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Integrated planning of net-zero power systems for all

Nature Energy (2026)Cite this article

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Abstract

Achieving global net-zero power systems by mid-century demands integrated frameworks addressing climate mitigation and energy access equity. Here we present a spatio-temporally resolved global power system model (0.25° × 0.25°, 8,760 hours) co-optimizing capacity expansion and operational strategies. Findings show that net-zero global power systems meeting universal electricity needs for decent living standards are technically feasible, requiring 15–20 TW of variable renewable energy (VRE). Abundant VRE resources offer cost-effective electricity access in low-income regions, such as Africa, promoting climate justice. Land use is critical, with solar photovoltaics alone requiring over 9 million hectares. Over 80% of VRE is within 200 km of load centres. Demand-side management could reduce system costs by 6.5% (∼US$182 billion yr−1). Expanding international transmission and removing renewable technology trade barriers could cut costs by 5.6% (∼US$157 billion yr−1) and 12.2% (∼US$345 billion yr−1), underscoring the pivotal role of international collaboration in building inclusive net-zero power systems.

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Fig. 1: Scenario framework and associated SCOE for net-zero power systems.
The alternative text for this image may have been generated using AI.
Fig. 2: Key features of optimized net-zero power systems across scenarios.
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Fig. 3: Optimized deployment of variable renewable energy.
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Fig. 4: Cell-level installation factors for solar photovoltaic and wind power in the base scenario.
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Fig. 5: Results of installed firm generators and daily generation profile.
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Fig. 6: Results of long-distance transmission and energy storage.
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Fig. 7: Results of carbon capture and storage deployment, carbon abatement costs, and marginal carbon abatement cost.
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Fig. 8: Economic performance and revenue sufficiency in the mid-century power system.
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Data availability

All input data are referenced in the main text or supplementary information with corresponding citations or repository links. Underlying data for all figures are available via Github at https://github.com/mrziheng/NetZero2050 (ref. 97). Renewable energy resource potential data generated in this study—including installation capacity potential and annual average capacity factors for onshore wind, offshore wind, utility-scale solar PV and rooftop solar PV—are accessible via Github at https://github.com/mrziheng/GlobalRenewableEnergyResource (ref. 98). Linear programming solving files for the GISPO model base scenario are available via Zenodo at https://doi.org/10.5281/zenodo.17618090 (ref. 99). Source data are provided with this paper.

Code availability

All Python scripts for figure generation in the main text, the GISPO model as an open-source Python package and the scenario-specific optimization scripts executing the GISPO model runs for this study are accessible via GitHub at https://github.com/mrziheng/NetZero2050 (ref. 97).

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Acknowledgements

D.Z. is supported by the National Natural Science Foundation of China (numbers 42341202 and 72140005), the Carbon Neutrality and Energy System Transformation (CNEST) Program, the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme and the Environmental Defense Fund. Y.Z. is supported by the Research Grants Council–Strategic Topics Grant (grant number STG2/P-705/24-R). We thank E. Trutnevyte for her constructive comments and suggestions, M. Cai and Z. Li for their excellent research assistance and L. Wang and S. Li for their valuable discussions.

Author information

Authors and Affiliations

  1. Institute of Energy, Environment and Economy, Tsinghua University, Beijing, China

    Ziheng Zhu, Hanjie Mao, Xiliang Zhang & Da Zhang

  2. Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China

    Runxin Yu

  3. Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, MA, USA

    Audun Botterud

  4. Department of Mechanical and Aerospace Engineering, University of California, San Diego, San Diego, CA, USA

    Michael R. Davidson

  5. State Key Laboratory of Regional Environment and Sustainability, School of Environment, Tsinghua University, Beijing, China

    Xi Lu

  6. Beijing Laboratory of Environmental Frontier Technologies, Tsinghua University, Beijing, China

    Xi Lu

  7. Institute for Carbon Neutrality, Tsinghua University, Beijing, China

    Xi Lu

  8. College of Environmental Sciences and Engineering, Peking University, Beijing, China

    Yue Qin

  9. Energy Studies Institute, National University of, Singapore, Singapore

    Bin Su

  10. Environmental Studies, Bren Hall, University of California, Santa Barbara, Santa Barbara, CA, USA

    Grace C. Wu

  11. Department of Geography and Institute for Climate and Carbon Neutrality, The University of Hong Kong, Hong Kong SAR, China

    Yuyu Zhou

  12. State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

    Xiaoye Zhang

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Contributions

Z.Z.: conceptualization, methodology, investigation, software, data collection and calibration, formal analysis, visualization, writing–original draft preparation and writing–review and editing. H.M.: data collection and calibration, visualization and writing–review and editing. R.Y.: conceptualization and writing–review and editing. A.B., M.R.D., X.L., Y.Q., B.S., G.C.W. and Y.Z.: writing–review and editing. Xiaoye Zhang: funding acquisition. Xiliang Zhang: conceptualization and writing–review and editing and funding acquisition. D.Z.: conceptualization, writing–review and editing, funding acquisition and supervision.

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Extended data

Extended Data Fig. 1 Sensitivity analysis of the power system expansion model for results in 2050 under different temporal coverage scenarios.

(a) Capacity (GW) of pumped hydro storage (left) and battery storage (right). (b) Transmission capacity (TW-km). (c) Capacity (GW) of utility-scale (left) and distributed (right) solar PV. (d) Capacity (GW) of onshore wind (left) and offshore wind (right). (e) Generation share (0--1) of different generators within the modelling period. UPV: utility-scale photovoltaics; DPV: distributed photovoltaics; PHS: pumped hydro storage; BAT: battery.

Source data

Extended Data Table 1 Definitions and key findings across scenarios
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Supplementary Figs. 1–156, text and Tables 1–27.

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Zhu, Z., Mao, H., Yu, R. et al. Integrated planning of net-zero power systems for all. Nat Energy (2026). https://doi.org/10.1038/s41560-026-02054-1

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