Stochastic Long-Term Joint Decarbonization Planning for Power Systems and Data Centers: A Case Study in PJM

TL;DR

This paper introduces a dynamic, stochastic joint planning framework for decarbonizing power systems and data centers over 15 years, optimizing infrastructure development to reduce costs and emissions in PJM.

Contribution

It presents a novel co-optimization model that integrates long-term data center siting and power system expansion, considering both operational and embodied emissions.

Findings

Supports up to 55 GW data center demand in PJM.

Reduces investment costs by 12.6% compared to non-joint planning.

Increases renewable deployment by 25.5% when including lifecycle emissions.

Abstract

With the rapid growth of artificial intelligence (AI) and cloud services, data centers have become critical infrastructures driving digital economies, with increasing energy demand heightening concerns over electricity use and carbon emissions, emphasizing the need for carbon-aware infrastructure planning. Most studies assume static power systems, focus only on operational emissions, and overlook co-optimization. This paper proposes a dynamic joint planning framework that co-optimizes long-term data center and power system development over 15 years. The model determines siting, capacity, and type of data centers alongside power generation expansion, storage deployment, and retirements, accounting for both operational and embodied emissions. To handle multi-scale uncertainty, a large-scale two-stage stochastic program is formulated and solved via an enhanced Benders decomposition. Applied to the PJM Interconnection, with curated datasets released on GitHub, results show the system can support up to 55 GW peak data center demand, with Virginia (DOM) and Northern Illinois (ComEd) as optimal hosts. Compared to non-joint planning, the framework cuts investment cost by 12.6%, operational cost by 8.25%, and emissions by 5.63%. Including lifecycle emissions further raises renewable deployment by 25.5%, highlighting embodied carbon's role in deeper decarbonization.