). Furthermore, a two-stage energy sharing model is developed, incorporating the integrated demand response (IDR) across multiple loads. The first stage is the day-ahead optimization stage, in which the probability distribution uncertainties of wind power, photovoltaic power, and loads are fully considered, and a shared energy storage (SES) optimization scheduling method based on the worst conditional value-at-risk is constructed. The second stage is the real-time optimization stage; first, a new peer-to-peer (P2P) trading mechanism based on electricity and heat supply/demand ratios is designed to realize the joint sharing of electricity and heat among DCPs; then, a refined IDR model that considers the temporal-spatial transferability of data loads, household appliance flexibility, thermal retardation and thermal comfort is demonstrated, and some metrics such as efficiency improvement ratio are introduced to evaluate the IDR model; finally, the benefit functions for both DCCO and DCPs are formulated. A Stackelberg game model for DCC is introduced, which incorporates the SES trading price determined by DCCO, along with the IDR and P2P trading strategies employed by DCPs. The results demonstrate that the proposed DCC framework and energy-sharing model achieve a 39.34 % reduction in the total daily operating costs of DCPs, while fostering mutual benefits and a win-win outcome for both DCCO and DCPs.