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Impact of model resolution on scenario outcomes for electricity sector system expansion

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  • Mallapragada, Dharik S.
  • Papageorgiou, Dimitri J.
  • Venkatesh, Aranya
  • Lara, Cristiana L.
  • Grossmann, Ignacio E.

Abstract

Power sector capacity expansion models (CEMs) explore least-cost infrastructure trends under alternate techno-economic and policy scenarios. To maintain computational tractability when considering investment decisions over multiple decades, CEMs typically rely on a compact representation of annual hourly grid operations. Even as CEMs are being used to explore the role of variable renewable energy (RE) sources in the transition to a low-carbon grid, the required temporal resolution and operational detail in a CEM to adequately capture the fundamental economics of RE sources remains an open question. Here, we investigate the impact of embedding additional operational and temporal details in a CEM framework on the resulting projections for generation capacity additions and their utilization. Our approach is based on systematically comparing the outputs from a “chronological” CEM (C-CEM), which models annual grid operations using up to 12 representative days (288 h), with outputs from a commonly used “time slice” CEM (TS-CEM), using seasonally-averaged time blocks. The CEMs mainly differ in their representation of operational flexibility of thermal generators as well as the temporal resolution of load and RE generation. Studying the Texas grid over a range of hypothetical RE penetration scenarios, we find that, more often than not, the TS-CEM estimates higher solar capacity and lesser wind and natural gas capacity relative to the C-CEM, with 35% higher solar capacity projected by the TS-CEM in one scenario. We also test capacity projections of both CEMs through an hourly grid operations model to explore operational metrics, such as the ability to meet demand subject to intra and inter-annual variations in load and RE generation. This experiment reveals that C-CEM projections consistently lead to lower unmet demand compared to the TS-CEM capacity projections. These findings imply the need for sufficient temporal resolution and chronology or validated parameterizations that yield similar behavior to be included in power sector CEMs and multi-sector energy-economic models using a time slice representation.

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  • Mallapragada, Dharik S. & Papageorgiou, Dimitri J. & Venkatesh, Aranya & Lara, Cristiana L. & Grossmann, Ignacio E., 2018. "Impact of model resolution on scenario outcomes for electricity sector system expansion," Energy, Elsevier, vol. 163(C), pages 1231-1244.
    • Handle: RePEc:eee:energy:v:163:y:2018:i:c:p:1231-1244
    DOI: 10.1016/j.energy.2018.08.015
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    4. Qu, Chunzi & Bang, Rasmus Noss, 2024. "European Grid Development Modeling and Analysis: Established Frameworks, Research Trends, and Future Opportunities," Discussion Papers 2024/11, Norwegian School of Economics, Department of Business and Management Science.
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    6. Heggarty, Thomas & Bourmaud, Jean-Yves & Girard, Robin & Kariniotakis, Georges, 2024. "Assessing the relative impacts of maximum investment rate and temporal detail in capacity expansion models applied to power systems," Energy, Elsevier, vol. 290(C).
    7. Merrick, James H. & Bistline, John E.T. & Blanford, Geoffrey J., 2024. "On representation of energy storage in electricity planning models," Energy Economics, Elsevier, vol. 136(C).
    8. Scott, Ian J. & Carvalho, Pedro M.S. & Botterud, Audun & Silva, Carlos A., 2019. "Clustering representative days for power systems generation expansion planning: Capturing the effects of variable renewables and energy storage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    9. Marcy, Cara & Goforth, Teagan & Nock, Destenie & Brown, Maxwell, 2022. "Comparison of temporal resolution selection approaches in energy systems models," Energy, Elsevier, vol. 251(C).
    10. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    11. Thomas Heggarty & Jean-Yves Bourmaud & Robin Girard & Georges Kariniotakis, 2024. "Assessing the relative impacts of maximum investment rate and temporal detail in capacity expansion models applied to power systems," Post-Print hal-04383397, HAL.
    12. Mallapragada, Dharik S. & Junge, Cristian & Wang, Cathy & Pfeifenberger, Hannes & Joskow, Paul L. & Schmalensee, Richard, 2023. "Electricity pricing challenges in future renewables-dominant power systems," Energy Economics, Elsevier, vol. 126(C).
    13. Bandyopadhyay, Arkasama & Leibowicz, Benjamin D. & Webber, Michael E., 2021. "Solar panels and smart thermostats: The power duo of the residential sector?," Applied Energy, Elsevier, vol. 290(C).
    14. Wei, Zhinong & Yang, Li & Chen, Sheng & Ma, Zhoujun & Zang, Haixiang & Fei, Youdie, 2022. "A multi-stage planning model for transitioning to low-carbon integrated electric power and natural gas systems," Energy, Elsevier, vol. 254(PC).
    15. Yeganefar, Ali & Amin-Naseri, Mohammad Reza & Sheikh-El-Eslami, Mohammad Kazem, 2020. "Improvement of representative days selection in power system planning by incorporating the extreme days of the net load to take account of the variability and intermittency of renewable resources," Applied Energy, Elsevier, vol. 272(C).
    16. Li, Can & Conejo, Antonio J. & Liu, Peng & Omell, Benjamin P. & Siirola, John D. & Grossmann, Ignacio E., 2022. "Mixed-integer linear programming models and algorithms for generation and transmission expansion planning of power systems," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1071-1082.
    17. Shu, Tony & Papageorgiou, Dimitri J. & Harper, Michael R. & Rajagopalan, Srinivasan & Rudnick, Iván & Botterud, Audun, 2023. "From coal to variable renewables: Impact of flexible electric vehicle charging on the future Indian electricity sector," Energy, Elsevier, vol. 269(C).
    18. Hailun Xie & Lars Johanning, 2023. "A Hierarchical Met-Ocean Data Selection Model for Fast O&M Simulation in Offshore Renewable Energy Systems," Energies, MDPI, vol. 16(3), pages 1-20, February.
    19. Zhao, Dongwei & Jafari, Mehdi & Botterud, Audun & Sakti, Apurba, 2022. "Strategic energy storage investments: A case study of the CAISO electricity market," Applied Energy, Elsevier, vol. 325(C).
    20. Teichgraeber, Holger & Brandt, Adam R., 2022. "Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    21. Li, Mingquan & Virguez, Edgar & Shan, Rui & Tian, Jialin & Gao, Shuo & Patiño-Echeverri, Dalia, 2022. "High-resolution data shows China’s wind and solar energy resources are enough to support a 2050 decarbonized electricity system," Applied Energy, Elsevier, vol. 306(PA).
    22. Mallapragada, Dharik S. & Sepulveda, Nestor A. & Jenkins, Jesse D., 2020. "Long-run system value of battery energy storage in future grids with increasing wind and solar generation," Applied Energy, Elsevier, vol. 275(C).

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