Spatio-temporal optimization of a future energy system for power-to-hydrogen applications in Germany

Welder, Lara; Robinius, Martin; Kotzur, Leander; Grube, Thomas; Stolten, Detlef; Ryberg, Severin David

doi:10.1016/j.energy.2018.05.059

Cited by 196 publications

(102 citation statements)

References 36 publications

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“…The number of constraints is thereby reduced from 2xN i xN g to 2x(N k xN g + N i ) while adding 2xN g additional variables. An application of the approach can be found in Welder et al [64]. 18…”

Section: Appendix B Simplification Of the Storage Operation Restrictmentioning

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: Appendix B Simplification Of the Storage Operation Restrictmentioning

confidence: 99%

Time series aggregation for energy system design: Modeling seasonal storage

et al. 2018

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“…The building optimization is based on a typical Mixed-Integer Linear Program (MILP) with the objective of minimizing the annual energy cost of a single building as proposed in the vast literature. The operation and design of the supply system is modeled with the object-oriented system modeling framework FINE [65,71,72]. The binary variables are considered to sufficiently incorporate the economy of scale of the technologies.…”

Section: Optimizing Structure Scale and Operationmentioning

confidence: 99%

Bottom-up energy supply optimization of a national building stock

Kotzur¹,

Markewitz²,

Robinius³

et al. 2020

Energy and Buildings

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The installation and operation distributed energy resources (DER) and the electrification of the heat supply significantly changes the interaction of the residential building stock with the grid infrastructure. Evaluating the mass deployment of DER at the national level would require analyzing millions of individual buildings, entailing significant computational burden.To overcome this, this work proposes a novel bottom-up model that consists of an aggregation algorithm to create a spatially distributed set of typical residential buildings from census data. Each typical building is then optimized with a Mixed-Integer Linear Program to derive its cost optimal technology adoption and operation, determining its changing grid load in future scenarios.The model is validated for Germany, with 200 typical buildings considered to sufficiently represent the diversity of the residential building stock. In a future scenario for 2050, photovoltaic and heat pumps are predicted to be the most economically and ecologically robust supply solutions for the different building types. Nevertheless, their electricity generation and demand temporally do not match, resulting in a doubling of the peak electricity grid load in the rural areas during the winter. The urban areas can compensate this with efficient co-generation units, which are not cost-efficient in the rural areas.

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“…In order to achieve reasonable model run times, time series aggregation [31,32] is applied to aggregate the hourly resolution of one year for all demand and VRES generation time series data to 30 representative days, each with 24 hourly values. Further details about the underlying optimization methodology are thoroughly discussed by Welder et al [9], including the mathematical representation of the framework and the constraints. Figure 1 shows the model input and output, as well as the representation of the exemplary system chosen for this analysis.…”

Section: Model Approachmentioning

confidence: 99%

Impact of Different Weather Years on the Design of Hydrogen Supply Pathways for Transport Needs

Caglayan¹,

Heinrichs²,

Linßen³

et al. 2019

Preprint

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Renewable energy sources (RES) will play a crucial role in future sustainable energy systems. In scenarios analyzing future energy system designs, a detailed spatial and temporal representation of renewable-based electricity generation is essential. For this, sufficiently representative weather data are required. Most analyses performed in this context use the historical data of either one specific reference year or an aggregation of multiple years. In contrast, this study analyzes the impact of different weather years based on historical weather data from 1980 through 2015 in accordance with the design of an exemplary future energy system. This exemplary energy system consists of on-and offshore wind energy for power-to-hydrogen via electrolysis, including hydrogen pipeline transport for most southwestern European countries. The assumed hydrogen demand for transportation needs represents a hypothetical future market penetration for fuel cellelectric vehicles of 75%. An optimization framework is used in order to evaluate the resulting system design with the objective function of minimizing the total annual cost (TAC) of the system. For each historical weather year, the applied optimization model determines the required capacities and operation of wind power plants, electrolyzers, storage technologies and hydrogen pipelines to meet the assumed future hydrogen demand in a highly spatially-and temporally-detailed manner, as well as the TAC of the system. Following that, the results of every individual year are compared in terms of installed capacities, overall electricity generation and connection to the transmission network, as well as the cost of these components within each region. The results reveal how sensitive the final design of the exemplary system is to the choice of the weather year. For example, the TAC of the system changes by up to 20% across two consecutive weather years. Furthermore, significant variation in the optimization results regarding installed capacities per region with respect to the choice of weather years can be observed.

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Spatio-temporal optimization of a future energy system for power-to-hydrogen applications in Germany

Cited by 196 publications

References 36 publications

Time series aggregation for energy system design: Modeling seasonal storage

Time series aggregation for energy system design: Modeling seasonal storage

Bottom-up energy supply optimization of a national building stock

Impact of Different Weather Years on the Design of Hydrogen Supply Pathways for Transport Needs

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