Abstract
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.
Highlights
Amongst the bottom-up frameworks, this review focuses on a vast number of approaches to modeling the different dimensions of energy systems, including methodologies such as optimizations and simulations [9,12,13]
Moore’s law has held true for approximately 40 years [31,32] and there have been significant advances made in the branch-and-bound algorithms used for solving big mixed integer linear programs (MILPs) such as those used in energy system optimization models [33], a decelerating increase of transistor density could be observed in recent years [34]
The integration of time series features considered to be extreme can happen in three different ways: by adding extreme periods to the set of typical periods, by the inclusion of extreme periods or time steps into typical periods using replacement, or by directly modifying the corresponding feature-based merging algorithm used for time series aggregation (TSA) in such a way that it automatically accounts for atypical periods
Summary
Due to the climate change caused by anthropogenic CO2 emissions resulting from the burning of fossil fuels, a major turnaround in the fields of energy supply and consumption is an increasing necessity. Attempts to forecast future energy demands can be traced back to the 1950s and constitute simple, assumption-based scenarios [2] Another theoretical foundation for modern energy system models (ESMs) is the principle of peak-load-pricing first described by Boiteux in 1949 [3] (English translation in 1960 [4]) and Steiner in 1957 [5]. This approach distinguishes between capacity and the operating costs of facilities producing non-storable goods.
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