The value of network resolution – A validation study of the European energy system model PyPSA-Eur

Unnewehr, Jan Frederick; Schäfer, Mirko; Weidlich, Anke

doi:10.1109/osmses54027.2022.9769123

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…For each federal state, the national hourly demand timeseries is distributed proportionally to a weighted share of 60 % GDP and 40 % population. This weighting has been suggested in [15] based on a linear regression and has also been used in [23].…”

Section: A Static 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: A Static 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

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“…PyPSA has been used to model the European network through the PyPSA-Eur project [12]. This model has already been used and validated in a series of studies [5], [18], [19]. In this case study, the default version of the model [20] is used with a few modifications.…”

Section: Case Studymentioning

confidence: 99%

Impacts of spatial and temporal resolutions on the near-optimal spaces of energy system optimisation models

Dubois,

Ernst

2023

2023 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE)

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Over the past years, the rising penetration of renewable energy in power systems has led to the need for more detailed energy system models. Specifically, spatial and temporal resolutions have become increasingly important, and multiple studies have investigated their impact on the optimal solutions to energy system optimisation problems. However, these studies have yet to be conducted for near-optimal solutions, which can provide valuable insights to decision-makers. This paper aims to initiate this research by examining the effects of spatial and temporal resolutions on the values of necessary conditions for near-optimality. In particular, we investigate how spatiotemporal resolution changes affect minimal capacity investments in the European electricity grid. Our analysis leads to three key observations. Firstly, we show that minimal capacities for nearoptimality exhibit similar trends to optimal capacities when each resolution varies. Secondly, the resolutions that result in higher optimal capacities are also the ones where minimal capacities deviate the least from the optimal capacities. Thirdly, as a result of the second observation, spatial or temporal resolution changes have a greater impact on minimal capacities for near-optimality than on optimal capacities. We conclude by suggesting solutions to expand this research track and gain a deeper understanding of the impact of spatiotemporal resolution on near-optimal spaces.

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“…Note that load shedding is below 0.01% of the annual demand for every simulated scenario. Using the PyPSA-EUR model at a high spatio-temporal resolution has proven to reproduce historical results, particularly the amount of renewable curtailment (Frysztacki and Brown 2020) and conventional power generation patterns as well as wind and solar production (Unnewehr et al 2022). Therefore, the model provides a sound basis for investigating net load.…”

Section: Datamentioning

confidence: 99%

Net load forecasting using different aggregation levels

Beichter

Phipps²,

Frysztacki³

et al. 2022

Energy Inform

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In the electricity grid, constantly balancing the supply and demand is critical for the network’s stability and any expected deviations require balancing efforts. This balancing becomes more challenging in future energy systems characterised by a high proportion of renewable generation due to the increased volatility of these renewables. In order to know when any balancing efforts are required, it is essential to predict the so-called net load, the difference between forecast energy demand and renewable supply. Although various forecasting approaches exist for both the individual components of the net load and the net load itself, it is unclear if it is more beneficial to aggregate several specialised forecasts to obtain the net load or to aggregate the input data to forecast the net load with one approach directly. Therefore, the present paper compares three net load forecasting approaches that exploit different levels of aggregation. We compare an aggregated strategy that directly forecasts the net load, a partially aggregated strategy that forecasts demand and supply separately, and a disaggregated strategy that forecasts demand and supply from each generator separately. We evaluate the forecast performance of all strategies with a simple and a complex forecasting model, both for deterministic and probabilistic forecasts, using one year of data from a simulated realistic future energy system characterised by a high share of renewable energy sources. We find that the partially aggregated strategy performs best, suggesting that a balance between specifically tailored forecasting models and aggregation is advantageous.

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The value of network resolution – A validation study of the European energy system model PyPSA-Eur

Cited by 5 publications

References 11 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

Impacts of spatial and temporal resolutions on the near-optimal spaces of energy system optimisation models

Net load forecasting using different aggregation levels

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