A Stochastic Investment Planning Model for On-Site Energy Sector Coupling in Data Centers

In this paper, we address the investment planning problem related to on-site sector coupling of cooling, heat and electricity as it is the case for e.g. data centers. Data centers have a significant energy demand, in particular, for cooling purposes. The efficiency of cooling can be improved by using storage systems such as aquifer thermal energy storage (ATES) systems that make use of underground water to store cooling and heating energy. The electrification of cooling and heat demand as well as the use of ATES systems couples heating, cooling and electricity energy flows together. In this work, we propose a novel stochastic optimization model based on mixed-integer linear programming (MILP) to optimize the investment decisions of data centers for additional energy technologies taking pre-existent technologies and uncertain factors in relation to prices, demand and production into account. Due to the long-term nature of the decisions, we model energy-based uncertainties using data from long-term climate models. The model formulation includes the ATES operation and is generic to allow for different setups of heating, cooling and electricity technologies and flows. We show the applicability and performance of the model and the resulting decisions for a real case from a data center in Denmark.