Spectral clustering for designing robust and reliable multi‐MG smart distribution systems

Hasanvand, Saeed; Nayeripour, Majid; Arefifar, Seyed Ali; Fallahzadeh-Abarghouei, Hossein

doi:10.1049/iet-gtd.2017.0671

Cited by 21 publications

(11 citation statements)

References 28 publications

(33 reference statements)

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“…Moreover, an MMG system is designed in [32] considering multiple objectives and non-dispatchable RESs. A number of researches are developed on partitioning the distribution power network into MMG [3,4,6,9,[33][34][35][36][37][38]. A comparison between this paper and the related researches in the area of constructing MMG is presented in Table 1.…”

Section: Design and Operation Of Mmg In Adnsmentioning

confidence: 99%

Multi‐objective design method for construction of multi‐microgrid systems in active distribution networks

Moghateli¹,

Taher²,

Karimi³

et al. 2020

IET Smart Grid

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One of the important issues in the planning stage of active distribution networks (ADNs) is the optimal design of microgrids (MGs). The design, as a multi-MG system, is comprehensively investigated in this study. In this way, the allocation of energy storage systems (ESSs) and partitioning of ADN are simultaneously performed in order to minimise the cost and maximise the self-adequacy and the reliability considering the uncertainty of load and renewable energy resources. In this study, two approaches are considered. In approach I, the cost, reliability and self-adequacy objectives are taken into account whereas, in approach II, a new probabilistic index representing the ratio of load to storage capacity is also added to mentioned objectives. The proposed multi-objective problem is solved with non-dominated sorting genetic algorithm-II (NSGA-II) as a well-known algorithm based on a probabilistic approach using the Monte-Carlo simulation method (MCSM) and in each approach, several Pareto optimal solutions are evaluated. To simulate and validate the effectiveness of the proposed method, two benchmark distribution networks (the 33-bus and the 119-bus) are used.

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Section: Design and Operation Of Mmg In Adnsmentioning

confidence: 99%

Multi‐objective design method for construction of multi‐microgrid systems in active distribution networks

Moghateli¹,

Taher²,

Karimi³

et al. 2020

IET Smart Grid

View full text Add to dashboard Cite

show abstract

“…Calculation burden reduction will be more necessary in probabilistic studies. Recently, clustering methods have been considered in power system probabilistic studies [28][29][30][31][32]. The task of organizing a collection of objects in such a way that objects within the same group are more similar to one another than to those in other groups is called clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

Robust optimal operation of smart distribution grids with renewable basedgenerators

Sehsalar¹,

Galvani²,

Farsadi³

2020

Turk J Elec Eng & Comp Sci

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Modern distribution systems are equipped with various distributed energy resources (DERs) because of the importance of local generation. These distribution systems encounter more and more uncertainties because of the ever-increasing use of renewable energies. Other sources of uncertainty, such as load variation and system components' failure, will intensify the unpredictable nature of modern distribution systems. Integrating energy storage systems into distribution grids can play a role as a flexible bidirectional source to accommodate issues from constantly varying loads and renewable resources. The overall functionality of these modern distribution systems is enhanced using communication and computational abilities in smart grid frameworks. Robust operation of these systems is effectively taken into consideration to manage the uncertainty, which offers an explicit way to control the desired conservativeness. This paper presents an optimal operating program for smart grids equipped with wind generators, controllable distributed generators, energy storage systems, and reactive power compensators. In order to make the studies more practical, uncertainty about wind generators and grid loads is taken into account. Furthermore, the presented operating program is robust in various conditions, i.e. there is no need to change the operating program in a wide range of probable states. The point estimation method and fuzzy clustering method are used for probabilistic assessment of the distribution system in the presence of uncertainties. The IEEE 37-node standard test system, which is a highly unbalanced system, is selected for the case study and the results are discussed comprehensively.

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“…A data clustering scheme based on the particle swarm optimisation algorithm was used for PPF analysis [16]. Moreover, a data clustering scheme based on a K-means method was proposed for dividing the system into a multimicrogrid system [17]. Also, Lami et al [18] determined the optimal location, size, and year of installation of energy resources with two methods based on two known data clustering schemes, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

New approach for the probabilistic power flow of distribution systems based on data clustering

Sehsalar

Galvani

Farsadi

2019

IET Renewable Power Generation

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The growing popularity of renewable-based generations along with loads fluctuation and network topology variation has exposed distribution systems to high uncertainties, causing difficulties in operating and planning decisions. In addition, the correlation among various uncertain variables has introduced more complexity to this problem. The probabilistic assessment of power systems with various uncertain variables and with any correlation between them can be efficiently handled by Monte-Carlo simulation (MCS) method, but the calculation burden in this method is heavy and thus it is not appropriate in online applications. Keeping the accuracy of the results, data clustering techniques can be efficiently substituted for this method with much less calculation time and burden. In this study, two methods based on data clustering which can consider the correlation between different variables in a straightforward manner are presented for the probabilistic power flow of distribution systems. In order to demonstrate the efficiency of the proposed methods, IEEE 37 node test feeder and IEEE 123 node test feeder were selected as the case study. The results obtained by the proposed methods were compared with those of the MCS method in terms of accuracy and calculation time.

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Spectral clustering for designing robust and reliable multi‐MG smart distribution systems

Cited by 21 publications

References 28 publications

Multi‐objective design method for construction of multi‐microgrid systems in active distribution networks

Multi‐objective design method for construction of multi‐microgrid systems in active distribution networks

Robust optimal operation of smart distribution grids with renewable basedgenerators

New approach for the probabilistic power flow of distribution systems based on data clustering

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