Hierarchical clustering based zone formation in power networks

Kiran, Deep; Abhyankar, A. R.; Panigrahi, Bijaya Ketan

doi:10.1109/npsc.2016.7858891

Cited by 19 publications

(15 citation statements)

References 16 publications

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“…Determining both the size and the optimal number of clusters leads to complex and combinatorial optimization problems, and different solution procedures have been proposed in the literature. 35,47,48 On the other hand, other heuristic approaches can be proposed, for instance executing the METIS algorithm multiple times with a varying number of clusters and selecting then the optimal partition based on some key performance indexes. For the sake of simplicity, the METIS algorithm is here executed with a fixed number of M clusters, assumed to have been optimally predefined through one of the mentioned methods.…”

Section: Minimal Edge-cut Partitioning Using Metismentioning

confidence: 99%

“…The METIS algorithm requires to define a priori the number of clusters of the network graph. Determining both the size and the optimal number of clusters leads to complex and combinatorial optimization problems, and different solution procedures have been proposed in the literature 35,47,48 . On the other hand, other heuristic approaches can be proposed, for instance executing the METIS algorithm multiple times with a varying number of clusters and selecting then the optimal partition based on some key performance indexes.…”

Section: Network Clustering Algorithmmentioning

confidence: 99%

“…[29][30][31][32] Another recently developed approach, named coalitional control, 33,34 is based on the following idea: assuming the network divided into nonoverlapping clusters, each agent accepts to belong to a given cluster until this guarantees it an economic benefit. With specific reference to the clustering of power networks, many other contributions have been proposed, 20,23,[35][36][37] including sparsity-promoting optimization algorithms for damping the interarea oscillations in power networks. 38 Our article focuses on the design of a network partitioning procedure and of a corresponding control architecture, in order to balance unexpected load power variations in large-scale power networks.…”

Section: Introductionmentioning

confidence: 99%

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Supervised model predictive control of large‐scale electricity networks via clustering methods

Bella

Klaus

Ferrari-Trecate

et al. 2021

Optim Control Appl Methods

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This article describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118-bus system is used to test the proposed design procedure in a nontrivial scenario.

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Section: Minimal Edge-cut Partitioning Using Metismentioning

confidence: 99%

Section: Network Clustering Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervised model predictive control of large‐scale electricity networks via clustering methods

Bella

Klaus

Ferrari-Trecate

et al. 2021

Optim Control Appl Methods

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“…As an energy microgrid based on electric energy, the microgrid is the current research hotspot and difficulty of new energy power generation technology [1][2][3][4][5]. e USA, Japan, the European Union, my country, and many other countries have made lots of fundamental work about microgrids, and have also constructed a variety of demonstration projects to verify and display-related technologies and research results, which are briefly described below [6].…”

Section: Introductionmentioning

confidence: 99%

Distributed Energy Storage Cluster Control Method for DC Microgrid Considering Flexibility

Tao

Zhang

Zhao

2022

Mathematical Problems in Engineering

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As the core support, when we develop some new energies, the energy storage industry and energy storage technology cover both the power supply, grid and the user side, residential side, and socialized functional store of the energy facilities. If we can implement the storage of energy by the distributed unit, we can save the time and space translation of energy in function and has the characteristics of flexible layout and installation at the equipment level. It is important to promote the production and consumption of renewable energy and improve the operational reliability of alternating current or direct current AC/DC hybrid power grids. It is a virtual form and develops a tendency for energy storage resources in the power grid in the future. However, as an emerging technology and resource, the use of distributed energy storage still has problems such as low efficiency, high idle rate, and single functional scenarios. Distributed energy storage systems can be used almost everywhere around the system of power, have broad application prospects and huge application potential, and will become more and more significant for the power grid in the near future. In general, the distributed energy storage system now is at a primary level of engineering application, and deep study about it still exists various key technologies which needed to lay the foundation for its popularization and application. In this paper, by constructing a microgrid experimental system containing a variety of distributed energy storage systems, research is carried out around the modeling, control, efficiency analysis, and energy management of distributed energy storage systems.

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“…Clustering approaches are used in the power system research in many applications such as operation and control applications [ 13 , 14 ], generators coherency identification [ 15 , 16 , 17 ], event location [18] , power system vulnerability assessment [19] , controlled islanding [20] and power system restoration [21] . Hierarchical Clustering [22] is applied in this paper to partition the power system network into specific zones that comprising the correlated system buses and identifying the optimal PMU location within each zone based on simple rules.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Clustering based optimal PMU placement for power system fault observability

Eissa

Kassem

2018

Heliyon

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Optimal number and location of phasor measurement units (PMUs) in the power system networks faces challenges for achieving the full network observability during fault conditions. Achieving fault observability approach requires more constraints than normal system observability and consequently suffers from complex analysis and heavy computational burden for the large-scale networks. A new algorithm for determining the optimal PMU placement considering the network fault observability is introduced. The proposed algorithm is achieved through four stages. The first stage is achieved through the network fault simulation to obtain the post fault change in voltage (ΔV) at each bus. Then, the post fault change in voltage (ΔV) is used to build the network connectivity matrix (CM) and forming a new developed Faulted Connectivity Matrix (FCM) that describes the power system topology during the fault conditions. The correlation between the buses is obtained, in the second stage, by applying Pearson correlation coefficient. Hierarchical Clustering technique is given, in the third stage, to cluster the network into coherent zones to find the most correlated buses. Finally, the optimal location of the PMUs is identified within each zone based on simple proposed placement rules. The proposed algorithm is tested under a variety of fault events applied on different standard test systems. The results show the simplicity and the effectiveness of the proposed algorithm.

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Hierarchical clustering based zone formation in power networks

Cited by 19 publications

References 16 publications

Supervised model predictive control of large‐scale electricity networks via clustering methods

Supervised model predictive control of large‐scale electricity networks via clustering methods

Distributed Energy Storage Cluster Control Method for DC Microgrid Considering Flexibility

Hierarchical Clustering based optimal PMU placement for power system fault observability

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