Energy-Constrained Optimization of Data Center Layouts: An Integer Linear Programming Approach

Abstract

Optimizing the layout of data centers is important for the rapid development of digital infrastructure, while also addressing the issues of energy consumption, environmental sustainability, and geographic resource distribution. Traditional strategies usually focus only on the distance to demand centers and ignore the energy and environmental costs of data centers in densely populated areas. In this paper, we propose a layout optimization model based on energy consumption constraints that combines integer linear programming with binary decision variables. The model combines energy efficiency, renewable resource availability, and regional characteristics to balance economic benefits and environmental impacts, consistent with the “East data, West computing” project. The experimental results showed that the energy efficient scenario consistently reduced costs, from ¥3.68 × 108 to ¥3.08 × 108 without energy constraints, and from ¥4.08 × 108 to ¥3.47 × 108 under energy consumption constraints. Additionally, energy constraints increased the number of required data centers from two to three. The results of the study emphasized the importance of strategic siting, especially in low electricity price areas, in order to optimize the layout and improve sustainability.