Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany

Kühnbach, Matthias; Bekk, Anke; Weidlich, Anke

doi:10.1016/j.esr.2020.100609

Cited by 13 publications

(10 citation statements)

References 43 publications

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“…Dynamic regionalization factors for electricity demand are derived from an hourly resolved demand data set for all NUTS3-regions in Germany for 2015 [17]. Hourly values have been aggregated for each Federal State, and the entire data set temporally shifted to match the days of the week of the year 2021.…”

Section: B Dynamic Regionalization Factorsmentioning

confidence: 99%

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Sundblad¹,

Fürmann²,

Weidlich³

et al. 2023

2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)

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Electricity generation and demand time series often are only available on a national scale. In this contribution, we derive regionalization factors to allocate publicly available national generation and demand time series for Germany to the federalstate level. We compare two different types of regionalization approaches: Static factors are based on the regional distribution of capacities or population and GPD, whereas dynamic factors take plant-specific generation time series, regionally resolved weather patterns or compositions of different load profiles into account. We observe that dynamic regionalization factors show significant temporal variability, emphasizing the limitations of static regionalization factors for a spatio-temporally more detailed representation of power system time series.

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Section: B Dynamic Regionalization Factorsmentioning

confidence: 99%

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Sundblad¹,

Fürmann²,

Weidlich³

et al. 2023

2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)

Self Cite

View full text Add to dashboard Cite

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“…The literature presents examples of cluster analyses in which regions in Germany are clustered according to different criteria [42][43][44][45][46]. To analyze decentralized energy systems, the studies use technoeconomic energy indicators at the municipal level [47,48].…”

Section: Cluster Analysismentioning

confidence: 99%

“…With regard to decentralized energy systems, a wide range of possible parameters can be used. Typical data of decentralized energy systems are, for example, electricity, heat or fuel demand by consumption sectors; installed electricity or heat generation capacities; as well as social and geographic data, such as area, population or number of households [42,43,47]. We also included information about the potential role of hydrogen for the municipalities in NWM in our analysis (see Section 3.1).…”

Section: Cluster Analysismentioning

confidence: 99%

Modelling Decentralized Hydrogen Systems: Lessons Learned and Challenges from German Regions

et al. 2022

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Green hydrogen produced by power-to-gas will play a major role in the defossilization of the energy system as it offers both carbon-neutral chemical energy and the chance to provide flexibility. This paper provides an extensive analysis of hydrogen production in decentralized energy systems, as well as possible operation modes (H2 generation or system flexibility). Modelling was realized for municipalities—the lowest administrative unit in Germany, thus providing high spatial resolution—in the linear optimization framework OEMOF. The results allowed for a detailed regional analysis of the specific operating modes and were analyzed using full-load hours, share of used negative residual load, installed capacity and levelized cost of hydrogen to derive the operation mode of power-to-gas to produce hydrogen. The results show that power-to-gas is mainly characterized by constant hydrogen production and rarely provides flexibility to the system. Main drivers of this dominant operation mode include future demand for hydrogen and the fact that high full-load hours reduce hydrogen-production costs. However, changes in the regulatory, market and technical framework could promote more flexibility and support possible use cases for the central technology to succeed in the energy transition.

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“…As LEMs are in general not self-sufficient [23,24,25], residual demand and generation of the local areas has to be procured in the wholesale market or through backup instances such as aggregators or utilities. This raises the question of how the interaction of local and wholesale energy systems is to be organized.…”

Section: Analysis Of Lem Economicsmentioning

confidence: 99%

How will local energy markets influence the pan-European day-ahead market and transmission systems? A case study for local markets in France and Germany

et al. 2022

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Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany

Cited by 13 publications

References 43 publications

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Load and generation time series for German federal states: Static vs. dynamic regionalization factors

Modelling Decentralized Hydrogen Systems: Lessons Learned and Challenges from German Regions

How will local energy markets influence the pan-European day-ahead market and transmission systems? A case study for local markets in France and Germany

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