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. |
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| 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] |
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| Database: | GreenFILE |
| 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] |
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| ISSN: | 15567036 |
| DOI: | 10.1080/15567036.2022.2083270 |
