A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage

Tso, William W.; Demirhan, C. Doga; Heuberger, Clara F.; Powell, Joseph B.; Pistikopoulos, Efstratios N.

doi:10.1016/j.apenergy.2020.115190

Cited by 59 publications

(26 citation statements)

References 30 publications

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“…In long-term ESOMs, input data including IRE sources and demand profiles span across a long time horizon. Such ESOMs have extremely high dimensions, and computational intractability is a significant issue when directly solving the models [33], as in Figure (2).a). Time aggregation is used to address the intractability through approximation.…”

Section: Model-adaptive Clustering Methodsmentioning

confidence: 99%

A Model-Adaptive Clustering Method for Low-Carbon Energy System Optimization

Zhang¹,

Cheng²,

Mallapragada³

et al. 2022

Preprint

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Intermittent renewable energy resources like wind and solar pose great uncertainty of multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find the least-cost solution to matching the uncertainty with flexibility resources. However, input data that capture such multi-time-scale uncertainty are characterized with a long time horizon and bring great difficulty to solving the optimization model. Here we propose an adaptive clustering method based on the decision variables of optimization model to alleviate the computational complexity, in which the energy system is optimized over selected representative time periods instead of the full time horizon. The proposed clustering method is adaptive to various energy system optimization models or settings, because it extracts features from the optimization models. Results show that the proposed clustering method can significantly lower the error in approximating the solution with the full time horizon, compared to traditional clustering methods.

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Section: Model-adaptive Clustering Methodsmentioning

confidence: 99%

A Model-Adaptive Clustering Method for Low-Carbon Energy System Optimization

Zhang¹,

Cheng²,

Mallapragada³

et al. 2022

Preprint

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“…According to the standard of GB/T 4754-2017-Classification of National Economy Industries, multiple user loads in each industry are analyzed using multiple clustering algorithms to extract the corresponding typical load curves. Among them, the system encapsulates a variety of clustering analysis methods including hierarchical clustering algorithm [27], k-means algorithm [28,29], and Minibatch k-means algorithm in the API of the clustering analysis module. In this paper, the description of load clustering and typical daily load curve extraction module is introduced based on the k-means method.…”

Section: Industry-based Load Clustering Methodmentioning

confidence: 99%

“…e predicted load value is obtained for each feeder. Part of the actual feeder load data is selected for prediction [27] and then combined with the experience of experts, through manual verification and analysis of the connected distribution and other feeder state information, and the prediction results can be revised more accurately [25], as shown in Figure 17.…”

Section: Load Predictionmentioning

confidence: 99%

Multidimensional Intelligent Distribution Network Load Analysis and Forecasting Management System Based on Multidata Fusion Technology

Cheng

Liu

et al. 2021

Mathematical Problems in Engineering

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In order to improve the work efficiency of load characteristic analysis and realize lean management, scientific prediction, and reasonable planning of the distribution networks, this paper develops a multidimensional intelligent distribution network load analysis and prediction management system based on the fusion of multidimensional data for the application of multidimensional big data in the smart distribution network. First, the framework of the software system is designed, and the functional modules for multidimensional load characteristic analysis are designed. Then, the method of multidimensional user load characterization is introduced; furthermore, the application functions and the design process of some important function modules of the software system are introduced. Finally, an application example of the multidimensional user load characterization system is presented. Overall, the developed system has the features of interoperability of data links between functional modules, information support between different functions, and modular design concept, which can meet the daily application requirements of power grid enterprises and can respond quickly to the issued calculation requirements.

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“…of PCS For all the numerical results concerning the problem of PCS, we consider the peak power minimization problem that is, p " 8 in (16). For simplicity reasons, the weighting matrix is chosen as W " I T .…”

Section: Influence Of the Clustering Scheme On The Performancementioning

confidence: 99%

“…KMC has been used e.g., in [7] for time-series aggregation, in [8] [9] to perform load estimation, in [10] for electricity generation expansion planning, or in [11] [12] where 365 days are clustered into few representative days. More elaborate techniques have been proposed such as Fuzzy C-Means (FCM) clustering to generate the optimal fuzzy rule for decentralized load frequency control [13], and hierarchical clustering (HC) to aggregate periods with similar load and renewable electricity generation levels [14][15] [16]. To exploit the data features more efficiently, the authors of [17] proposed to use dynamic time warping instead of the Euclidean distance to partition the residential electricity profiles into different clusters, the authors of [18] proposed to use cross correlation as a measure to cluster data from wind turbine power generator, and the authors of [19] used the delay coordinate embedding technique to reduce the dimensionality of load time series.…”

Section: Introductionmentioning

confidence: 99%

Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling

Zhang,

Lasaulce,

Hennebel

et al. 2021

Preprint

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Data clustering is an instrumental tool in the area of energy resource management. One problem with conventional clustering is that it does not take the final use of the clustered data into account, which may lead to a very suboptimal use of energy or computational resources. When clustered data are used by a decision-making entity, it turns out that significant gains can be obtained by tailoring the clustering scheme to the final task performed by the decision-making entity. The key to having good final performance is to automatically extract the important attributes of the data space that are inherently relevant to the subsequent decision-making entity, and partition the data space based on these attributes instead of partitioning the data space based on predefined conventional metrics. For this purpose, we formulate the framework of decision-making oriented clustering and propose an algorithm providing a decision-based partition of the data space and good representative decisions. By applying this novel framework and algorithm to a typical problem of real-time pricing and that of power consumption scheduling, we obtain several insightful analytical results such as the expression of the best representative price profiles for real-time pricing and a very significant reduction in terms of required clusters to perform power consumption scheduling as shown by our simulations.

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A hierarchical clustering decomposition algorithm for optimizing renewable power systems with storage

Cited by 59 publications

References 30 publications

A Model-Adaptive Clustering Method for Low-Carbon Energy System Optimization

A Model-Adaptive Clustering Method for Low-Carbon Energy System Optimization

Multidimensional Intelligent Distribution Network Load Analysis and Forecasting Management System Based on Multidata Fusion Technology

Decision-making Oriented Clustering: Application to Pricing and Power Consumption Scheduling

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