A Top-Down Spatially Resolved Electrical Load Model

Robinius, Martin; Stein, F.; Stolten, Detlef; Schwane, Adrien

doi:10.3390/en10030361

Cited by 21 publications

(11 citation statements)

References 22 publications

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“…To enforce a large share of renewable energy, the electricity supply of the backup power plant is limited to 10% of the overall electricity consumption. The time series of the wind turbines and electricity load are drawn from Robinius et al [55] [56,57] and the photovoltaic feed-in is simulated with the PV-lib as well.…”

Section: Energy System Modeling Frameworkmentioning

confidence: 99%

Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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The optimization-based design of renewable energy systems is a computationally demanding task because of the high temporal fluctuation of supply and demand time series. In order to reduce these time series, the aggregation of typical operation periods has become common. The problem with this method is that these aggregated typical periods are modeled independently and cannot exchange energy. Therefore, seasonal storage cannot be adequately taken into account, although this will be necessary for energy systems with a high share of renewable generation.To address this issue, this paper proposes a novel mathematical description for storage inventories based on the superposition of inter-period and intra-period states. Interperiod states connect the typical periods and are able to account their sequence. The approach has been adopted for different energy system configurations. The results show that a significant reduction in the computational load can be achieved also for long term storage-based energy system models in comparison to optimization models based on the full annual time series.

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Section: Energy System Modeling Frameworkmentioning

confidence: 99%

Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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“…This, along with the high present cost of synthetic methane and hydrogen [4], are from today's perspective a factor that makes instant implementation implausible. However, in numerous studies, such as Sternberg 2015, Robinius 2015, ETG-Task-Force 2012, DLR 2012 and Prognos AG 2012, about the future energy system in Germany, it is assumed that both the massive expansion of renewable power generation and the availability of large quantities of excess power will also lead to a reduction in costs [8,[49][50][51][52][53]. Through the use of alternative technologies for steelmaking, future energy systems could also be supported because the released power generation capacity of the integrated steelworks could be used as backup power to ensure security of supply in a strong share of fluctuating renewable energies for power generation.…”

Section: Discussionmentioning

confidence: 99%

Power-to-Steel: Reducing CO2 through the Integration of Renewable Energy and Hydrogen into the German Steel Industry

et al. 2017

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This paper analyses some possible means by which renewable power could be integrated into the steel manufacturing process, with techniques such as blast furnace gas recirculation (BF-GR), furnaces that utilize carbon capture, a higher share of electrical arc furnaces (EAFs) and the use of direct reduced iron with hydrogen as reduction agent (H-DR). It is demonstrated that these processes could lead to less dependence on-and ultimately complete independence from-coal. This opens the possibility of providing the steel industry with power and heat by coupling to renewable power generation (sector coupling). In this context, it is shown using the example of Germany that with these technologies, reductions of 47-95% of CO 2 emissions against 1990 levels and 27-95% of primary energy demand against 2008 can be achieved through the integration of 12-274 TWh of renewable electrical power into the steel industry. Thereby, a substantial contribution to reducing CO 2 emissions and fuel demand could be made (although it would fall short of realizing the German government's target of a 50% reduction in power consumption by 2050).

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“…Eurostat provides present country aggregated sums [38] of electricity consumption in households per end-use, which need to be disaggregated to NUTS3 regions. Spatial disaggregation is performed similar to [39] or [40] via the census 2011 population data available on Eurostat [30], assuming a direct correlation between population size and electricity consumption. We present the consumption per end-use and country used in this work in Table A2 in the Appendix A.…”

Section: Annual Consumptionmentioning

confidence: 99%

A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

2021

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End-use specific residential electricity load profiles are of interest for energy system modelling that requires future load curves or demand-side management. We present a model that is applicable across countries to predict consumption on a regional and national scale, using openly available data. The model uses neural networks (NNs) to correlate measured consumption from one country (United Kingdom) with weather data and daily profiles of a mix of human activity and device specific power profiles. We then use region-specific weather data and time-use surveys as input for the trained NNs to predict unscaled electric load profiles. The total power profile consists of the end-use household load profiles scaled with real consumption. We compare the model’s results with measured and independently simulated profiles of various European countries. The NNs achieve a mean absolute error compared with the average load of 6.5 to 33% for the test set. For Germany, the standard deviation between the simulation, the standard load profile H0, and measurements from the University of Applied Sciences Berlin is 26.5%. Our approach reduces the amount of input data required compared with existing models for modelling region-specific electricity load profiles considering end-uses and seasonality based on weather parameters. Hourly load profiles for 29 European countries based on four historical weather years are distributed under an open license.

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A Top-Down Spatially Resolved Electrical Load Model

Cited by 21 publications

References 22 publications

Time series aggregation for energy system design: Modeling seasonal storage

Time series aggregation for energy system design: Modeling seasonal storage

Power-to-Steel: Reducing CO2 through the Integration of Renewable Energy and Hydrogen into the German Steel Industry

A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles

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