Comparison of different model formulations for modelling future power systems with high shares of renewables – The Dispa-SET Balkans model

Pavičević, Matija; Kavvadias, Konstantinos; Pukšec, Tomislav; Quoilin, Sylvain

doi:10.1016/j.apenergy.2019.113425

Cited by 30 publications

(12 citation statements)

References 52 publications

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“…Due to vast amount of generation units in the EU system, some clustering techniques are applied to ensure computational tractability. The clustering techniques used within this study have been discussed in more detail in a previous publication [44], where an optimal level of clustering was determined as a trade-off between accuracy and computation time. This clustering is referred to as "Per-typical technology" clustering and consists of grouping similar individual units into clusters of N units which share the same characteristics.…”

Section: Power Plant Fleetmentioning

confidence: 99%

The potential of sector coupling in future European energy systems: Soft linking between the Dispa-SET and JRC-EU-TIMES models

et al. 2020

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The relevance of sector coupling is increasing when shifting from the current highly centralised and mainly fossil fuel-based energy system to a more decentralized and renewable energy system. Cross-sectoral linkages are already recognized as a cost-effective decarbonisation strategy that provides significant flexibility to the system. Modelling such cross-sectoral interconnections is thus highly relevant. In this work, these interactions are considered in a long-term perspective by uni-directional soft-linking of two models: JRC-EU-TIMES, a long term planning multisectoral model, and Dispa-SET, a unit commitment and optimal dispatch model covering multiple energy sectors such as power, heating & cooling, transportation etc. The impact of sector coupling in future Europe-wide energy systems with high shares of renewables is evaluated through five scenarios. Results show that the contributions of individual sectors are quite diverse. The transport sector provides the highest flexibility potential in terms of power curtailment, load shedding, congestion in the interconnection lines

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Section: Power Plant Fleetmentioning

confidence: 99%

The potential of sector coupling in future European energy systems: Soft linking between the Dispa-SET and JRC-EU-TIMES models

et al. 2020

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“…One of these examples was published by Alzate et al [195], who used spectral clustering with an out-of-sample extension to cluster customer profiles for electricity demand. For this, 123 time series were used for training and 122 for validation.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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Due to the high degree of intermittency of renewable energy sources (RES) and the growing interdependences amongst formerly separated energy pathways, the modeling of adequate energy systems is crucial to evaluate existing energy systems and to forecast viable future ones. However, this corresponds to the rising complexity of energy system models (ESMs) and often results in computationally intractable programs. To overcome this problem, time series aggregation (TSA) is frequently used to reduce ESM complexity. As these methods aim at the reduction of input data and preserving the main information about the time series, but are not based on mathematically equivalent transformations, the performance of each method depends on the justifiability of its assumptions. This review systematically categorizes the TSA methods applied in 130 different publications to highlight the underlying assumptions and to evaluate the impact of these on the respective case studies. Moreover, the review analyzes current trends in TSA and formulates subjects for future research. This analysis reveals that the future of TSA is clearly feature-based including clustering and other machine learning techniques which are capable of dealing with the growing amount of input data for ESMs. Further, a growing number of publications focus on bounding the TSA induced error of the ESM optimization result. Thus, this study can be used as both an introduction to the topic and for revealing remaining research gaps.

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“…The short-term Dispa-SET UCM module optimizes the power dispatch of individual generator units, energy flows between interconnected zones, variable renewable energy source (VRES) curtailment, shed load, carbon emissions, interactions between water and energy sector, shadow prices, water values etc. It is expressed as a MILP problem and integrates diverse clustering techniques and a rolling horizon optimization to ensure computational tractability [18]. The model outputs are then analysed, and relevant water-energy nexus metrics are computed.…”

Section: Power System Modelmentioning

confidence: 99%

“…A detailed formulation of the Dispa-SET model is out of the scope of this paper. All relevant equations and constraints can be found in [19], and detailed description of the clustering approaches is further discussed here [18]. The main constraints of the model are as follows:…”

Section: Power System Modelmentioning

confidence: 99%