Bidirectional linkage between a long-term energy system and a short-term power market model

Seljom, Pernille; Rosenberg, Eva; Schäffer, Linn Emelie; Fodstad, Marte

doi:10.1016/j.energy.2020.117311

Cited by 17 publications

(4 citation statements)

References 27 publications

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“…An example of the former definition can be found in the study [64], which proposes a novel bidirectional linkage between a TIMES-Norway energy system model and an EMPS power market model to improve the modeling of hydropower generation and external electricity markets in the TIMES-Norway model. As the Norwegian power system is hydropower-dominated, the chosen convergence criterion is the income difference of hydropower producers, which is endogenous in both models.…”

Section: Imentioning

confidence: 99%

Assessing the security of electricity supply through multi-scale modeling: The TIMES-ANTARES linking approach

et al. 2020

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Section: Imentioning

confidence: 99%

Assessing the security of electricity supply through multi-scale modeling: The TIMES-ANTARES linking approach

et al. 2020

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“…Approaches aiming to bridge the "temporal resolution gap" between long-term energy system models and hourly PSMs have been proposed in the past (Deane et al, 2012;Sullivan et al, 2013;Alimou et al, 2020;Brinkerink, 2020;Seljom et al, 2020).…”

Section: Iterative Coupling For Full Model Convergencementioning

confidence: 99%

Bidirectional coupling of a long-term integrated assessment model REMIND v3.0.0 with an hourly power sector model DIETER v1.0.2

Gong

Ueckerdt²,

Pietzcker³

et al. 2022

Preprint

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Abstract. Integrated assessment models (IAMs) are a central tool for the quantitative analysis of climate change mitigation strategies. However, due to their global, cross-sectoral and centennial scope, IAMs cannot explicitly represent the temporal and spatial details required to properly analyze the key role of variable renewable electricity (VRE) for decarbonizing the power sector and enabling emission reductions through end-use electrification. In contrast, power sector models (PSMs) can incorporate high spatio-temporal resolutions, but tend to have narrower sectoral and geographic scopes and shorter time horizons. To overcome these limitations, here we present a novel methodology: an iterative and fully automated soft-coupling framework that combines the strengths of a long-term IAM and a detailed PSM. The key innovation is that the framework uses the market values of power generations as well as the capture prices of demand flexibilities in the PSM as price signals that change the capacity and power mix of the IAM. Hence, both models make endogenous investment decisions, leading to a joint solution. We apply the method to Germany in a proof-of-concept study using the IAM REMIND v3.0.0 and the PSM DIETER v1.0.2, and confirm the theoretical prediction of almost-full convergence both in terms of decision variables and (shadow) prices. At the end of the iterative process, the absolute model difference between the generation shares of any generator type for any year is <5 % for a simple configuration (no storage, no flexible demand) under a “proof-of-concept” baseline scenario, and 6–7 % for a more realistic and detailed configuration (with storage and flexible demand). For the simple configuration, we mathematically show that this coupling scheme corresponds uniquely to an iterative mapping of the Lagrangians of two power sector optimization problems of different time resolutions, which can lead to a comprehensive model convergence of both decision variables and (shadow) prices. The remaining differences in the two models can be explained by a slight mismatch between the standing capacities in the real-world and optimal modeling solutions purely based on cost competition. Since our approach is based on fundamental economic principles, it is applicable also to other IAM-PSM pairs.

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“…By employing a fit-for-purpose, modified and updated energy system model for Norway (IFE-TIMES-Norway) (Loulou 2008;Loulou and Labriet 2008;Danebergs et al 2021;Seljom et al 2020), we explicitly derive the socially optimal selection of WPPs, taking into consideration both energy system revenues and costs, local disamenity costs, and the impacts of WPPs on W&B.…”

Section: Introductionmentioning

confidence: 99%

Spatial Trade-Offs in National Land-Based Wind Power Production in Times of Biodiversity and Climate Crises

Grimsrud

Hagem

Haaskjold

et al. 2023

Environ Resource Econ

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Energy generated by land-based wind power is expected to play a crucial role in the decarbonisation of the economy. However, with the looming biodiversity and nature crises, spatial allocation of wind power can no longer be considered solely a trade-off against local disamenity costs. Emphasis should also be put on wider environmental impacts, especially if these challenge the sustainability of the renewable energy transition. We suggest a modelling system for selecting among a pool of potential wind power plants (WPPs) by combining an energy system model with a GIS analysis of WPP sites and surrounding viewscapes. The modelling approach integrates monetised local disamenity and carbon sequestration costs and places constraints on areas of importance for wilderness and biodiversity (W&B). Simulating scenarios for the Norwegian energy system towards 2050, we find that the southern part of Norway is the most favourable region for wind power siting when only the energy system surplus is considered. However, when local disamenity costs (and to a lesser extent carbon costs) and W&B constraints are added successively to the scenarios, it becomes increasingly beneficial to site WPPs in the northern part of Norway. We find that the W&B constraints have the largest impact on the spatial distribution of WPPs, while the monetised costs of satisfying these constraints are relatively small. Overall, our results show that there is a trade-off between local disamenities and loss of W&B. Siting wind power plants outside the visual proximity of households has a negative impact on W&B.

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Bidirectional linkage between a long-term energy system and a short-term power market model

Cited by 17 publications

References 27 publications

Assessing the security of electricity supply through multi-scale modeling: The TIMES-ANTARES linking approach

Assessing the security of electricity supply through multi-scale modeling: The TIMES-ANTARES linking approach

Bidirectional coupling of a long-term integrated assessment model REMIND v3.0.0 with an hourly power sector model DIETER v1.0.2

Spatial Trade-Offs in National Land-Based Wind Power Production in Times of Biodiversity and Climate Crises

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