Abstract
BackgroundThe transition to a sustainable future challenges the current energy grids with the integration of variable, distributed renewable energy sources. On a technical level, multi-energy systems may provide the necessary flexibility to minimise the gap between demand and supply. Suitable methods and tools are necessary to derive relevant results and to support a transition to renewable energy sources. While several, dedicated tools to model grids and infrastructure of single-energy carriers exist, there are no tools capable of modelling multi-energy systems in detail. Thus, this paper presents the necessary aspects to consider when modelling grid-based multi-energy systems, presents three open source frameworks for modelling grid-based energy systems and points out the major challenges.MethodologyThe current main aspects and challenges for modelling grid-based energy systems are derived from a literature review. Three open source multi-energy modelling frameworks (Calliope, oemof, urbs) are presented, and the extent to which they consider these aspects and how they tackle challenges is analysed.Grid-based MES modellingWe identified five general energy system modelling aspects (modelling scope, model formulation, spatial coverage, time horizon, data) and three aspects specific to modelling energy grids (level of detail, spatial resolution, temporal resolution). While the specific aspects mainly influence the representation of the technical parts of the energy system and the computational effort, the general aspects primarily relate to the system boundaries and scope of the model. For the evaluation of the modelling results, we identified several assessment criteria, including economic, energetic, exergetic and reliability. Each of the studied open source modelling frameworks provides generic capabilities to model energy converters, and the electricity, gas and district heat networks. However, the general and specific aspects present respective challenges. Relating to the general aspects, complexity of model formulation increases when including additional boundary conditions. The accuracy of the results is also dependent on data quality. Temporal and spatial resolutions are the major specific challenges for modelling the energy infrastructure.ConclusionsThere is still a broad field of opportunities for researchers to contribute to grid-based energy system modelling. This encompasses especially the consideration of short- and long-term dynamics of renewable energy sources in planning models.
Highlights
The transition to a sustainable future challenges the current energy grids with the integration of variable, distributed renewable energy sources
Mathematical methods like linear programming developed for operations research in the Second World War [9, 10] were used to create models which allowed the formalisation of scattered knowledge about complex interactions in the energy sector and helped analysts to understand a sector that had become complex [11]
We focus on integrated, grid-based multi-energy system (MES) for three main reasons: (1) for a decarbonisation of the global energy system, fossil fuels must be substituted by renewable electricity [20], (2) the integration of fluctuating renewable energy sources (RES) is especially a challenge for the electricity grid [21] and (3) an integrated MES approach supports a better utilisation of volatile RES and existing grid infrastructures [22]
Summary
The transition to a sustainable future challenges the current energy grids with the integration of variable, distributed renewable energy sources. This paper presents the necessary aspects to consider when modelling grid-based multi-energy systems, presents three open source frameworks for modelling grid-based energy systems and points out the major challenges. Methodology: The current main aspects and challenges for modelling grid-based energy systems are derived from a literature review. Three open source multi-energy modelling frameworks (Calliope, oemof, urbs) are presented, and the extent to which they consider these aspects and how they tackle challenges is analysed. Conclusions: There is still a broad field of opportunities for researchers to contribute to grid-based energy system modelling. This encompasses especially the consideration of short- and long-term dynamics of renewable energy sources in planning models. Mathematical methods like linear programming developed for operations research in the Second World War [9, 10] were used to create models which allowed the formalisation of scattered knowledge about complex interactions in the energy sector and helped analysts to understand a sector that had become complex [11]
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