State-of-the-art modeling approaches for energy system planning reflect the interdependencies across demand sectors such as buildings, transport, and industry, the supply sector, and the transformation sector. However, modeling continues to be hindered by the fragmentation of data sources, inconsistent formats, and the extensive effort required for data acquisition and harmonization. To address these challenges, this paper presents the open dataset SEDOS, tailored for technologically-detailed modeling of the German energy system. SEDOS offers a comprehensive, structured, and up-to-date collection of data across all energy sectors, supporting cross-sectoral analyses. The dataset is based on a relational model structure that ensures consistency between technologies, commodities, and sectors, and is accessible through an interactive online dashboard. SEDOS includes scenario data up to the year 2070, hourly time series, and a detailed representation of more than 2000 technologies. On the demand side, the dataset records the development of energy services and products for the supply of which a broad technology portfolio is characterized in terms of its technical and economic parameters. While focused on Germany, the dataset incorporates a European perspective for the power sector to enable cross-border market assessments. A key novelty of SEDOS is its alignment with the FAIR principles (Findable, Accessible, Interoperable, Reusable), supporting transparency, reusability, and integration into diverse modeling frameworks, which lowers the entry barrier for modelers and facilitates consistent and reproducible energy system analyses. This paper outlines the methodology behind the dataset, presents its structure and contents, and describes instructions for use in long-term energy system planning and sector integration studies.
Efforts to decarbonise the transport sector lead to an increasing share of electric vehicles (EVs), which can impose significant stress on future distribution grids. The level of stress is thereby not only dependent on the number of integrated EVs but also the existing topology of the grid and present shares of supply and demand units. These factors are also likely to influence strategies to help the integration of EVs, such as utilising EV flexibility through smart charging. We therefore examine the influence of different charging strategies on distinct distribution grid types, namely PV-, wind- and load-dominated grids. With the help of a quadratic problem formulation including linearised AC power flow constraints, we deduce optimised charging profiles to minimise the additional stress on the grids. In reality, a central optimisation of all EVs might be challenging to implement. We therefore compare the results to rule-based charging strategies to extract suggestions for the effective integration of EVs. Our analysis shows that the potential of the examined charging strategies to decrease curtailment that is necessary to solve arising grid issues is limited. However, the additional grid expansion costs caused by EV integration, which mainly occur in the low voltage (LV), can be reduced significantly compared to the reference charging, where EV charging is mostly uncontrolled. Our results also show that the charging strategies prove to be differently useful in the different types of grids. This stresses the importance of considering various grid topologies when investigating the influence of smart charging on distribution grids.
Industrial Demand Response (IDR) presents a vital opportunity to enhance energy system flexibility, especially as the share of variable renewable energy continues to grow. Our paper studies the economic benefits of IDR in Finland's pulp and paper industry, which accounts for nearly half of the country's industrial electricity consumption. A load-shifting demand response model was developed using the open energy modelling framework (oemof) to represent the entire sector as a single flexible unit. The model simulates demand response participation across various electricity price scenarios and intervention periods, including stress scenarios based on the 2022 energy crisis. Our findings reveal that IDR can reduce peak electricity demand by up to 30 % and cut system costs for the industry by several million euros. Critically, DR's economic viability is profoundly amplified under high price volatility, relative cost reductions surged from 4 % with 2019 day-ahead prices to 7 % and 10 % with 2022 day-ahead and intraday prices, respectively. This substantial increase positions IDR as a financial resilience strategy during market stress. Furthermore, our analysis indicates that under extreme volatility, optimisation priorities shift towards holistic energy cost reduction rather than solely peak demand minimisation. Our work uniquely quantifies IDR's external economic amplification driven by acute market price volatility, providing empirical evidence of its immediate value. This distinguishes our work from existing literature that primarily focuses on internal operational constraints, specific production technology choices, or broad long-term system-level benefits. Despite limitations including deterministic modelling and industry aggregation, our study highlights the significant, yet untapped, potential for IDR in Finland's energy transition, underscoring the necessity of supportive policy and market incentives.
Simulation models have provided reliable simulation pathways for planning the Global and national energy transition. While simulation tools have gained prominence in providing transition insights to modelling the energy paradigm of developed countries, their developing country counterparts have yet to gain a clear transition pathway. This could be due to the cost of commercial tools, technical know-how, and the complex attributes and underdevelopment of developing countries’ energy systems transition paradigm. Open-source energy simulation modelling tools (OsEMT) could help developing countries gain reliable insight and understand their modern energy transition paradigm and mitigate modelling bottlenecks posed by proprietary modelling tools. The current study aims to review open-source energy models suitable for emerging economies and conduct a compatibility assessment of OsEMT for modelling the energy transitions of Developing Countries. To do this, a review of 11 OsEMT was carried out based on information and secondary data from the applications of the models and how they have captured and obtained modelling insights about 33 thematic attributes of developing countries’ energy systems. A compatibility and suitability assessments were conducted for the investigation through a weighted scoring matrix and ranking. Results showed that most open-source energy models have been effectively and successfully applied to model the energy transition paradigm of developed countries. However, a few open-source models have ranked higher and are suitable for developing countries. OSeMOSYS, OEMOF and PyPSA were among the most compatible and suitable OsEMT for gaining reliable insights and modelling developing countries’ energy transition pathways, although OSeMOSYS is ranked highest. Recommendations for further research and holistic support for open-source energy modelling tools were provided.
