An autopilot for energy models – Automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions

Mattsson, Niclas; Verendel, Vilhelm; Hedenus, Fredrik; Reichenberg, Lina

doi:10.1016/j.esr.2020.100606

Cited by 40 publications

(43 citation statements)

References 39 publications

(52 reference statements)

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“…For each country we model RES potentials and timeseries based on results from a GIS-analysis and historical weather data using the GlobalEnergyGIS (GEGIS) model [24]. Eligible areas for PV and wind installations are determined on a 1 km 2 grid resolution by exclusion of protected areas, unsuitable land types and areas of high population density.…”

Section: Electricity Supply Demand and Supply Curvesmentioning

confidence: 99%

“…Areas not within a 400 km radius of a gross domestic product (GDP) density of 100 000 USD/km 2 are further excluded where the threshold serves as a proxy to grid access and location accessibility. For a detailed description we refer to [24]. Annual capacity factors are determined for all eligible grid cells and types of RES using ERA5 reanalysis weather data and data from the Global Wind Atlas (GWA) for 2013 as representative weather year.…”

Section: Electricity Supply Demand and Supply Curvesmentioning

confidence: 99%

“…Using the supply curve we account for projected domestic electricity demand: We generate electricity demand projections with a machine learning approach implemented by GEGIS [24]. The demand projections are based on global datasets for GDP, calendar days, temperature from ERA5 and the SSP2-34 'Middle of the Road' scenario [28] for 2050.…”

Section: Electricity Supply Demand and Supply Curvesmentioning

confidence: 99%

“…We use a multi-step workflow hard-linked using snakemake [34]. In the first part of the workflow we utilise GEGIS [24] to determine potentials for RES, RES generation time-series and predict electricity demand. In the second part of the workflow we implement the ESCs in PyPSA [14] and combine them with the RES potentials, RES time-series and demand predictions into one dedicated PyPSA model for each combination of ESC and exporting country.…”

Section: Technical Model Structurementioning

confidence: 99%

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Import options for chemical energy carriers from renewable sources to Germany

Hampp¹,

Düren²,

Brown³

2021

Preprint

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9 supply chains for synthetic chemical energy carriers from 8 countries to Germany analysed • Modelling at hourly resolution of wind and photovoltaic considering land availability and domestic demand • Lowest cost energy and hydrogen imports are by pipeline from Denmark, Spain and Western Asia and Northern Africa at 40.6 EUR/MWh LHV to 46.8 EUR/MWh LHV (1.4 EUR/kg H2 to 1.6 EUR/kg H2 ) in 2050 assuming 5 % p.a. capital cost • Importing energy carriers or hydrogen is generally cheaper than domestically producing their equivalents in Germany • Adaptation of renewable chemical energy carriers requires policy or other incentivising support to compensate for the cost gap to fossil counterparts • The model and data are made available under open licenses

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Section: Electricity Supply Demand and Supply Curvesmentioning

confidence: 99%

Section: Electricity Supply Demand and Supply Curvesmentioning

confidence: 99%

Section: Electricity Supply Demand and Supply Curvesmentioning

confidence: 99%

Section: Technical Model Structurementioning

confidence: 99%

See 2 more Smart Citations

Import options for chemical energy carriers from renewable sources to Germany

Hampp¹,

Düren²,

Brown³

2021

Preprint

View full text Add to dashboard Cite

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“…Our model predicts quantitatively and qualitatively similar time series for most countries, lending credit to the view that we have a model with generalization across larger geographical regions. The resulting demand series are treated as inelastic in the optimization model (for more details, see [58]).…”

Section: Hourly Demand Profilesmentioning

confidence: 99%

MENA Compared to Europe: The Influence of Land Use, Nuclear Power, and Transmission Expansion on Renewable Electricity System Costs

Fälth,

Atsmon,

Reichenberg

et al. 2020

Preprint

Self Cite

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Most studies of near-zero-carbon power systems consider Europe and the United States. In this paper, we focus on the Middle East and North Africa (MENA), where weather conditions, especially for solar, differ substantially from those in Europe. We use a green-field linear capacity expansion model with over-night investment to assess the effect on system cost of (i) limiting/expanding the amount of land available for wind and solar farms, (ii) allowing for nuclear power and (iii) disallowing for international transmission. This is done under three different cost regimes for solar PV and battery storage. We find that:• The amount of available land for wind and solar farms can have a great impact on the system cost. We found a cost increase of 0-50% as a result of reduced available land. In MENA, the impact on system cost is greatly influenced by the PV and battery cost regime, which is not the case in Europe.

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Which wind turbine types are needed in a cost‐optimal renewable energy system?

Hodel,

Göransson,

Chen

et al. 2024

Wind Energy

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Previous research has indicated that wind power plants can be designed to have less‐variable power generation, thereby mitigating the drop in economic value that typically occurs at high wind power penetration rates. This study investigates the competitiveness of adapted turbine design and the interplay with other flexibility measures, such as batteries and hydrogen storage, for managing variations. The analysis covers seven turbine designs for onshore and offshore wind generation, with different specific power ratings and hub heights. Various flexibility measures (batteries, hydrogen storage and transmission expansion) are included in the optimization of investment and dispatch of the electricity system of northern Europe. Three driving forces for turbine design selection are identified: (1) lowest cost of electricity generation; (2) annual wind production per land area and (3) improved generation profile of wind power. The results show that in regions with good wind resources and limited availability of variation management, it is cost‐efficient to reduce the variability of wind power production by adapting the turbine design. This remains the case when variation management is available in the form of batteries, hydrogen storage and transmission system expansion. Moreover, it is more cost‐effective to improve variability by changing the specific power rating rather than the turbine hub height.

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An autopilot for energy models – Automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions

Cited by 40 publications

References 39 publications

Import options for chemical energy carriers from renewable sources to Germany

Import options for chemical energy carriers from renewable sources to Germany

MENA Compared to Europe: The Influence of Land Use, Nuclear Power, and Transmission Expansion on Renewable Electricity System Costs

Which wind turbine types are needed in a cost‐optimal renewable energy system?

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