Optimal planning of data centers with on-site generation and storage a case study in Dublin Ireland

Abstract

Data center energy demand is soaring globally. In Ireland, data centers accounted for 21 percent of national electricity demand in 2023 and are expected to represent 31 percent by 2030, with virtually all incremental load in Dublin. These pressures have driven the Commission for Regulation of Utilities (CRU) to require on-site dispatchable generation (and/or storage) for all pending data center approvals and led EirGrid, the Transmission System Operator (TSO) in Ireland, to suspend new data center applications until 2028. To address these challenges, this thesis develops a mixed-integer linear programming model from first principles to size and operate an on-site energy portfolio consisting of photovoltaic panels, onshore wind turbines, small modular reactors, and battery energy storage for a 5 MW data center in Dublin, Ireland. The model minimizes annualized life-cycle cost by co-optimizing capacity investments and hourly dispatch under realistic time-of-use tariffs, whole sale spot prices, load profile, operational constraints, and regulatory requirements. Under 2025 cost assumptions, the cost-optimal mix comprises PV and wind with grid imports. Battery energy storage enters the least-cost portfolio by 2028 on pure energy arbitrage. Including additional revenue streams, such as demand response, would enable BESS deployment as early as 2025. Sensitivity analyses reveal that system scale, resource cost trajectories, spot price volatility, and demand response participation can substantially reshape investment decisions: SMRs become competitive in the 50 MW scenario; wider intraday price spreads alone justify significant storage capacity; and dynamic demand response revenues can more than double BESS earnings compared to arbitrage. These results demonstrate the model’s utility as a decision-support tool for data center developers, investors, and planners navigating complex economic, technological, and regulatory uncertainties.