Ionenaustauscher

Dorfner, Konrad

doi:10.1515/9783110836615

Cited by 42 publications

(10 citation statements)

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“…Optimisation problems are formulated using the Pythonbased optimization modeling language Pyomo [53], [54], which is solver agnostic and intuitive to extend. The use of Pyomo also allows inter-operability with other energy system frameworks that use Pyomo, such as calliope [11], oemof [14] and urbs [20]. In PyPSA lower-level functions in Pyomo have been exploited to improve computational performance.…”

Section: H Planned New Featuresmentioning

confidence: 99%

“…PyPSA differs from more general energy system models such as calliope [11], oemof [14], OSeMOSYS [15] and urbs [20] by offering more detailed modelling of power networks, in particular the physics of power flow according to the impedances in the network. PyPSA can model a more general energy network using link components (see Section II-D), but cannot, for example, yet do the multiyear dynamic investment that OSeMOSYS does.…”

Section: Comparison To Other Power System Toolsmentioning

confidence: 99%

“…The consideration of multiple time periods is important on the operational side for unit commitment of conventional generators and the optimisation of storage and demand side management, and on the investment side for optimising infrastructure capacities over representative load and weather situations. Several tools have subsets of these capabilities, such as calliope [11], minpower [12], MOST [13], oemof [14], OSeMOSYS [15], PLEXOS [16], PowerGAMA [17], PRIMES [18], TIMES [19] and urbs [20], but their representations of electrical grids are often simplified.…”

Section: Introductionmentioning

confidence: 99%

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PyPSA: Python for Power System Analysis

Brown¹,

Hörsch²,

Schlachtberger

2018

JORS

386

226

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Python for Power System Analysis (PyPSA) is a free software toolbox for simulating and optimising modern electrical power systems over multiple periods. PyPSA includes models for conventional generators with unit commitment, variable renewable generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. It is designed to be easily extensible and to scale well with large networks and long time series. In this paper the basic functionality of PyPSA is described, including the formulation of the full power flow equations and the multi-period optimisation of operation and investment with linear power flow equations. PyPSA is positioned in the existing free software landscape as a bridge between traditional power flow analysis tools for steady-state analysis and full multi-period energy system models. The functionality is demonstrated on two open datasets of the transmission system in Germany (based on SciGRID) and Europe (based on GridKit).

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Section: H Planned New Featuresmentioning

confidence: 99%

Section: Comparison To Other Power System Toolsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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PyPSA: Python for Power System Analysis

Brown¹,

Hörsch²,

Schlachtberger

2018

JORS

386

226

View full text Add to dashboard Cite

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“…Since then, various other projects with different purposes have been developed (e.g. urbs [20], PyPSA [21], calliope [22]). Their focus covers the full range from power flow simulation to long-term investment models.…”

Section: Overview Of Modelling Landscapementioning

confidence: 99%

The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling

Hilpert¹,

Kaldemeyer²,

Krien³

et al. 2018

Energy Strategy Reviews

211

122

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Energy system models have become indispensable tools for planning future energy systems by providing insights into different development trajectories. However, sustainable systems with high shares of renewable energy are characterized by growing cross-sectoral interdependencies and decentralized structures. To capture important properties of increasingly complex energy systems, sophisticated and flexible modelling tools are needed. At the same time, open science is becoming increasingly important in energy system modelling. This paper presents the Open Energy Modelling Framework (oemof) as a novel approach to energy system modelling, representation and analysis. The framework provides a toolbox to construct comprehensive energy system models and has been published open source under a free licence. Through collaborative development based on open processes, the framework supports a maximum level of participation, transparency and open science principles in energy system modelling. Based on a generic graph-based description of energy systems, it is well-suited to flexibly model complex cross-sectoral systems and incorporate various modelling approaches. This makes the framework a multi-purpose modelling environment for modelling and analyzing different systems at scales ranging from urban to transnational.

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“…Alumina proved to be the rst solid catalyst in this eld [47,59]. Over time, many studies [59][60][61] have been undertaken that have specialized in the conversion of alcohols to heterogeneous catalyst systems. In this case, metal oxides such as Al 2 O 3 , O 2 , SiO 2 , and WO 3 , as well as zeolites and sul des such as MoS 2 or WS 2 in a temperature range of 100°C to 500°C were used.…”

Section: Catalysts With Brønsted Acid Sites Such As Alumina and H-zsm-5mentioning

confidence: 99%

Overview of Current and Future Perspectives of Saudi Arabian Natural Clinoptilolite Zeolite: A Case Review

Alsawalha

2019

Journal of Chemistry

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After a thorough review of existing studies of clinoptilolite zeolites, three areas for potential investigation of the Saudi Arabian zeolites were found. They are the characterizations, the catalytic activity, active sites, and uses of natural clinoptilolite zeolites. First, no analysis is available worldwide to compare the percentage weight of local zeolites with those sourced from other countries, nor does one exist for the establishment on the zeolite conversion of MBOH with water on acidic catalysts at lower temperatures. Secondly, a review of current literature on the topic revealed that basic and active sites of Saudi Arabian zeolites have yet to be examined. Future investigation of zeolite catalytic activity can be achieved by methyl butynol test reaction (MBOH) and absorption-desorption of ammonia. In the characterization of a range of international materials, the methyl butynol test reaction was utilized, including on natural zeolites, natural clays, and synthesized hydrotalcites. However, the catalytic performance of natural Saudi Arabian clinoptilolite zeolites by test reaction of MBOH conversion has not been yet investigated. Therefore, this article also includes an outline of the general testing conditions and parameters required to execute the accurate characterization of local Saudi clinoptilolite under optimal test conditions. Likewise, knowledge of the important active acidic centers of local materials is prescribed. This can be ascertained by determining the conditions together with the test parameters for the application of the “temperature-programmed desorption of ammonia” method in order to obtain an accurate determination of local Saudi clinoptilolite acidic centers. Additionally, an outline of the catalytic activity of worldwide clinoptilolite is given in this article together with kinetic investigations of other sources for the clinoptilolite zeolite in order to form the basis for the testing of local Saudi clinoptilolite. The percentage average of chemical composition (Wt.%) of natural clinoptilolite from various countries is also included. Finally, a future research plan is proposed here. This will form the basis for a complete study or survey to be compiled detailing the modifications needed to increase the surface areas for Saudi natural clinoptilolite zeolites using different methods of modifications. This could enhance its application as acid catalysts for use in the retardation of coke formation and for membrane separation on cationic exchange.

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Ionenaustauscher

Cited by 42 publications

References 0 publications

PyPSA: Python for Power System Analysis

PyPSA: Python for Power System Analysis

The Open Energy Modelling Framework (oemof) - A new approach to facilitate open science in energy system modelling

Overview of Current and Future Perspectives of Saudi Arabian Natural Clinoptilolite Zeolite: A Case Review

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