A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation
Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functio...
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Gespeichert in:
| Autor*in: |
Samal, Padarbinda [verfasserIn] Ganguly, Sanjib [verfasserIn] Mohanty, Sanjeeb [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
Unbalanced radial distribution system planning |
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| Übergeordnetes Werk: |
Enthalten in: Soft Computing - Springer-Verlag, 2003, 23(2019), 23 vom: 22. Jan., Seite 12317-12330 |
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| Übergeordnetes Werk: |
volume:23 ; year:2019 ; number:23 ; day:22 ; month:01 ; pages:12317-12330 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00500-019-03772-3 |
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SPR006509126 |
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| 520 | |a Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. | ||
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| 650 | 4 | |a Cuckoo search algorithm |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Ganguly, Sanjib |e verfasserin |4 aut | |
| 700 | 1 | |a Mohanty, Sanjeeb |e verfasserin |4 aut | |
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10.1007/s00500-019-03772-3 doi (DE-627)SPR006509126 (SPR)s00500-019-03772-3-e DE-627 ger DE-627 rakwb eng Samal, Padarbinda verfasserin aut A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. Unbalanced radial distribution system planning (dpeaa)DE-He213 Fuzzy set (dpeaa)DE-He213 Distributed generation (dpeaa)DE-He213 Differential evolution algorithm (dpeaa)DE-He213 Cuckoo search algorithm (dpeaa)DE-He213 Ganguly, Sanjib verfasserin aut Mohanty, Sanjeeb verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 22. Jan., Seite 12317-12330 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:22 month:01 pages:12317-12330 https://dx.doi.org/10.1007/s00500-019-03772-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 22 01 12317-12330 |
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10.1007/s00500-019-03772-3 doi (DE-627)SPR006509126 (SPR)s00500-019-03772-3-e DE-627 ger DE-627 rakwb eng Samal, Padarbinda verfasserin aut A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. Unbalanced radial distribution system planning (dpeaa)DE-He213 Fuzzy set (dpeaa)DE-He213 Distributed generation (dpeaa)DE-He213 Differential evolution algorithm (dpeaa)DE-He213 Cuckoo search algorithm (dpeaa)DE-He213 Ganguly, Sanjib verfasserin aut Mohanty, Sanjeeb verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 22. Jan., Seite 12317-12330 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:22 month:01 pages:12317-12330 https://dx.doi.org/10.1007/s00500-019-03772-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 22 01 12317-12330 |
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10.1007/s00500-019-03772-3 doi (DE-627)SPR006509126 (SPR)s00500-019-03772-3-e DE-627 ger DE-627 rakwb eng Samal, Padarbinda verfasserin aut A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. Unbalanced radial distribution system planning (dpeaa)DE-He213 Fuzzy set (dpeaa)DE-He213 Distributed generation (dpeaa)DE-He213 Differential evolution algorithm (dpeaa)DE-He213 Cuckoo search algorithm (dpeaa)DE-He213 Ganguly, Sanjib verfasserin aut Mohanty, Sanjeeb verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 22. Jan., Seite 12317-12330 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:22 month:01 pages:12317-12330 https://dx.doi.org/10.1007/s00500-019-03772-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 22 01 12317-12330 |
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10.1007/s00500-019-03772-3 doi (DE-627)SPR006509126 (SPR)s00500-019-03772-3-e DE-627 ger DE-627 rakwb eng Samal, Padarbinda verfasserin aut A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. Unbalanced radial distribution system planning (dpeaa)DE-He213 Fuzzy set (dpeaa)DE-He213 Distributed generation (dpeaa)DE-He213 Differential evolution algorithm (dpeaa)DE-He213 Cuckoo search algorithm (dpeaa)DE-He213 Ganguly, Sanjib verfasserin aut Mohanty, Sanjeeb verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 22. Jan., Seite 12317-12330 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:22 month:01 pages:12317-12330 https://dx.doi.org/10.1007/s00500-019-03772-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 22 01 12317-12330 |
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10.1007/s00500-019-03772-3 doi (DE-627)SPR006509126 (SPR)s00500-019-03772-3-e DE-627 ger DE-627 rakwb eng Samal, Padarbinda verfasserin aut A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. Unbalanced radial distribution system planning (dpeaa)DE-He213 Fuzzy set (dpeaa)DE-He213 Distributed generation (dpeaa)DE-He213 Differential evolution algorithm (dpeaa)DE-He213 Cuckoo search algorithm (dpeaa)DE-He213 Ganguly, Sanjib verfasserin aut Mohanty, Sanjeeb verfasserin aut Enthalten in Soft Computing Springer-Verlag, 2003 23(2019), 23 vom: 22. Jan., Seite 12317-12330 (DE-627)SPR006469531 nnns volume:23 year:2019 number:23 day:22 month:01 pages:12317-12330 https://dx.doi.org/10.1007/s00500-019-03772-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER AR 23 2019 23 22 01 12317-12330 |
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A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation |
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Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. |
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Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. |
| abstract_unstemmed |
Abstract This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation. |
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A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation |
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https://dx.doi.org/10.1007/s00500-019-03772-3 |
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Ganguly, Sanjib Mohanty, Sanjeeb |
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