Optimal design and operations of a flexible oxyfuel natural gas plant

Teichgraeber, Holger; Brodrick, Philip G.; Brandt, Adam R.

doi:10.1016/j.energy.2017.09.087

Cited by 28 publications

(13 citation statements)

References 51 publications

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“…Another normalization that can be found in the literature [41,97,[146][147][148] is the z-normalization that directly accounts for the standard deviation, rather than for the maximum and minimum outliers, which implies a normal distribution with different spreads amongst different attributes:…”

Section: Preprocessing and Normalizationmentioning

confidence: 99%

“…One of the most common partitional clustering algorithms used in energy system optimization is the k-means algorithm, which has been used in a variety of studies [14,15,24,37,57,58,63,69,74,78,[83][84][85][86][87]97,[137][138][139]141,142,[145][146][147][148][153][154][155][156][157][158][159][160][161]. The objective of the k-means algorithm is to minimize the sum of the squared distances between all cluster members of all clusters and the corresponding cluster centers, i.e., min…”

Section: Partitional Clusteringmentioning

confidence: 99%

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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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Section: Preprocessing and Normalizationmentioning

confidence: 99%

Section: Partitional Clusteringmentioning

confidence: 99%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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“…We compute the step length p by comparing the values of the function L( ) defined in (30) at different iterations p of Algorithm 3, similarly as in Shor (1985):…”

Section: Initializing Columnsmentioning

confidence: 99%

A column generation algorithm for solving energy system planning problems

et al. 2021

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Energy system optimization models are typically large models which combine sub-models which range from linear to very nonlinear. Column generation (CG) is a classical tool to generate feasible solutions of sub-models, defining columns of global master problems, which are used to steer the search for a global solution. In this paper, we present a new inner approximation method for solving energy system MINLP models. The approach is based on combining CG and the Frank Wolfe algorithm for generating an inner approximation of a convex relaxation and a primal heuristic for computing solution candidates. The features of this approach are: (i) no global branch-and-bound tree is used, (ii) sub-problems can be solved in parallel to generate columns, which do not have to be optimal, nor become available at the same time to synchronize the solution, (iii) an arbitrary solver can be used to solve sub-models, (iv) the approach (and the implementation) is generic and can be used to solve other nonconvex MINLP models. We perform experiments with decentralized energy supply system models with more than 3000 variables. The numerical results show that the new decomposition method is able to compute high-quality solutions and has the potential to outperform state-of-the-art MINLP solvers.

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“…Many of the design and operations optimization problems to which time-series aggregation has been applied are in the general domain of energy systems optimization. These problems include generation and transmission capacity expansion problems (Nahmmacher, Schmid, Hirth, & Knopf, 2016;Pfenninger, 2017), local energy supply system design problems (Bahl, Kümpel, Seele, Lampe, & Bardow, 2017;Kotzur, Markewitz, Robinius, & Stolten, 2018), and individual technology design problems (Brodrick, Brandt, & Durlofsky, 2017;Teichgraeber, Brodrick, & Brandt, 2017). Time series of interest in these problems include energy demands (electricity, heating, cooling), electricity prices, wind and solar availability factors, and temperatures.…”

Section: Application Areasmentioning

confidence: 99%

TimeSeriesClustering: An extensible framework in Julia

Teichgraeber¹,

Kuepper²,

Brandt³

2019

JOSS

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TimeSeriesClustering is a Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets. The software provides a type system for temporal data, and provides an implementation of the most commonly used clustering methods and extreme value selection methods for temporal data. TimeSeriesClustering provides simple integration of multi-dimensional time-series data (e.g., multiple attributes such as wind availability, solar availability, and electricity demand) in a single aggregation process. The software is applicable to general time series datasets and lends itself well to a multitude of application areas within the field of time series data mining. TimeSeriesClustering was originally developed to perform time series aggregation for energy systems optimization problems. Because of the software's origin, many of the examples in this work stem from the field of energy systems optimization.

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Optimal design and operations of a flexible oxyfuel natural gas plant

Cited by 28 publications

References 51 publications

A Review on Time Series Aggregation Methods for Energy System Models

A Review on Time Series Aggregation Methods for Energy System Models

A column generation algorithm for solving energy system planning problems

TimeSeriesClustering: An extensible framework in Julia

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