Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling

Liu, Yixian; Sioshansi, Ramteen; Conejo, Antonio J.

doi:10.1109/tpwrs.2017.2746379

Cited by 105 publications

(77 citation statements)

References 20 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%

“…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%

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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%

Section: Agglomerative 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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“…Yearly profiles of load, wind, and solar are first normalized and then split into daily profiles of 365 days, respectively. Then, a high-dimensional vector (called "daily operating vector") is constructed by putting a daily load profile, daily wind profile, and daily solar profile of the same day together [32]. Next, the well-known k-means clustering algorithm [41] is used to cluster all daily operating vectors, yielding a subset of representative days that capture a wide variety of operating conditions (including load, wind, and solar) in different seasons.…”

Section: Typical Day Selection Modulementioning

confidence: 99%

“…Therefore, to deal with the challenge brought by high penetration of renewable energy generation, it becomes necessary to consider the detailed operation constraints in GEP models. However, it would be time-consuming if operational constraints of all days are considered in the GEP model, especially in the multi-period planning model, since the model would become computationally intractable [29,32]. To deal with this problem, selecting typical days or weeks of a year in the GEP model incorporating operational constraints is seen as an alternative and efficient way to reduce the computational burden and increase efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multi-Period Generation Expansion Planning for Sustainable Power Systems to Maximize the Utilization of Renewable Energy Sources

Wang

Zhang

et al. 2020

Sustainability

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The increasing penetration of renewable energy brings great challenges to the planning and operation of power systems. To deal with the fluctuation of renewable energy, the main focus of current research is on incorporating the detailed operation constraints into generation expansion planning (GEP) models. In most studies, the traditional objective function of GEP is to minimize the total cost (including the investment and operation cost). However, in power systems with high penetration of renewable energy, more attention has been paid to increasing the utilization of renewable energy and reducing the renewable energy curtailment. Different from the traditional objective function, this paper proposes a new objective function to maximize the accommodation of renewable energy during the planning horizon, taking into account short-term operation constraints and uncertainties from load and renewable energy sources. A power grid of one province in China is modified as a case study to verify the rationality and effectiveness of the proposed model. Numerical results show that the proposed GEP model could install more renewable power plants and improve the accommodation of renewable energy compared to the traditional GEP model.

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