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The role of nuclear energy and baseload demand in capacity expansion planning for low-carbon power systems

Abstract

The green transition requires electrifying industries with traditionally stable energy demands. Combined with the rise of artificial intelligence (AI) and hyperscale data centers, a significant increase in grid-connected baseload is expected. These loads, with high capital and operational costs, often lack financial incentives for flexibility. This paper explores how the modeling of additional load affects the optimal energy mix under varying nuclear energy overnight construction cost (OCC) levels, highlighting nuclear energy’s potential role in providing the necessary baseload for AI data centers and heavy industry electrification. By utilizing an analytical approach, the study assesses how additional load profiles match variable renewable energies (VRE) outputs to determine the mix of technologies to be responsible for accommodating additional power demands. A stylized case study using the baseload addition (BA) method showed a significant increase in the share of baseplant units, handling 95.1% of the additional load. In contrast, linear load profile scaling (LLPS) of historical loads left the energy mix unchanged. A more detailed case study with the European Model for Power system Investment with Renewable Energy (EMPIRE) confirmed the same trend as found in theory, indicating a 24% increase in nuclear generation using the BA method over historical load scaling. Moreover, a low-cost nuclear scenario (€4200/kW) installed 59% more capacity than a high-cost scenario (€6900/kW). Finally, higher nuclear shares are shown to significantly reduce the need for transmission, storage, VRE curtailment, and land use, emphasizing nuclear power’s potential role in low-carbon power systems.

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Category

Academic article

Language

English

Author(s)

  • Martin N. Hjelmeland
  • Jonas Kristiansen Nøland
  • Stian Backe
  • Magnus Korpås

Affiliation

  • Norwegian University of Science and Technology
  • SINTEF Energy Research / Energisystemer

Year

2024

Published in

Applied Energy

ISSN

0306-2619

Publisher

Elsevier

Volume

377 Part A

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