Kai Hufendiek

Head of Institute

University of Stuttgart

Germany

Research Interests

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About

Kai Hufendiek is the Head of Institute at Universität Stuttgart, Germany. His research focuses on capacity expansion models, electricity market modeling, and renewable energy integration. Recent publications explore topics such as the regulatory framework for small-scale photovoltaic systems and innovative business models for flexibility trading in the energy sector.

Articles (12)

Year:

2025

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2024

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2024

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2023

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2023

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2023

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2023

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2022

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2022

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2022

Year:

2022

Year:

2022

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Kai Hufendiek

Research Interests

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About

Articles (12)

How temporal and technological simplifications affect the performance of capacity expansion models

Iterative coupling of a fundamental electricity market model and an agent-based simulation model to reduce the efficiency gap

A Modular System Architecture for an Offshore Off-grid Platform for Climate-neutral Power-to-X Production in H<sub>2</sub>Mare

Modelling power prices in markets with high shares of renewable energies and storages—The Norwegian example

Modelling power prices in markets with high shares of renewable energies and storages-The Norwegian example

Assessment of the regulatory framework in view of effectiveness and distributional effects in the context of small-scale PV—The German experience

Assessment of the regulatory framework in view of effectiveness and distributional effects in the context of small-scale PV-The German experience

Asset Profitability in the Electricity Sector: An Iterative Approach in a Linear Optimization Model

Asset Profitability in the Electricity Sector: An Iterative Approach in a Linear Optimization Model

Business Model Language "Value Network" for Smart Business Models: A Case Study for a Platform forTrading Flexibility

Expansion or decommission? – Transformation of existing district heating networks by reducing temperature levels in a cost-optimum network design

Expansion or decommission? - Transformation of existing district heating networks by reducing temperature levels in a cost-optimum network design

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