Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an Improved Version of Particle Swarm Optimization
Keywords:
power loss reduction, improved PSO-NTVAC, meshed and radial networks, optimal size, optimal location
Abstract
The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.
References
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20. Prakash D.B., Lakshminarayana C. Multiple DG Placements in Radial Distribution System for Multi Objectives Using Whale Optimization Algorithm. – Alexandria Engineering Journal, 2018, vol. 57, No. 4, pp. 2797–2806, DOI: 10.1016/j.aej.2017.11.003.
21. Mohamed I.A., Kowsalya M. Optimal Size and Siting of Multiple Distributed Generators in Distribution System Using Bacterial Foraging Optimization. – Swarm and Evolutionary Computation, 2014, vol. 15, pp. 58–65, DOI: 10.1016/j.swevo.2013.12.001.
22. Wang L., Singh C. Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm. – IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2008, vol. 38, No. 6, pp. 757–764, DOI: 10.1109/TSMCC.2008.2001573.
23. Arya L.D., Koshti A., Choube S.C. Distributed Generation Planning Using Differential Evolution Accounting Voltage Stability Consideration. – International Journal of Electrical Power and Energy Systems, 2012, vol. 42, No. 1, pp. 196–207, DOI: 10.1016/j.ijepes.2012.04.011.
24. Reddy P.D.P., Reddy V., Manohar T. Ant Lion Optimization Algorithm for Optimal Sizing of Renewable Energy Resources for Loss Reduction in Distribution Systems. – Journal of Electrical Systems and Information Technology, 2018, vol. 5, No. 3, pp. 663–680, DOI: 10.1016/j.jesit.2017.06.001.
25. Sun J., Feng B., Xu W. Particle Swarm Optimization with Particles Having Quantum Behavior. – Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, 2004, vol. 1, pp. 325–331, DOI: 10.1109/cec.2004.1330875.
26. Alrashidi M.R., El-Hawary M.E. A Survey of Particle Swarm Optimization Applications in Electric Power Systems. – IEEE Transactions on Evolutionary Computation, 2009, vol. 13, No. 4, pp. 913–918, DOI:10.1109/TEVC.2006.880326.
27. Ratnaweera A., Halgamuge S.K., Watson H.C. Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. – IEEE Transactions on Evolutionary Computation, 2004, vol. 8, No. 3, pp. 240–255, DOI: 10.1109/TEVC.2004.826071.
28. Samakpong T., Ongsakul W., Manjiparambil N.M. Optimal Power Flow Incorporating Renewable Uncertainty Related Opportunity Costs. – Computational Intelligence, 2020, DOI: 10.1111/coin.12316.
29. Jithendranath J., Das D. Multi-Objective Optimal Power Flow in Islanded Microgrids with Solar PV Generation by NLTV-MOPSO. – IETE Journal of Research, 2021, DOI: 10.1080/03772063.2021.1886609.
30. Chaturvedi K.T., Pandit M., Srivastava L. Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch. – IEEE Transactions on Power Systems, 2008, vol. 23, No. 3, pp. 1079–1087, DOI: 10.1109/TPWRS.2008.926455.
31. Chen K., et al. An Ameliorated Particle Swarm Optimizer for Solving Numerical Optimization Problems. – Applied Soft Computing Journal, 2018, vol. 73, pp. 482–496, DOI: 10.1016/j.asoc.2018.09.007.
32. Mamdouh K.A., Shehata A.A., Korovkin N.V. Multi-Objective Voltage Control and Reactive Power Optimization Based on Multi-Objective Particle Swarm Algorithm. – IOP Conference Series: Materials Science and Engineering, 2019, vol. 643, No. 1, DOI: 10.1088/1757-899X/643/1/012089.
#
1. Ahmed M.K., Osman M.H., Korovkin N.V. Optimal reactive power dispatch in power system comprising renewable energy sources by means of a multi-objective particle swarm algorithm. – Materials Science. Power Engineering, 2021, vol. 27, No. 1, pp. 5–20, DOI: 10.18721/JEST.27101.
2. Hung D.Q., Nadarajah М, Bansal R. Analytical Expressions for DG Allocation in Primary Distribution Networks. – IEEE Transactions on Energy Conversion, 2010, vol. 25, No. 3, pp. 814–820, DOI: 10.1109/TEC.2010.2044414.
3. Prabha D.R., et al. Optimal Location and Sizing of Distributed Generation Unit Using Intelligent Water Drop Algorithm. – Sustainable Energy Technologies and Assessments, 2015, vol. 11, pp. 106–113, DOI: 10.1016/j.seta.2015.07.003.
4. Vadhera S., Mahajan S. Optimal Allocation of Dispersed Generation Unit in a Network System. – International Conference on Microelectronics, Computing and Communication (MicroCom), 2016, pp. 0–4, 2016, DOI: 10.1109/MicroCom.2016.7522519.
5. Osman M.H., Refaat A., Korovkin N.V. A Novel Method to Extract Single-Diode PV Parameters Based on Datasheet Values. – Elektrichestvo, 2021, No. 2, pp. 16–21.
6. Refaat A., Elgamal M., Korovkin N.V. A Novel Grid-Connected Photovoltaic Centralized Inverter Topology to Improve the Power Harvest during Partial Shading Condition. – Elektrichestvo, 2019, No. 7, pp. 59–68, DOI: 10.24160/0013-5380-2019-7-59-68.
7. ChithraDevi S.A., Lakshminarasimman L., Balamurugan R. Stud Krill Herd Algorithm for Multiple DG Placement and Sizing in a Radial Distribution System. – Engineering Science and Technology, an International Journal, 2017, vol. 20, No. 2, pp. 748–759, DOI: 10.1016/j.jestch.2016.11.009.
8. Martín García J.A., Gil-Mena A.J. Optimal Distributed Generation Location and Size Using a Modified Teaching-Learning Based Optimization Algorithm. – International Journal of Electrical Power and Energy Systems, 2013, vol. 50, No. 1, pp. 65–75, DOI: 10.1016/j.ijepes.2013.02.023.
9. Rezaee Jordehi A. Allocation of Distributed Generation Units in Electric Power Systems: A Review. – Renewable and Sustainable Energy Reviews, 2016, vol. 56, pp. 893–905, DOI: 10.1016/j.rser.2015.11.086.
10. Reddy P.D.P., Reddy V.C.V., Manohar T.G. Whale Optimization Algorithm for Optimal Sizing of Renewable Resources for Loss Reduction in Distribution Systems. – Renewables: Wind, Water, and Solar, 2017, vol. 4, No. 1, pp. 1–13, DOI: 10.1186/s40807-017-0040-1.
11. Refaat A., Osman M.H., Korovkin N.V. Optimum Power Extraction from Non-Uniform Aged PV Array Using Current Collector Optimizer Topology. – Elektrichestvo, 2019, No. 10, pp. 54–60.
12. Wang C., Nehrir M.H. Analytical approaches for optimal placement of distributed generation sources in power systems. – IEEE Transactions on Power Systems, 2004, vol. 19, No. 4, pp. 2068–2076, DOI: 10.1109/TPWRS.2004.836189.
13. Shehata A.A., Ahmed M.K. State estimation accuracy enhancement for optimal power system steady state modes. – IOP Conference Series: Materials Science and Engineering, 2019, vol. 643, No. 1, DOI: 10.1088/1757-899X/643/1/012049.
14. Babaei E., Galvani S., Nejabatkhah F. Optimal Placement of DG Units Considering Power Losses Minimization and Voltage Stability Enhancement in Power System. – International Journal of Automation and Control Engineering, 2014, vol. 3, No. 1, DOI: 10.14355/ijace.2014.0301.01.
15. Prakash D.B., Lakshminarayana C. Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm. – Procedia Technology, 2016, vol. 25, No. Raerest, pp. 785–792, DOI: 10.1016/j.protcy.2016.08.173.
16. Shehata A.A., et al. Optimal Placement and Sizing of FACTS Devices Based on Autonomous Groups Partical Swarm Optimization Technique. – Archives of Electrical Engineering, 2020, vol. 70, No. 1, DOI: 10.24425/aee.2021.136059.
17. Shukla T.N., et al. Optimal Sizing of Distributed Generation Placed on Radial Distribution System. – Electric Power Components and Systems, 2010, vol. 38, No. 3, pp. 260–274, DOI: 10.1080/15325000903273403.
18. Abu-Mouti F.S., El-Hawary M.E. Optimal Distributed Generation Allocation and Sizing in Distribution Systems Via Artificial Bee Colony Algorithm. – IEEE Transactions on Power Delivery, 2011, vol. 26, No. 4, pp. 2090–2101, DOI: 10.1109/TPWRD.2011.2158246.
19. Elattar E.E., et al. Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique. – IEEE Access, 2020, vol. 8, pp. 109625–109638, DOI: 10.1109/ACCESS.2020.3001758.
20. Prakash D.B., Lakshminarayana C. Multiple DG Placements in Radial Distribution System for Multi Objectives Using Whale Optimization Algorithm. – Alexandria Engineering Journal, 2018, vol. 57, No. 4, pp. 2797–2806, DOI: 10.1016/j.aej.2017.11.003.
21. Mohamed I.A., Kowsalya M. Optimal Size and Siting of Multiple Distributed Generators in Distribution System Using Bacterial Foraging Optimization. – Swarm and Evolutionary Computation, 2014, vol. 15, pp. 58–65, DOI: 10.1016/j.swevo.2013.12.001.
22. Wang L., Singh C. Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm. – IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2008, vol. 38, No. 6, pp. 757–764, DOI: 10.1109/TSMCC.2008.2001573.
23. Arya L.D., Koshti A., Choube S.C. Distributed Generation Planning Using Differential Evolution Accounting Voltage Stability Consideration. – International Journal of Electrical Power and Energy Systems, 2012, vol. 42, No. 1, pp. 196–207, DOI: 10.1016/j.ijepes.2012.04.011.
24. Reddy P.D.P., Reddy V., Manohar T. Ant Lion Optimization Algorithm for Optimal Sizing of Renewable Energy Resources for Loss Reduction in Distribution Systems. – Journal of Electrical Systems and Information Technology, 2018, vol. 5, No. 3, pp. 663–680, DOI: 10.1016/j.jesit.2017.06.001.
25. Sun J., Feng B., Xu W. Particle Swarm Optimization with Particles Having Quantum Behavior. – Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, 2004, vol. 1, pp. 325–331, DOI: 10.1109/cec.2004.1330875.
26. Alrashidi M.R., El-Hawary M.E. A Survey of Particle Swarm Optimization Applications in Electric Power Systems. – IEEE Transactions on Evolutionary Computation, 2009, vol. 13, No. 4, pp. 913–918, DOI:10.1109/TEVC.2006.880326.
27. Ratnaweera A., Halgamuge S.K., Watson H.C. Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. – IEEE Transactions on Evolutionary Computation, 2004, vol. 8, No. 3, pp. 240–255, DOI: 10.1109/TEVC.2004.826071.
28. Samakpong T., Ongsakul W., Manjiparambil N.M. Optimal Power Flow Incorporating Renewable Uncertainty Related Opportunity Costs. – Computational Intelligence, 2020, DOI: 10.1111/coin.12316.
29. Jithendranath J., Das D. Multi-Objective Optimal Power Flow in Islanded Microgrids with Solar PV Generation by NLTV-MOPSO. – IETE Journal of Research, 2021, DOI: 10.1080/03772063.2021.1886609.
30. Chaturvedi K.T., Pandit M., Srivastava L. Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch. – IEEE Transactions on Power Systems, 2008, vol. 23, No. 3, pp. 1079–1087, DOI: 10.1109/TPWRS.2008.926455.
31. Chen K., et al. An Ameliorated Particle Swarm Optimizer for Solving Numerical Optimization Problems. – Applied Soft Computing Journal, 2018, vol. 73, pp. 482–496, DOI: 10.1016/j.asoc.2018.09.007.
32. Mamdouh K.A., Shehata A.A., Korovkin N.V. Multi-Objective Voltage Control and Reactive Power Optimization Based on Multi-Objective Particle Swarm Algorithm. – IOP Conference Series: Materials Science and Engineering, 2019, vol. 643, No. 1, DOI: 10.1088/1757-899X/643/1/012089.
2. Hung D.Q., Nadarajah М, Bansal R. Analytical Expressions for DG Allocation in Primary Distribution Networks. – IEEE Transactions on Energy Conversion, 2010, vol. 25, No. 3, pp. 814–820, DOI: 10.1109/TEC.2010.2044414.
3. Prabha D.R., et al. Optimal Location and Sizing of Distributed Generation Unit Using Intelligent Water Drop Algorithm. – Sustainable Energy Technologies and Assessments, 2015, vol. 11, pp. 106–113, DOI: 10.1016/j.seta.2015.07.003.
4. Vadhera S., Mahajan S. Optimal Allocation of Dispersed Generation Unit in a Network System. – International Conference on Microelectronics, Computing and Communication (MicroCom), 2016, pp. 0–4, 2016, DOI: 10.1109/MicroCom.2016.7522519.
5. Osman M.H., Refaat A., Korovkin N.V. A Novel Method to Extract Single-Diode PV Parameters Based on Datasheet Values. – Электричество, 2021, №. 2, с. 16–21.
6. Refaat A., Elgamal M., Korovkin N.V. A Novel Grid-Connected Photovoltaic Centralized Inverter Topology to Improve the Power Harvest during Partial Shading Condition. – Электричество, 2019, №. 7, с. 59–68, DOI: 10.24160/0013-5380-2019-7-59-68.
7. ChithraDevi S.A., Lakshminarasimman L., Balamurugan R. Stud Krill Herd Algorithm for Multiple DG Placement and Sizing in a Radial Distribution System. – Engineering Science and Technology, an International Journal, 2017, vol. 20, No. 2, pp. 748–759, DOI: 10.1016/j.jestch.2016.11.009.
8. Martín García J.A., Gil-Mena A.J. Optimal Distributed Generation Location and Size Using a Modified Teaching-Learning Based Optimization Algorithm. – International Journal of Electrical Power and Energy Systems, 2013, vol. 50, No. 1, pp. 65–75, DOI: 10.1016/j.ijepes.2013.02.023.
9. Rezaee Jordehi A. Allocation of Distributed Generation Units in Electric Power Systems: A Review. – Renewable and Sustainable Energy Reviews, 2016, vol. 56, pp. 893–905, DOI: 10.1016/j.rser.2015.11.086.
10. Reddy P.D.P., Reddy V.C.V., Manohar T.G. Whale Optimization Algorithm for Optimal Sizing of Renewable Resources for Loss Reduction in Distribution Systems. – Renewables: Wind, Water, and Solar, 2017, vol. 4, No. 1, pp. 1–13, DOI: 10.1186/s40807-017-0040-1.
11. Refaat A., Osman M.H., Korovkin N.V. Optimum Power Extraction from Non-Uniform Aged PV Array Using Current Collector Optimizer Topology. – Электричество, 2019, №. 10, с. 54–60.
12. Wang C., Nehrir M.H. Analytical approaches for optimal placement of distributed generation sources in power systems. – IEEE Transactions on Power Systems, 2004, vol. 19, No. 4, pp. 2068–2076, DOI: 10.1109/TPWRS.2004.836189.
13. Shehata A.A., Ahmed M.K. State estimation accuracy enhancement for optimal power system steady state modes. – IOP Conference Series: Materials Science and Engineering, 2019, vol. 643, No. 1, DOI: 10.1088/1757-899X/643/1/012049.
14. Babaei E., Galvani S., Nejabatkhah F. Optimal Placement of DG Units Considering Power Losses Minimization and Voltage Stability Enhancement in Power System. – International Journal of Automation and Control Engineering, 2014, vol. 3, No. 1, DOI: 10.14355/ijace.2014.0301.01.
15. Prakash D.B., Lakshminarayana C. Multiple DG Placements in Distribution System for Power Loss Reduction Using PSO Algorithm. – Procedia Technology, 2016, vol. 25, No. Raerest, pp. 785–792, DOI: 10.1016/j.protcy.2016.08.173.
16. Shehata A.A., et al. Optimal Placement and Sizing of FACTS Devices Based on Autonomous Groups Partical Swarm Optimization Technique. – Archives of Electrical Engineering, 2020, vol. 70, No. 1, DOI: 10.24425/aee.2021.136059.
17. Shukla T.N., et al. Optimal Sizing of Distributed Generation Placed on Radial Distribution System. – Electric Power Components and Systems, 2010, vol. 38, No. 3, pp. 260–274, DOI: 10.1080/15325000903273403.
18. Abu-Mouti F.S., El-Hawary M.E. Optimal Distributed Generation Allocation and Sizing in Distribution Systems Via Artificial Bee Colony Algorithm. – IEEE Transactions on Power Delivery, 2011, vol. 26, No. 4, pp. 2090–2101, DOI: 10.1109/TPWRD.2011.2158246.
19. Elattar E.E., et al. Optimal Location and Sizing of Distributed Generators Based on Renewable Energy Sources Using Modified Moth Flame Optimization Technique. – IEEE Access, 2020, vol. 8, pp. 109625–109638, DOI: 10.1109/ACCESS.2020.3001758.
20. Prakash D.B., Lakshminarayana C. Multiple DG Placements in Radial Distribution System for Multi Objectives Using Whale Optimization Algorithm. – Alexandria Engineering Journal, 2018, vol. 57, No. 4, pp. 2797–2806, DOI: 10.1016/j.aej.2017.11.003.
21. Mohamed I.A., Kowsalya M. Optimal Size and Siting of Multiple Distributed Generators in Distribution System Using Bacterial Foraging Optimization. – Swarm and Evolutionary Computation, 2014, vol. 15, pp. 58–65, DOI: 10.1016/j.swevo.2013.12.001.
22. Wang L., Singh C. Reliability-Constrained Optimum Placement of Reclosers and Distributed Generators in Distribution Networks Using an Ant Colony System Algorithm. – IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, 2008, vol. 38, No. 6, pp. 757–764, DOI: 10.1109/TSMCC.2008.2001573.
23. Arya L.D., Koshti A., Choube S.C. Distributed Generation Planning Using Differential Evolution Accounting Voltage Stability Consideration. – International Journal of Electrical Power and Energy Systems, 2012, vol. 42, No. 1, pp. 196–207, DOI: 10.1016/j.ijepes.2012.04.011.
24. Reddy P.D.P., Reddy V., Manohar T. Ant Lion Optimization Algorithm for Optimal Sizing of Renewable Energy Resources for Loss Reduction in Distribution Systems. – Journal of Electrical Systems and Information Technology, 2018, vol. 5, No. 3, pp. 663–680, DOI: 10.1016/j.jesit.2017.06.001.
25. Sun J., Feng B., Xu W. Particle Swarm Optimization with Particles Having Quantum Behavior. – Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, 2004, vol. 1, pp. 325–331, DOI: 10.1109/cec.2004.1330875.
26. Alrashidi M.R., El-Hawary M.E. A Survey of Particle Swarm Optimization Applications in Electric Power Systems. – IEEE Transactions on Evolutionary Computation, 2009, vol. 13, No. 4, pp. 913–918, DOI:10.1109/TEVC.2006.880326.
27. Ratnaweera A., Halgamuge S.K., Watson H.C. Self-Organizing Hierarchical Particle Swarm Optimizer with Time-Varying Acceleration Coefficients. – IEEE Transactions on Evolutionary Computation, 2004, vol. 8, No. 3, pp. 240–255, DOI: 10.1109/TEVC.2004.826071.
28. Samakpong T., Ongsakul W., Manjiparambil N.M. Optimal Power Flow Incorporating Renewable Uncertainty Related Opportunity Costs. – Computational Intelligence, 2020, DOI: 10.1111/coin.12316.
29. Jithendranath J., Das D. Multi-Objective Optimal Power Flow in Islanded Microgrids with Solar PV Generation by NLTV-MOPSO. – IETE Journal of Research, 2021, DOI: 10.1080/03772063.2021.1886609.
30. Chaturvedi K.T., Pandit M., Srivastava L. Self-Organizing Hierarchical Particle Swarm Optimization for Nonconvex Economic Dispatch. – IEEE Transactions on Power Systems, 2008, vol. 23, No. 3, pp. 1079–1087, DOI: 10.1109/TPWRS.2008.926455.
31. Chen K., et al. An Ameliorated Particle Swarm Optimizer for Solving Numerical Optimization Problems. – Applied Soft Computing Journal, 2018, vol. 73, pp. 482–496, DOI: 10.1016/j.asoc.2018.09.007.
32. Mamdouh K.A., Shehata A.A., Korovkin N.V. Multi-Objective Voltage Control and Reactive Power Optimization Based on Multi-Objective Particle Swarm Algorithm. – IOP Conference Series: Materials Science and Engineering, 2019, vol. 643, No. 1, DOI: 10.1088/1757-899X/643/1/012089.
#
1. Ahmed M.K., Osman M.H., Korovkin N.V. Optimal reactive power dispatch in power system comprising renewable energy sources by means of a multi-objective particle swarm algorithm. – Materials Science. Power Engineering, 2021, vol. 27, No. 1, pp. 5–20, DOI: 10.18721/JEST.27101.
2. Hung D.Q., Nadarajah М, Bansal R. Analytical Expressions for DG Allocation in Primary Distribution Networks. – IEEE Transactions on Energy Conversion, 2010, vol. 25, No. 3, pp. 814–820, DOI: 10.1109/TEC.2010.2044414.
3. Prabha D.R., et al. Optimal Location and Sizing of Distributed Generation Unit Using Intelligent Water Drop Algorithm. – Sustainable Energy Technologies and Assessments, 2015, vol. 11, pp. 106–113, DOI: 10.1016/j.seta.2015.07.003.
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