Impacts of power sector model features on optimal capacity expansion: A comparative study

Ouwerkerk, Jonas van; Gils, Hans Christian; Gardian, Hedda; Kittel, Martin; Schill, Wolf-Peter; Zerrahn, Alexander; Murmann, Alexander; Launer, Jann; Torralba-Díaz, Laura; Bußar, Christian

doi:10.1016/j.rser.2021.112004

Cited by 21 publications

(8 citation statements)

References 31 publications

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“…Inter technology expansion costs [26]. Some examples for the sub-category iv) "generation and demand" are the annual electricity generation from different sources [27], the variable renewable energy (VRE) share [28], the electricity generation of dispatchable sources [25] or the change in sectoral electricity demand [29].…”

Section: Comparison Of Frameworkmentioning

confidence: 99%

“…Some examples for the sub-category iv) "generation and demand" are the annual electricity generation from different sources [27], the variable renewable energy (VRE) share [28], the electricity generation of dispatchable sources [25] or the change in sectoral electricity demand [29]. Some examples for the sub-category v)" installed capacities of generation sources" are VRE capacity [28], conventional power plant capacity [30], capacity expansion of peak load and based load power plants [26]. Some examples for the sub-category vi) "installed capacities of storages and transmission units" are storage capacity [31], storage expansion capacity and transmission grid expansion capacity [26].…”

Section: Comparison Of Frameworkmentioning

confidence: 99%

“…Some examples for the sub-category v)" installed capacities of generation sources" are VRE capacity [28], conventional power plant capacity [30], capacity expansion of peak load and based load power plants [26]. Some examples for the sub-category vi) "installed capacities of storages and transmission units" are storage capacity [31], storage expansion capacity and transmission grid expansion capacity [26]. Some examples for the sub-category vii) "curtailments and excess energy" are annual VRE curtailment [32], curtailment [24] and critical excess electricity production [33].…”

Section: Comparison Of Frameworkmentioning

confidence: 99%

“…The authors use a quantitative approach based on direct comparison of the following indicators: costs, generation and demand, CO 2 emissions. Ouwerkerk et al [26] compare six power system frameworks (DIETER [56], E2M2 [57], GENESYS-MOD2 [58], ISAaR [59], oemof [43,44], REMix [60]) using a quantitative approach based on direct comparison of the following indicators: installed capacities of generation sources, storage and transmission units. Siala et al [22] compare five power system frameworks using a quantitative approach based on direct comparison of the following indicators: system costs, generation and demand, installed capacities of generation sources, storage and transmission units, and CO 2 emissions.…”

Section: Comparison Of Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

Comparison methods of energy system frameworks, models and scenario results

Prina

Nastasi

Groppi

et al. 2022

Renewable and Sustainable Energy Reviews

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Section: Comparison Of Frameworkmentioning

confidence: 99%

Section: Comparison Of Frameworkmentioning

confidence: 99%

Section: Comparison Of Frameworkmentioning

confidence: 99%

Section: Comparison Of Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Comparison methods of energy system frameworks, models and scenario results

Prina

Nastasi

Groppi

et al. 2022

Renewable and Sustainable Energy Reviews

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“…This leads to a sharp increase in the amount of scheduling data in the power system [2], but due to various reasons such as sensor failures, communication failures, etc., there may be missing values in the grid scheduling data [3]. These missing values will affect the safe operation and scheduling decisions of the power system [4]. Therefore, how to accurately interpolate missing values in power grid dispatch data has become an urgent problem that needs to be solved.…”

Section: Introductionmentioning

confidence: 99%

A missing sequence interpolation algorithm for power grid dispatching data under multisource data fusion

Chen,

Zhang,

et al. 2024

Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023)

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To solve the problem of missing values in dispatching data in power system, so as to improve the security and stability of power system. This article proposes a missing sequence interpolation algorithm for power grid dispatch data based on multi-source data fusion. Firstly, preprocess the multi-source power grid dispatch data, including data normalization, data serialization, etc. Then, construct an LSTM long-term and short-term memory network model, and train the model using existing data to learn the features and patterns in the original data. Next, tensor PARAFAC decomposition is implemented through first-order partial derivatives to interpolate sequence data containing missing values. Finally, the effectiveness of the evaluation algorithm is verified through experiments. The experimental results show that the algorithm proposed in this paper has high interpolation accuracy and real-time performance, and can effectively interpolate missing values in power grid dispatch data.

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TransHyDE‐Sys: An Integrated Systemic Approach for Analyzing and Supporting the Transformation of Energy Systems and Hydrogen Infrastructure Development

Kigle,

Mohr,

Kneiske

et al. 2024

Energy Tech

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In addition to the long‐term goal of mitigating climate change, the current geopolitical upheavals heighten the urgency to transform Europe's energy system. This involves expanding renewable energies while managing intermittent electricity generation. Hydrogen is a promising solution to balance generation and demand, simultaneously decarbonizing complex applications. To model the energy system's transformation, the project TransHyDE‐Sys, funded by the German Federal Ministry of Education and Research, takes an integrated approach beyond traditional energy system analysis, incorporating a diverse range of more detailed methods and tools. Herein, TransHyDE‐Sys is situated within the recent policy discussion. It addresses the requirements for energy system modeling to gain insights into transforming the European hydrogen and energy infrastructure. It identifies knowledge gaps in the existing literature on hydrogen infrastructure‐oriented energy system modeling and presents the research approach of TransHyDE‐Sys. TransHyDE‐Sys analyzes the development of hydrogen and energy infrastructures from “the system” and “the stakeholder” perspectives. The integrated modeling landscape captures temporal and spatial interactions among hydrogen, electricity, and natural gas infrastructure, providing comprehensive insights for systemic infrastructure planning. This allows a more accurate representation of the energy system's dynamics and aids in decision‐making for achieving sustainable and efficient hydrogen network development integration.

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Impacts of power sector model features on optimal capacity expansion: A comparative study

Cited by 21 publications

References 31 publications

Comparison methods of energy system frameworks, models and scenario results

Comparison methods of energy system frameworks, models and scenario results

A missing sequence interpolation algorithm for power grid dispatching data under multisource data fusion

TransHyDE‐Sys: An Integrated Systemic Approach for Analyzing and Supporting the Transformation of Energy Systems and Hydrogen Infrastructure Development

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