Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage

Pineda, Salvador; Morales, Juan M.

doi:10.1109/tpwrs.2018.2842093

Cited by 130 publications

(88 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, they compared the results of this approach to those of a perfect foresight approach for the fully resolved time horizon and a model-predictive control and proved the superiority of the approach, as it preserved the chronology of time steps. This was also pointed out in comparison to a typical periods approach by Pineda et al [72], who used the centroid-based hierarchical Ward's algorithm [73] with the side condition to only merge adjacent time steps. Bahl et al [74], meanwhile, introduced a similar algorithm as Fazlollahi et al [69] inspired by Lloyd's algorithm and the partitioning around medoids algorithm [75,76] with multiple initializations.…”

Section: Segmentationmentioning

confidence: 85%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Segmentationmentioning

confidence: 85%

A Review on Time Series Aggregation Methods for Energy System Models

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The choice of daily periods has the advantage of better approximating data with daily patterns, like the load and the solar generation, but at the price of a worst approximation of the wind generation, cf. [2]. The chronological clustering uses the same principle as the hierarchical clustering with the additional condition to cluster only consecutive hours of the original time series {x t } t=1,...,8760 , [2].…”

Section: B Clusteringmentioning

confidence: 99%

“…[2]. The chronological clustering uses the same principle as the hierarchical clustering with the additional condition to cluster only consecutive hours of the original time series {x t } t=1,...,8760 , [2].…”

Section: B Clusteringmentioning

confidence: 99%

Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models

Raventós

Bartels

2020

Energies

View full text Add to dashboard Cite

The growing share of renewable energy results in a more challenging and computationally more demanding modelling of energy transmission systems. This is mainly due to the widely distributed weather-dependent electricity generation. This article evaluates two different methods to reduce the temporal complexity of a power grid model with extendable storage capacity. The goal of the reduction techniques is to accelerate the computation of the linear optimal power flow. The reduction is achieved by choosing a small number of representative time periods to represent one whole year. To select representative time periods, the hierarchical clustering is used to aggregate either adjacent hours or independently distributed days into clusters of time series. The efficiency of the aggregation is evaluated by means of the error of the objective value and the time reduction of the linear optimal power flow. Some statistical indicators are also introduced with the intention of anticipating the accuracy of the aggregations. Further, both the influence of the size of the network and the efficiency of parallel computation in the optimization process are analyzed. As a test case, the transmission network of the northernmost German federal state of Schleswig-Holstein with a scenario corresponding to the year 2035 is considered. The considered scenario is characterized by a high share of installed renewables and the extendability of the storage capacity.Index Terms-Power system modelling, energy system modelling, renewable energy, linear optimal power flow, time series aggregation, storage capacity expansion planning.

show abstract

“…+ ⇒ Scenario generation and reduction has been a topic of great interest in the power systems literature. Most existing methods use clustering [19,[22][23][24] or sampling methods to reduce the number of scenarios from a randomly generated initial set. In a recent review article, Park et al [25] compared four methods for scenario reduction using a two-stage stochastic transmission planning model, including random sampling, importance sampling [26], the distance-based method [17,27,28], iterative scenario reduction approaches, and stratified scenario sampling [25].…”

Section: Stochastic Transmission Planningmentioning

confidence: 99%

Scenario Selection for Iterative Stochastic Transmission Expansion Planning

2020

View full text Add to dashboard Cite

Reliable transmission expansion planning is critical to power systems’ development. To make reliable and sustainable transmission expansion plans, numerous sources of uncertainty including demand, generation capacity, and fuel cost must be taken into consideration in both spatial and temporal dimensions. This paper presents a new approach to selecting a small number of high-quality scenarios for transmission expansion. The Kantorovich distance of social welfare distributions was used to assess the quality of the selected scenarios. A case study was conducted on a power system model that represents the U.S. Eastern and Western Interconnections, and ten high-quality scenarios out of a total of one million were selected for two transmission plans. Results suggested that scenarios selected using the proposed algorithm were able to provide a much more accurate estimation of the value of transmission plans than other scenario selection algorithms in the literature.

show abstract

Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage

Cited by 130 publications

References 34 publications

A Review on Time Series Aggregation Methods for Energy System Models

A Review on Time Series Aggregation Methods for Energy System Models

Evaluation of Temporal Complexity Reduction Techniques Applied to Storage Expansion Planning in Power System Models

Scenario Selection for Iterative Stochastic Transmission Expansion Planning

Contact Info

Product

Resources

About