Carpe diem: A novel approach to select representative days for long-term power system modeling

Nahmmacher, Paul; Schmid, Eva; Hirth, Lion; Knopf, Brigitte

doi:10.1016/j.energy.2016.06.081

Cited by 226 publications

(161 citation statements)

References 10 publications

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“…Clustering has been extensively studied for multiple applications in various fields, including power systems. It has been for example used in the context of grid expansion planning in [4], national energy system planning [5] [6] and unit commitment models [7].…”

Section: State Of the Art And Contributionmentioning

confidence: 99%

Clustering methods assessment for investment in zero emission neighborhoods’ energy system

Pinel¹

2020

International Journal of Electrical Power & Energy Systems

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This paper investigates the use of clustering in the context of designing the energy system of Zero Emission Neighborhoods (ZEN). ZENs are neighborhoods who aim to have net zero emissions during their lifetime. While previous work has used and studied clustering for designing the energy system of neighborhoods, no article dealt with neighborhoods such as ZEN, which have high requirements for the solar irradiance time series, include a CO2 factor time series and have a zero emission balance limiting the possibilities. To this end several methods are used and their results compared. The results are on the one hand the performances of the clustering itself and on the other hand, the performances of each method in the optimization model where the data is used. Various aspects related to the clustering methods are tested. The different aspects studied are: the goal (clustering to obtain days or hours), the algorithm (k-means or k-medoids), the normalization method (based on the standard deviation or range of values) and the use of heuristic. The results highlight that k-means offers better results than k-medoids and that k-means was systematically underestimating the objective value while k-medoids was constantly overestimating it. When the choice between clustering days and hours is possible, it appears that clustering days offers the best precision and solving time. The choice depends on the formulation used for the optimization model and the need to model seasonal storage. The choice of the normalization method has the least impact, but the range of values method show some advantages in terms of solving time. When a good representation of the solar irradiance time series is needed, a higher number of days or using hours is necessary. The choice depends on what solving time is acceptable.

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Section: State Of the Art And Contributionmentioning

confidence: 99%

Clustering methods assessment for investment in zero emission neighborhoods’ energy system

Pinel¹

2020

International Journal of Electrical Power & Energy Systems

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“…To obtain a solution that accounts for both a cost‐efficient generation expansion path and compliance with system security constraints, it is necessary to develop models that maximize the representativeness of consumption and feed‐in patterns. This can be achieved by integrating a spatial component by means of multiple regions . Applying such advanced temporal clustering methods can increase the validity of previously discussed optimization models, such as PERSEUS.…”

Section: Challenges Of Modeling Electricity Network In Energy‐systemmentioning

confidence: 99%

Meeting the Modeling Needs of Future Energy Systems

et al. 2017

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Structural changes in the energy sector have created considerable challenges for regulators, energy consumers, and suppliers. Energy researchers rely on quantitative modeling approaches to address these challenges. At the Karlsruhe Institute of Technology, we have developed several models to analyze today's and the future's energy markets to address the challenges for electricity networks as a result of intermittent renewable and decentralized power and heat supply and to find solutions for integrating demand response. This paper presents a survey of these approaches and discusses further challenges. The developed models have proved to be suitable for answering a range of research questions related to structural changes. As the energy sector remains in a constant state of flux, these models need to be further developed. This means coupling different energy sectors and markets and improving their technical accuracy as well as their spatial and temporal resolutions.

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“…The new POLES (Mima, 2016) power module now includes several forms of storage technologies as well as load shedding and curtailment of surplus power (Després et al, in this issue). Each region has an endogenous RLDC of 648 time-slices built from demand, wind and solar variations.…”

Section: Polesmentioning

confidence: 99%

System Integration of Wind and Solar Power in Integrated Assessment Models: A Cross-Model Evaluation of New Approaches

et al. 2017

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Mitigation-Process Integrated Assessment Models (MP-IAMs) are used to analyze long-term transformation pathways of the energy system required to achieve stringent climate change mitigation targets. Due to their substantial temporal and spatial aggregation, IAMs cannot explicitly represent all detailed challenges of integrating the variable renewable energies (VRE) wind and solar in power systems, but rather rely on parameterized modeling approaches. In the ADVANCE project, six international modeling teams have developed new approaches to improve the representation of power sector dynamics and VRE integration in IAMs. In this study, we qualitatively and quantitatively evaluate the last years' modeling progress and study the impact of VRE integration modeling on VRE deployment in IAM scenarios. For a comprehensive and transparent qualitative evaluation, we first develop a framework of 18 features of power sector dynamics and VRE integration. We then apply this framework to the newlydeveloped modeling approaches to derive a detailed map of strengths and limitations of the different approaches. For the quantitative evaluation, we compare the IAMs to the detailed hourlyresolution power sector model REMIX. We find that the new modeling approaches manage to represent a large number of features of the power sector, and the numerical results are in reasonable agreement with those derived from the detailed power sector model. Updating the power sector representation and the cost and resources of wind and solar substantially increased wind and solar shares across models: Under a carbon price of 30$/tCO2 in 2020 (increasing by 5% per year), the model-average cost-minimizing VRE share over the period 2050-2100 is 62% of electricity generation, 24%-points higher than with the old model version. Highlights: We develop a comprehensive framework to evaluate power sector modeling in IAMs  We evaluate 6 new modeling approaches to represent variability of wind and solar  Most IAMs now represent key power sector dynamics, as shown by hourly model REMIX  Previous integration modeling was in many of the analyzed IAMs too restrictive  IAMs with new approaches show on average 24%-points higher wind/solar shares than before Keywords:Integrated assessment models (IAM); variable renewable energy (VRE); wind and solar power; system integration; power sector model; flexibility options (storage, transmission grid, demand response); model evaluation; model validation.3

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Carpe diem: A novel approach to select representative days for long-term power system modeling

Cited by 226 publications

References 10 publications

Clustering methods assessment for investment in zero emission neighborhoods’ energy system

Clustering methods assessment for investment in zero emission neighborhoods’ energy system

Meeting the Modeling Needs of Future Energy Systems

System Integration of Wind and Solar Power in Integrated Assessment Models: A Cross-Model Evaluation of New Approaches

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