Stochastic Long-Term Joint Decarbonization Planning for Power Systems and Data Centers: A Case Study in PJM
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arXiv
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| Format: | Preprint |
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2025
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| author | Shao, Zhentong Yu, Nanpeng Wong, Daniel |
| author_facet | Shao, Zhentong Yu, Nanpeng Wong, Daniel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_25118 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stochastic Long-Term Joint Decarbonization Planning for Power Systems and Data Centers: A Case Study in PJM Shao, Zhentong Yu, Nanpeng Wong, Daniel Systems and Control 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. |
| title | Stochastic Long-Term Joint Decarbonization Planning for Power Systems and Data Centers: A Case Study in PJM |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2510.25118 |