Systematic Comparison of Aggregation Methods for Input Data Time Series Aggregation of Energy Systems Optimization Problems

Teichgraeber, Holger; Brandt, Adam R.

doi:10.1016/b978-0-444-64241-7.50154-3

Cited by 15 publications

(18 citation statements)

References 7 publications

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“…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%

“…This clustering algorithm was used by numerous authors [14,19,41,55,57,58,74,78,86,139,141,149,150,159,[163][164][165][166][167], either by using the partitioning around medoids (PAM) introduced by Kaufman et al [168] or by using an MILP formulation introduced by Vinod et al [169] and used in several studies [14,41,55,57,139,159,164]. The MILP can be formulated as follows:…”

Section: Partitional Clusteringmentioning

confidence: 99%

“…In a number of publications [41,57,58,86,139,141,159], k-medoids clustering was directly compared to k-means clustering. The general observation is that k-medoids clustering is more capable of preserving the intra-period variance, while k-means clustering underestimates extreme events more gravely.…”

Section: Partitional Clusteringmentioning

confidence: 99%

“…The algorithm is thus deterministic and does not require multiple random starting point initializations. Analogously to k-means and k-medoids, the cluster centers can either be represented by their centroids [41,159] or by their medoids [18,37,41,57,72,86,135,140,143,144,148,159]. The general property that centroids underestimate the intra-period variance more severely due to the averaging effect is equivalent to the findings when using k-means instead of k-medoids.…”

Section: Agglomerative Clusteringmentioning

confidence: 99%

“…These algorithms not only compare to the values of different time series at single time steps (pointwise), but also compare values along the time axis with those of other time series (pairwise). In the literature [41,159], dynamic time warping (DTW) and the k-shape algorithm are used, both of which are based on distance measures that are not sensitive to phase shifts within a typical period, which is the case for the Euclidean distance. The dynamic time-warping distance is defined as:…”

Section: Time Shift-tolerant Clustering Algorithmsmentioning

confidence: 99%

See 4 more Smart Citations

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: Partitional Clusteringmentioning

confidence: 99%

Section: Partitional Clusteringmentioning

confidence: 99%

Section: Partitional Clusteringmentioning

confidence: 99%

Section: Agglomerative Clusteringmentioning

confidence: 99%

Section: Time Shift-tolerant Clustering Algorithmsmentioning

confidence: 99%

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A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

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Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling

Palys

Mitrai

Daoutidis

2021

Optim Control Appl Methods

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Using hydrogen (H2) and ammonia (NH3) for renewable energy storage has the potential to enable economical power and heat supply with high renewable penetrations, especially in remote locations which are characterized by high energy costs. In this work we assess the economic competitiveness of renewable combined heat and power (CHP) systems in Mahaka HI, Nantucket MA, and Northwest Arctic Borough (NWAB) AK by optimally designing these systems for scenarios in which power and heat can be purchased over a range of historical energy prices as well as when 100% renewable supply is required. We use a combined optimal design and scheduling model which minimizes annualized net present cost by determining optimal technology selection and size simultaneously with optimal schedules for each period of a system operating horizon aggregated from full year hourly resolution data via a consecutive temporal clustering algorithm. We find that renewable generation meets at least 85% of power demands and 75% of heat demands under the lowest energy prices investigated. Higher conventional energy prices lead to increased renewable penetration which is facilitated by renewable NH3 as a seasonal energy storage medium, as are 100% renewable CHP systems. NH3 is used for power generation with heat cogeneration in all three locations, as well as directly for heating in NWAB. On an annual cost basis, NH3‐enabled 100% renewable CHP is only 3% more expensive in Mahaka and NWAB than systems which can purchase energy at the lowest prices, while it is 15% more expensive in Nantucket.

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Spatial heterogeneity and partitioning of soil health indicators in the Northern Great Plains using self-organizing map and change point methods

Boluwade

2023

Earth Sci Inform

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Systematic Comparison of Aggregation Methods for Input Data Time Series Aggregation of Energy Systems Optimization Problems

Cited by 15 publications

References 7 publications

A Review on Time Series Aggregation Methods for Energy System Models

A Review on Time Series Aggregation Methods for Energy System Models

Renewable hydrogen and ammonia for combined heat and power systems in remote locations: Optimal design and scheduling

Spatial heterogeneity and partitioning of soil health indicators in the Northern Great Plains using self-organizing map and change point methods

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