An adaptive robust planning method for data center micro-energy system towards flexibility enhancement

Driven by the economic, flexibility, and sustainability requirements of data center (DC) development, a key challenge lies in optimizing the capacity planning of diverse energy devices within DC micro-energy systems. Thus, to further exploit the flexibility potential and ensure robustness, a two-stage adaptive robust methodology is proposed, based on an innovative architecture for DC micro-energy system. First, to achieve the efficient utilization of energy, dual-condition heat pumps is integrated into the system architecture with seasonal waste heat recovery (WHR) strategy. Second, a novel batch load demand response (DR) model with a differential compensation scheme is proposed, uniquely incorporating users’ fatigue effect and trust process, to incentivize load participation. Finally, a two-stage adaptive robust planning model that accounts for planning flexibility is developed, utilizing gaussian process regression (GPR) to capture key features of forecasted data. Case studies demonstrate that compared to conventional waste heat recovery strategies, carbon emission reductions increased by 79% and investment costs for photovoltaic, gas turbine and energy storage systems were reduced by 9.1%, 14.3% and 8.6%, respectively. And compared to scenarios that omit planning flexibility, the planning costs can be reduced by approximately 0.5% to 4.9%. Through the incorporation of resource and planning flexibility, alongside the refinement of the uncertainty set, the flexibility of the system is unlocked, while the stability and economy of the planning results are guaranteed.