Department of Energy, Office of Science, Earth & Environmental Systems Modeling Program Acknowledged Support: yes
DOI: https://doi.org/10.1007/s40518-024-00238-5
Introduction
Electricity system capacity expansion models generally have coarse temporal, spatial, and process representations of grid operations, investment decisions, and infrastructure siting constraints [1,2,3,4,5]. Such models have served the industry well during the long period of resource portfolios dominated by traditional fossil fuel and thermoelectric generation technologies and predictable peak demands. However, evolving uncertainties in climate/extreme weather, technology cost and performance, renewable resource availability, distributed generation and storage, and energy system policies present new dynamical and spatial challenges for maintaining resource adequacy, which require new modeling capabilities [6,7,8,9,10].
Increasing the realism of capacity expansion models can lead to compounding computational demands due to their long modeling time horizons which typically span multiple decades [1, 5]. These time horizons are necessary to capture costs over asset lifetimes, evaluate policy and system transitions, and provide flexibility for planning needs [5, 11]. To overcome the challenge of modeling high resolution processes in traditional capacity expansion models, new methods are under development to link them to finer resolution models through integrated workflows. These innovative frameworks capture critical grid operations and spatial infrastructure considerations and can be used to identify energy plans that are both feasible and robust to the uncertainties introduced by climate, technology, and policy change. This review highlights recent efforts that simulate and analyze high temporal and spatial resolution processes in energy planning such as grid operations and power plant infrastructure siting through integrated workflows.
This paper is structured into three parts. We begin with a discussion of the typical temporal and spatial representation of investment decisions and grid operations in capacity expansion models and cover recent advances and limitations in this space. Next, we summarize recent research that interlinks long-range capacity expansion planning models and higher temporal resolution models and data, followed by integrations with high-resolution geospatial models and data. Last, we discuss existing opportunities and suggest methods for combining recent integration methods demonstrated in the literature in a singular harmonized framework.
References:
[1] Reichenberg L, Hedenus F. The error induced by using representative periods in capacity expansion models: system cost, total capacity mix and regional capacity mix. Energy Syst. 2024;15(1):215–32. https://doi.org/10.1007/s12667-022-00533-4.
[2] Rosende C, Sauma E, Harrison GP. Effect of climate change on wind speed and its impact on optimal power system expansion planning: the case of Chile. Energy Econ. 2019;80:434–51. https://doi.org/10.1016/j.eneco.2019.01.012.
[3] Frysztacki MM, Hagenmeyer V, Brown T. Inverse methods: how feasible are spatially low-resolved capacity expansion modelling results when disaggregated at high spatial resolution? Energy. 2023;281:128133. https://doi.org/10.1016/j.energy.2023.128133.
[4] Brinkerink M, Zakeri B, Huppmann D, Glynn J, Gallachóir BÓ, Deane P. Assessing global climate change mitigation scenarios from a power system perspective using a novel multi-model framework. Environ Model Softw. 2022;150:105336. https://doi.org/10.1016/j.envsoft.2022.105336.
[5] Boyd E. Power sector modeling 101. Washington D.C: US department of energy; 2016.
[6] Mimica M, Dominković DF, Kirinčić V, Krajačić G. Soft-linking of improved spatiotemporal capacity expansion model with a power flow analysis for increased integration of renewable energy sources into interconnected archipelago. Appl Energy. 2022;305:117855. https://doi.org/10.1016/j.apenergy.2021.117855.
[7] Cohen SM, Dyreson A, Turner S, Tidwell V, Voisin N, Miara A. A multi-model framework for assessing long- and short-term climate influences on the electric grid. Appl Energy. 2022;317:119193. https://doi.org/10.1016/j.apenergy.2022.119193.
[8] Fernandez AE, Houghton B, Rubin A, Schifrin A. Grid planning under uncertainty: Investing for the energy transition. 2023. https://www.mckinsey.com/industries/electric-power-and-natural-gas/our-insights/grid-planning-under-uncertainty-investing-for-the-energy-transition. Accessed 15 May 2024.
[9] O’Connell, Voisin N, Fu M. Sensitivity of Western U.S. power system dynamics to droughts compounded with fuel price variability. Appl Energy. 2019;247:745–54. https://doi.org/10.1016/j.apenergy.2019.01.156.
[10] Voisin N, Dyreson A, Fu T, O’Connell M, Turner SWD, Zhou T, et al. Impact of climate change on water availability and its propagation through the Western U.S. power grid. Appl Energy. 2020;276:115467. https://doi.org/10.1016/j.apenergy.2020.115467.
[11] Levin T, Bistline J, Sioshansi R, Cole WJ, Kwon J, Burger SP, et al. Energy storage solutions to decarbonize electricity through enhanced capacity expansion modelling. Nat Energy. 2023;8(11):1199–208. https://doi.org/10.1038/s41560-023-01340-6.
