A general spatio-temporal model of energy systems with a detailed account of transport and storage

Samsatli, Sheila

doi:10.1016/j.compchemeng.2015.05.019

Cited by 86 publications

(48 citation statements)

References 36 publications

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“…Developing an energy system design that minimizes costs and environmental impact is a complex task due to the spatial and temporal gap between energy production and demand. In consequence, optimization algorithms are required for solving these design problems [1,2,3,4,5,6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Impact of different time series aggregation methods on optimal energy system design

et al. 2018

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Modelling renewable energy systems is a computationally-demanding task due to the high fluctuation of supply and demand time series. To reduce the scale of these, this paper discusses different methods for their aggregation into typical periods. Each aggregation method is applied to a different type of energy system model, making the methods fairly incomparable.To overcome this, the different aggregation methods are first extended so that they can be applied to all types of multidimensional time series and then compared by applying them to different energy system configurations and analyzing their impact on the cost optimal design.It was found that regardless of the method, time series aggregation allows for significantly reduced computational resources. Nevertheless, averaged values lead to underestimation of the real system cost in comparison to the use of representative periods from the original time series. The aggregation method itself e.g., k-means clustering plays a minor role. More significant is the system considered: Energy systems utilizing centralized resources require fewer typical periods for a feasible system design in comparison to systems with a higher share of renewable feed-in. Furthermore, for energy systems based on seasonal storage, currently existing models integration of typical periods is not suitable.

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Section: Introductionmentioning

confidence: 99%

Impact of different time series aggregation methods on optimal energy system design

et al. 2018

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“…QESMAT is a linear programming framework that builds on the Resource-Technology Network model developed by our research group [22]. The energy system is represented as a set of 'technologies' that can produce, transform, or consume a set of 'resources', constrained by production and demand balances at each time slice.…”

Section: Optimizationmentioning

confidence: 99%

Optimizing Qatar’s energy system for a post-carbon future

Bohra

Shah

2019

Energy Transit

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Global decarbonization efforts, along with domestic pressures to diversify the economy, have created challenges and opportunities for the Qatari energy system. The government is focused on diversifying the national economy away from hydrocarbons, encouraging sustainable use of resources, and ensuring the security of food, energy, and water systems. Our optimization framework allows policymakers to apply a systems approach to the overall energy infrastructure in Qatar, covering a range of sectors such as industry, residential infrastructure, transportation, and agriculture. Our aims are twofold: first, to develop an open-source tool that can be used for national-level planning and policymaking, and second to use this tool to generate key technology and policy insights that can aid the transition of Qatari energy infrastructure in the long term. Our results provide a blueprint for a cross-sectoral energy transformation: from greater use of low-carbon transport such as electric cars and public transit, to grid-scale adoption of solar energy and reverse osmosis for desalination. Liquefied natural gas is not expected to remain the most economical export, instead being replaced by hydrogen obtained from the steam reforming of natural gas. We have modeled a long-term domestic divestment from hydrocarbon exports and have shown that the country can still retain significant economic wealth in a post-carbon world, thus maintaining existing political, economic, and social structures.

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“…The considered pipeline diameters and resulting specific investment costs are given in Table 1. According to Samsatli et al [29], the gas velocity inside pipelines is not only limited by pressure drop, but also by mechanical interferences. Excessively high gas velocities could lead to erosional behavior or high noise emissions.…”

Section: Pipeline Design Modelingmentioning

confidence: 99%

Modeling hydrogen networks for future energy systems: A comparison of linear and nonlinear approaches

Reuß

Welder

Thürauf

et al. 2019

International Journal of Hydrogen Energy

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Common energy system models that integrate hydrogen transport in pipelines typically simplify fluid flow models and reduce the network size in order to achieve solutions quickly. This contribution analyzes two different types of pipeline network topologies (namely, star and tree networks) and two different fluid flow models (linear and nonlinear) for a given hydrogen capacity scenario of electrical reconversion in Germany to analyze the impact of these simplifications. For each network topology, robust demand and supply scenarios are generated. The results show that a simplified topology, as well as the consideration of detailed fluid flow, could heavily influence the total pipeline investment costs. For the given capacity scenario, an overall cost reduction of the pipeline costs of 37% is observed for the star network with linear cost compared to the tree network with nonlinear fluid flow. The impact of these improvements regarding the total electricity reconversion costs has led to a cost reduction of 1.4%, which is fairly small. Therefore, the integration of nonlinearities into energy system optimization models is not recommended due to their high computational burden. However, the applied method for generating robust demand and supply scenarios improved the credibility and robustness of the network topology, while the simplified fluid flow consideration can lead to infeasibilities. Thus, we suggest the utilization of the nonlinear model for postprocessing to prove the feasibility of the results and strengthen their credibility, while retaining the computational performance of linear modeling.

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A general spatio-temporal model of energy systems with a detailed account of transport and storage

Cited by 86 publications

References 36 publications

Impact of different time series aggregation methods on optimal energy system design

Impact of different time series aggregation methods on optimal energy system design

Optimizing Qatar’s energy system for a post-carbon future

Modeling hydrogen networks for future energy systems: A comparison of linear and nonlinear approaches

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