Robust optimization of configurations of integrated energy system for data center considering source-load uncertainties#br#

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YANG Dongjunming1, FAN Junqiu2 , LI Qingsheng1, ZHANG Yu1, DU Renren1

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Abstract

To address the impacts of multiple uncertainties on configuration schemes of integrated energy systems for data centers, a three-stage decision framework integrating information gap decision theory and bi-level optimization is proposed. First, a bi-level optimization model is established with levelized annual cost and annual CO₂ emissions as the two objectives. In the upper level, the NSGA-II algorithm is used to optimize equipment capacities, while in the lower level, mixed-integer linear programming is employed to minimize daily operating costs. Then, based on information gap decision theory analysis, the robustness radius and opportuneness radius are calculated to quantify each scheme's ability to withstand unfavorable uncertainties and its potential to capitalize on favorable uncertainties. Finally, according to the decision-maker's risk preference, a final recommended configuration is adjusted and generated. A simulation was conducted using a typical data center as a case study, and the results indicate that the risk-averse scheme achieves robustness at the cost of low-carbon performance, with carbon emissions increasing to 3.8 times that of the deterministic scheme; while the risk-opportunistic scheme achieves a dual reduction in both cost and emissions, with decreases of 4.3% and 16.5%, respectively.

Key words

data center (DC) / integrated energy system (IES) / uncertainty / information gap decision theory (IGDT) / robust optimization

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YANG Dongjunming1, FAN Junqiu2 , LI Qingsheng1, ZHANG Yu1, DU Renren1. Robust optimization of configurations of integrated energy system for data center considering source-load uncertainties#br#[J]. Distributed Energy, 0