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Balanced Standalone Clustering Unit Commitment Solution for Smart Grid Using Probability Algorithms.

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Title: Balanced Standalone Clustering Unit Commitment Solution for Smart Grid Using Probability Algorithms.
Authors: Ramasamy, Kannan1 (AUTHOR) nieteeehod@gmail.com, Moses, Manoraja Paul1 (AUTHOR), Manoharan, Mathankumar2 (AUTHOR), Padmanaban, Sanjeevikumar3 (AUTHOR)
Source: Energy Sources Part A: Recovery, Utilization & Environmental Effects. 2022, Vol. 44 Issue 2, p5246-5266. 21p.
Subject Terms: *Carbon dioxide mitigation, *Power resources, Direct costing, Grids (Cartography)
Abstract: For a smart grid system operating with clustered generating units, the challenge usually lies in the optimal scheduling of energy resources. Thus, the improvement of a unit commitment problem gains importance in the present power network. In this paper, a novel probability algorithm is proposed for solving the unit commitment problem, in which five separate units from a four-cluster group are operated for one day. This is done with the aim of optimizing the characteristics such as optional units, generation, operating, and marginal cost. Being a multi-objective function, the other factors such as cost response, unit commitment of supply clustering units, clustering of combined operational units, and CO2 emissions are also included. For this study, an RLC load is mathematically modeled in the cluster system, whereas the Unit Control is provided by a Converter and Battery Management Unit. The clustering model developed along with the Unit demand aims to increase the optional units (from 20 to 100 units), decrease the average demand (from 99 to 93 percentage), and lower the generating and marginal costs (from Rs.23,881 to Rs.21,079, Rs.43,082 to Rs.42,111, Rs.71,162 to Rs.64,955, Rs.83,169 to Rs.81,694, Rs.104,104 to Rs.102,928). The suggested algorithm is simulated for a comprehensive smart grid system and the response for marginal cost, CO2 emissions are obtained. In comparison to conventional schemes, the results of the proposed optimization approach show a reduction in the Carbon dioxide emission (in the range of 100 to 400 kg) and in the losses after adding working units with cluster spans of 0.5 to 1.5 kW. [ABSTRACT FROM AUTHOR]
Copyright of Energy Sources Part A: Recovery, Utilization & Environmental Effects is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Label: Title
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  Data: Balanced Standalone Clustering Unit Commitment Solution for Smart Grid Using Probability Algorithms.
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  Data: <searchLink fieldCode="AR" term="%22Ramasamy%2C+Kannan%22">Ramasamy, Kannan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> nieteeehod@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Moses%2C+Manoraja+Paul%22">Moses, Manoraja Paul</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Manoharan%2C+Mathankumar%22">Manoharan, Mathankumar</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Padmanaban%2C+Sanjeevikumar%22">Padmanaban, Sanjeevikumar</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Energy+Sources+Part+A%3A+Recovery%2C+Utilization+%26+Environmental+Effects%22">Energy Sources Part A: Recovery, Utilization & Environmental Effects</searchLink>. 2022, Vol. 44 Issue 2, p5246-5266. 21p.
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  Data: *<searchLink fieldCode="DE" term="%22Carbon+dioxide+mitigation%22">Carbon dioxide mitigation</searchLink><br />*<searchLink fieldCode="DE" term="%22Power+resources%22">Power resources</searchLink><br /><searchLink fieldCode="DE" term="%22Direct+costing%22">Direct costing</searchLink><br /><searchLink fieldCode="DE" term="%22Grids+%28Cartography%29%22">Grids (Cartography)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: For a smart grid system operating with clustered generating units, the challenge usually lies in the optimal scheduling of energy resources. Thus, the improvement of a unit commitment problem gains importance in the present power network. In this paper, a novel probability algorithm is proposed for solving the unit commitment problem, in which five separate units from a four-cluster group are operated for one day. This is done with the aim of optimizing the characteristics such as optional units, generation, operating, and marginal cost. Being a multi-objective function, the other factors such as cost response, unit commitment of supply clustering units, clustering of combined operational units, and CO2 emissions are also included. For this study, an RLC load is mathematically modeled in the cluster system, whereas the Unit Control is provided by a Converter and Battery Management Unit. The clustering model developed along with the Unit demand aims to increase the optional units (from 20 to 100 units), decrease the average demand (from 99 to 93 percentage), and lower the generating and marginal costs (from Rs.23,881 to Rs.21,079, Rs.43,082 to Rs.42,111, Rs.71,162 to Rs.64,955, Rs.83,169 to Rs.81,694, Rs.104,104 to Rs.102,928). The suggested algorithm is simulated for a comprehensive smart grid system and the response for marginal cost, CO2 emissions are obtained. In comparison to conventional schemes, the results of the proposed optimization approach show a reduction in the Carbon dioxide emission (in the range of 100 to 400 kg) and in the losses after adding working units with cluster spans of 0.5 to 1.5 kW. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Energy Sources Part A: Recovery, Utilization & Environmental Effects is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/15567036.2022.2083270
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 21
        StartPage: 5246
    Subjects:
      – SubjectFull: Carbon dioxide mitigation
        Type: general
      – SubjectFull: Power resources
        Type: general
      – SubjectFull: Direct costing
        Type: general
      – SubjectFull: Grids (Cartography)
        Type: general
    Titles:
      – TitleFull: Balanced Standalone Clustering Unit Commitment Solution for Smart Grid Using Probability Algorithms.
        Type: main
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      – PersonEntity:
          Name:
            NameFull: Ramasamy, Kannan
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            NameFull: Moses, Manoraja Paul
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            NameFull: Manoharan, Mathankumar
      – PersonEntity:
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            NameFull: Padmanaban, Sanjeevikumar
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          Dates:
            – D: 15
              M: 01
              Text: 2022
              Type: published
              Y: 2022
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              Value: 15567036
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              Value: 44
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              Value: 2
          Titles:
            – TitleFull: Energy Sources Part A: Recovery, Utilization & Environmental Effects
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