Impact of Data Center Load on Co-Optimized Generation and Transmission Expansion Planning with Embedded Unit Commitment

arXiv:2610.18385 · eess.SY, cs.SY · Submitted 2026-10-13 · Read on arXiv

eess.SY, cs.SY

Submitted: 2026-10-13

Updated: 2026-10-13

Comments: 6 pages, 2 figures, 3 tables. Accepted to the 2027 IEEE Texas Power and Energy Conference (TPEC)

License: http://creativecommons.org/licenses/by/4.0/

The gist: The rapid growth of hyperscale data centers challenges grid expansion planning, yet existing studies focus largely on operational flexibility rather than long-term investment decisions.

Abstract

The rapid growth of hyperscale data centers challenges grid expansion planning, yet existing studies focus largely on operational flexibility rather than long-term investment decisions. This paper examines how large data center loads sited at multiple buses affect co-optimized Transmission Expansion Planning (TEP) and Generation Expansion Planning (GEP). The problem is formulated as a multi-year, Unit Commitment (UC)-embedded mixed-integer linear program on the modified IEEE 24-bus Reliability Test System (RTS-24) with Electric Reliability Council of Texas (ERCOT)-calibrated renewable energy profiles. A 500 MW data center load, about 17.5% of the base-year peak, is allocated across four buses by voltage level, generation headroom, renewable co-location, and network connectivity. Integrating this load raises the 15-year net present value of total system cost by 1,794 million USD (+61.6%) and the system-average locational marginal price (LMP) by 45.2%, with operating cost dominating capital investment (84.2% versus 15.8%). Renewable curtailment remains negligible: existing renewables are already near full utilization, so the added demand is served almost entirely by incremental thermal generation. Most notably, the load shifts the optimal plan from transmission-only to generation-only, replacing the base-case 230 kV reinforcement with a new 400 MW combined-cycle gas turbine and showing that strategically sited generation can substitute for network reinforcement.

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