Multiobjective Optimization of Power Flow Distribution in EPS with RES under Minimum Number of Transformer’s On-Load Tap Changing
Keywords:
DOPF model, RES integration, transformer's on-load tap-changer, improved PSO
Abstract
The incorporation of renewable energy sources (RES) such as wind and solar power plants into traditional power systems offers numerous technical and environmental benefits. However, improper operation of power grids with RES may lead to high operating costs and higher system losses, as well as voltage fluctuations. To solve these problems, in this paper a formulation of the dynamic optimal power flow (DOPF) problem has been proposed with the integration of RES in the grid. The maximum allowable number of switching operations in a day for on-load tap-changer (OLTC) of transformers is taken into consideration as practical constraint. For 24 hours, the objective functions were to minimize the total operating cost (including fuel cost and cost of power loss), minimize power loss and the daily number of tap operations for transformer's OLTC. The minimization of the daily number of operations for transformers was taken as the goal to preserve the service life of transformers, reduce maintenance and depreciation. The objectives were minimized by coordination between control variables such as the output power of generators, the voltage of generators, transformer tap changer, and shunt capacitor. The improved practical swarm optimization (PSO) algorithm has been employed successfully to solve DOPF with discrete and continuous decision variables in the electric power system.
References
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2. Liang R. et al. Multi‐Objective Dynamic Optimal Power Flow Using Improved Artificial Bee Colony Algorithm Based on Pareto Optimization. – International Transactions on Electrical Energy Systems, 2016, vol. 26, No 4, pp. 692–712, DOI: 10.1002/ETEP.2101.
3. Niknam T., et al. Modified Honey Bee Mating Optimisation to Solve Dynamic Optimal Power Flow Considering Generator Constraints. – IET Generation, Transmission and Distribution, 2011, vol. 5, No. 10, pp. 989–1002, DOI:10.1049/iet-gtd.2011.0055.
4. Chung C.Y., Yan W., Liu F. Decomposed Predictor-Corrector Interior Point Method for Dynamic Optimal Power Flow. – IEEE Transactions on Power Systems, 2010, vol. 26, No. 3, pp. 1030–1039, DOI: 10.1109/TPWRS.2010.2080326.
5. Costa A.L., Costa A.S. Energy and Ancillary Service Dispatch Thro-ugh Dynamic Optimal Power Flow. – Electric power systems research, 2007, vol. 77, No. 8, pp. 1047–1055, DOI:10.1109/PTC.2003.1304717.
6. Yamin H.Y., Al-Tallaq K., Shahidehpour S.M. New Approach for Dynamic Optimal Power Flow Using Benders Decomposition in a Deregulated Power Market. – Electric Power Systems Research, 2003, vol. 65, No. 2, pp. 101–107, DOI: 10.1016/S0378-7796(02)00224-9.
7. Nejdawi I.M., Kimball L.M. Dynamic Optimal Power Flow with Minimal Inter-Temporal Control Variable Changes. – Electric Power Components and Systems, 2005, vol. 33, No. 10, pp. 1071–1080, DOI:10.1080/15325000590933474.
8. Xie K., Song Y.H. Dynamic Optimal Power Flow by Inte-rior Point Methods. – IEE Proceedings-Generation, Transmission and Distribution, 2001, vol. 148, No. 1, pp. 76–84, DOI:10.1049/ip-gtd:20010026.
9. Pu G.-C., Chen N. Sensitivity Factor Based Short Term Dynamic Optimal Power Flow. – Journal of the Chinese Institute of Engineers, 1997, vol. 20, No. 5, pp. 585–592, DOI:10.1080/02533839.1997.9741865.
10. Niknam T., Narimani M.R., Jabbari M. Dynamic Optimal Power Flow Using Hybrid Particle Swarm Optimization and Simulated Annealing. – International Transactions on Electrical Energy Systems, 2013, vol. 23, No. 7, pp. 975–1001, DOI: 10.1002/ETEP.1633.
11. Liu B.. et al. Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables. – IEEE Access, 2020, vol. 8, pp. 194260–194268, DOI:10.1109/ACCESS.2020.3033224.
12. Awad A.S.A., Turcotte D., El-Fouly T.H.M. Impact Assess-ment and Mitigation Techniques for High Penetration Levels of Renewable Energy Sources in Distribution Networks: Voltage-Control Perspective. – Journal of Modern Power Systems and Clean Energy, 2022, vol. 10, iss. 2, pp. 450–458, DOI:10.35833/MPCE.2020.000177.
13. Korovkin N.V, Potienko A.A. The Use of a Genetic Algorithm for Solving Electric Engineering Problems. – Electrical Technology Russia, 2002(4).
14. Toma S. et al. Optimal Control of Voltage in Distribution Systems by Voltage Reference Management. – 2008 IEEE 2nd International Power and Energy Conference, 2008, pp. 1239–1244, DOI: 10.1002/TEE.20452.
15. Agalgaonkar Y.P., Pal B.C., Jabr R.A. Distribution Voltage Control Considering the Impact of PV Generation on Tap Changers and Autonomous Regulators. – IEEE Transactions on Power Systems, 2014, vol. 29, No. 1, pp. 182–192, DOI:10.1109/TPWRS.2013.2279721.
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17. Belyaev N.А., et al. Methods for Optimization of Power System Operation Modes. – Russian Electrical Engineering, 2013, No. 2, DOI: 10.3103/S1068371213020028.
18. Hu Z., et al. Volt/VAr Control in Distribution Systems Using a Time-Interval Based Approach. – IEEЕ Proceedings-Generation, Transmission and Distribution, 2003, vol. 150, No. 5, pp. 548–554, DOI:10.1049/ip-gtd:20030562.
19 Niknam T., Azizipanah-Abarghooee R., Narimani M.R. Re-serve Constrained Dynamic Optimal Power Flow Subject to Valve-Point Effects, Prohibited Zones and Multi-Fuel Constraints. – Energy, 2012, vol. 47, No. 1, pp. 451–464, DOI: 10.1016/J.ENERGY.2012.07.053.
20. 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.
21. Ma Z., et al. Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization. – Symmetry. Multidisciplinary Digital Publishing Institute, 2019, vol. 11, No. 7, DOI: 10.3390/SYM11070876.
22. 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.
23. Ullah Z., et al. A Solution to the Optimal Power Flow Problem Considering WT and PV Generation. – IEEE Access. 2019, vol. 7, pp. 46763–46772, DOI:10.1109/ACCESS.2019.2909561.
24. Das T., et al. Optimal Reactive Power Dispatch Incorporating Solar Power Using Jaya Algorithm. – Computational advancement in communication circuits and systems, 2020, pp. 37–48, DOI: 10.1007/978-981-13-8687-9_4.
25. 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.
26. 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.
27. Zhou W., Yang H., Fang Z. A Novel Model for Photovoltaic Array Performance Prediction. – Applied Energy, 2007, vol. 84, No. 12, pp. 1187–1198, DOI:10.1016/j.apenergy.2007.04.006.
28. Ahmed M.K., Osman M.H., Korovkin N.V. Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an improved Version of Particle Swarm Optimization. – Электричество, 2021, №. 12, с. 15–27.
29. Pfenninger S., Staffell I. Long-Term Patterns of European PV Output Using 30 Years of Validated Hourly Reanalysis and Satellite Data. – Energy, 2016, vol. 114, pp. 1251–1265, DOI:10.1016/j.energy.2016.08.060.
---
Исследователь [Мамдух К. Ахмед] финансируется за счет стипендии [PhD] в рамках стратегического сотрудничества между Арабской Республикой Египет и Российской Федерацией
#
1. Dommel H.W., Tinney W.F. Optimal Power Flow Solutions. – IEEE Transactions on Power Apparatus and Systems, 1968, No. 10, pp. 1866–1876, DOI: 10.1109/TPAS.1968.292150.
2. Liang R. et al. Multi‐Objective Dynamic Optimal Power Flow Using Improved Artificial Bee Colony Algorithm Based on Pareto Optimization. – International Transactions on Electrical Energy Systems, 2016, vol. 26, No 4, pp. 692–712, DOI: 10.1002/ETEP.2101.
3. Niknam T., et al. Modified Honey Bee Mating Optimisation to Solve Dynamic Optimal Power Flow Considering Generator Constraints. – IET Generation, Transmission and Distribution, 2011, vol. 5, No. 10, pp. 989–1002, DOI:10.1049/iet-gtd.2011.0055.
4. Chung C.Y., Yan W., Liu F. Decomposed Predictor-Corrector Interior Point Method for Dynamic Optimal Power Flow. – IEEE Transactions on Power Systems, 2010, vol. 26, No. 3, pp. 1030–1039, DOI: 10.1109/TPWRS.2010.2080326.
5. Costa A.L., Costa A.S. Energy and Ancillary Service Dispatch Through Dynamic Optimal Power Flow. – Electric power systems research, 2007, vol. 77, No. 8, pp. 1047–1055, DOI:10.1109/PTC.2003. 1304717.
6. Yamin H.Y., Al-Tallaq K., Shahidehpour S.M. New Approach for Dynamic Optimal Power Flow Using Benders Decomposition in a Deregulated Power Market. – Electric Power Systems Research, 2003, vol. 65, No. 2, pp. 101–107, DOI: 10.1016/S0378-7796(02)00224-9.
7. Nejdawi I.M., Kimball L.M. Dynamic Optimal Power Flow with Minimal Inter-Temporal Control Variable Changes. – Electric Power Components and Systems, 2005, vol. 33, No. 10, pp. 1071–1080, DOI:10.1080/15325000590933474.
8. Xie K., Song Y.H. Dynamic Optimal Power Flow by Interior Point Methods. – IEE Proceedings-Generation, Transmission and Distribution, 2001, vol. 148, No. 1, pp. 76–84, DOI:10.1049/ip-gtd:20010026.
9. Pu G.-C., Chen N. Sensitivity Factor Based Short Term Dynamic Optimal Power Flow. – Journal of the Chinese Institute of Engineers, 1997, vol. 20, No. 5, pp. 585–592, DOI:10.1080/02533839.1997.9741865.
10. Niknam T., Narimani M.R., Jabbari M. Dynamic Optimal Power Flow Using Hybrid Particle Swarm Optimization and Simulated Annealing. – International Transactions on Electrical Energy Systems, 2013, vol. 23, No. 7, pp. 975–1001, DOI: 10.1002/ETEP.1633.
11. Liu B.. et al. Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables. – IEEE Access, 2020, vol. 8, pp. 194260–194268, DOI:10.1109/ACCESS.2020.3033224.
12. Awad A.S.A., Turcotte D., El-Fouly T.H.M. Impact Assessment and Mitigation Techniques for High Penetration Levels of Renewable Energy Sources in Distribution Networks: Voltage-Control Perspective. – Journal of Modern Power Systems and Clean Energy, 2022, vol. 10, iss. 2, pp. 450–458, DOI:10.35833/MPCE.2020.000177.
13. Korovkin N.V, Potienko A.A. The Use of a Genetic Algorithm for Solving Electric Engineering Problems. – Electrical Technology Russia, 2002(4).
14. Toma S. et al. Optimal Control of Voltage in Distribution Systems by Voltage Reference Management. – 2008 IEEE 2nd International Power and Energy Conference, 2008, pp. 1239–1244, DOI: 10.1002/TEE.20452.
15. Agalgaonkar Y.P., Pal B.C., Jabr R.A. Distribution Voltage Control Considering the Impact of PV Generation on Tap Changers and Autonomous Regulators. – IEEE Transactions on Power Systems, 2014, vol. 29, No. 1, pp. 182–192, DOI:10.1109/TPWRS.2013.2279721.
16. Korovkin N.V, Odintsov M.V, Frolov O.V. Operational Planning in Power Systems Based on Multi-Objective Optimization. – Power Technology and Engineering, 2016, vol. 50, No. 1, pp. 75–78, DOI:10.1007/s10749-016-0662-2.
17. Belyaev N.А., et al. Methods for Optimization of Power System Operation Modes. – Russian Electrical Engineering, 2013, No. 2, DOI: 10.3103/S1068371213020028.
18. Hu Z., et al. Volt/VAr Control in Distribution Systems Using a Time-Interval Based Approach. – IEEE Proceedings-Generation, Transmission and Distribution, 2003, vol. 150, No. 5, pp. 548–554, DOI:10.1049/ip-gtd:20030562.
19. Niknam T., Azizipanah-Abarghooee R., Narimani M.R. Reserve Constrained Dynamic Optimal Power Flow Subject to Valve-Point Effects, Prohibited Zones and Multi-Fuel Constraints. – Energy, 2012, vol. 47, No. 1, pp. 451–464, DOI: 10.1016/J.ENERGY.2012.07.053.
20. 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.
21. Ma Z., et al. Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization. – Symmetry. Multidisciplinary Digital Publishing Institute, 2019, vol. 11, No. 7, DOI: 10.3390/SYM11070876.
22. 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.
23. Ullah Z., et al. A Solution to the Optimal Power Flow Problem Considering WT and PV Generation. – IEEE Access. 2019, vol. 7, pp. 46763–46772, DOI:10.1109/ACCESS.2019.2909561.
24. Das T., et al. Optimal Reactive Power Dispatch Incorporating Solar Power Using Jaya Algorithm. – Computational advancement in communication circuits and systems, 2020, pp. 37–48, DOI: 10.1007/978-981-13-8687-9_4.
25. 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.
26. 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.
27. Zhou W., Yang H., Fang Z. A Novel Model for Photovoltaic Array Performance Prediction. – Applied Energy, 2007, vol. 84, No. 12, pp. 1187–1198, DOI:10.1016/j.apenergy.2007.04.006.
28. Ahmed M.K., Osman M.H., Korovkin N.V. Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an improved Version of Particle Swarm Optimization. – Elektrichestvo, 2021, No. 12, pp. 15–27.
29. Pfenninger S., Staffell I. Long-Term Patterns of European PV Output Using 30 Years of Validated Hourly Reanalysis and Satellite Data. – Energy, 2016, vol. 114, pp. 1251–1265, DOI:10.1016/j.ener-gy.2016.08.060
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The researcher [Mamdouh K. Ahmed] is funded by a scholarship [PhD] under the Joint (Executive Program between the Arab Republic of Egypt and the Russian Federation)
2. Liang R. et al. Multi‐Objective Dynamic Optimal Power Flow Using Improved Artificial Bee Colony Algorithm Based on Pareto Optimization. – International Transactions on Electrical Energy Systems, 2016, vol. 26, No 4, pp. 692–712, DOI: 10.1002/ETEP.2101.
3. Niknam T., et al. Modified Honey Bee Mating Optimisation to Solve Dynamic Optimal Power Flow Considering Generator Constraints. – IET Generation, Transmission and Distribution, 2011, vol. 5, No. 10, pp. 989–1002, DOI:10.1049/iet-gtd.2011.0055.
4. Chung C.Y., Yan W., Liu F. Decomposed Predictor-Corrector Interior Point Method for Dynamic Optimal Power Flow. – IEEE Transactions on Power Systems, 2010, vol. 26, No. 3, pp. 1030–1039, DOI: 10.1109/TPWRS.2010.2080326.
5. Costa A.L., Costa A.S. Energy and Ancillary Service Dispatch Thro-ugh Dynamic Optimal Power Flow. – Electric power systems research, 2007, vol. 77, No. 8, pp. 1047–1055, DOI:10.1109/PTC.2003.1304717.
6. Yamin H.Y., Al-Tallaq K., Shahidehpour S.M. New Approach for Dynamic Optimal Power Flow Using Benders Decomposition in a Deregulated Power Market. – Electric Power Systems Research, 2003, vol. 65, No. 2, pp. 101–107, DOI: 10.1016/S0378-7796(02)00224-9.
7. Nejdawi I.M., Kimball L.M. Dynamic Optimal Power Flow with Minimal Inter-Temporal Control Variable Changes. – Electric Power Components and Systems, 2005, vol. 33, No. 10, pp. 1071–1080, DOI:10.1080/15325000590933474.
8. Xie K., Song Y.H. Dynamic Optimal Power Flow by Inte-rior Point Methods. – IEE Proceedings-Generation, Transmission and Distribution, 2001, vol. 148, No. 1, pp. 76–84, DOI:10.1049/ip-gtd:20010026.
9. Pu G.-C., Chen N. Sensitivity Factor Based Short Term Dynamic Optimal Power Flow. – Journal of the Chinese Institute of Engineers, 1997, vol. 20, No. 5, pp. 585–592, DOI:10.1080/02533839.1997.9741865.
10. Niknam T., Narimani M.R., Jabbari M. Dynamic Optimal Power Flow Using Hybrid Particle Swarm Optimization and Simulated Annealing. – International Transactions on Electrical Energy Systems, 2013, vol. 23, No. 7, pp. 975–1001, DOI: 10.1002/ETEP.1633.
11. Liu B.. et al. Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables. – IEEE Access, 2020, vol. 8, pp. 194260–194268, DOI:10.1109/ACCESS.2020.3033224.
12. Awad A.S.A., Turcotte D., El-Fouly T.H.M. Impact Assess-ment and Mitigation Techniques for High Penetration Levels of Renewable Energy Sources in Distribution Networks: Voltage-Control Perspective. – Journal of Modern Power Systems and Clean Energy, 2022, vol. 10, iss. 2, pp. 450–458, DOI:10.35833/MPCE.2020.000177.
13. Korovkin N.V, Potienko A.A. The Use of a Genetic Algorithm for Solving Electric Engineering Problems. – Electrical Technology Russia, 2002(4).
14. Toma S. et al. Optimal Control of Voltage in Distribution Systems by Voltage Reference Management. – 2008 IEEE 2nd International Power and Energy Conference, 2008, pp. 1239–1244, DOI: 10.1002/TEE.20452.
15. Agalgaonkar Y.P., Pal B.C., Jabr R.A. Distribution Voltage Control Considering the Impact of PV Generation on Tap Changers and Autonomous Regulators. – IEEE Transactions on Power Systems, 2014, vol. 29, No. 1, pp. 182–192, DOI:10.1109/TPWRS.2013.2279721.
16. Korovkin N.V, Odintsov M.V, Frolov O.V. Operational Planning in Power Systems Based on Multi-Objective Optimization. – Power Technology and Engineering, 2016, vol. 50, No. 1, pp. 75–78, DOI:10.1007/s10749-016-0662-2.
17. Belyaev N.А., et al. Methods for Optimization of Power System Operation Modes. – Russian Electrical Engineering, 2013, No. 2, DOI: 10.3103/S1068371213020028.
18. Hu Z., et al. Volt/VAr Control in Distribution Systems Using a Time-Interval Based Approach. – IEEЕ Proceedings-Generation, Transmission and Distribution, 2003, vol. 150, No. 5, pp. 548–554, DOI:10.1049/ip-gtd:20030562.
19 Niknam T., Azizipanah-Abarghooee R., Narimani M.R. Re-serve Constrained Dynamic Optimal Power Flow Subject to Valve-Point Effects, Prohibited Zones and Multi-Fuel Constraints. – Energy, 2012, vol. 47, No. 1, pp. 451–464, DOI: 10.1016/J.ENERGY.2012.07.053.
20. 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.
21. Ma Z., et al. Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization. – Symmetry. Multidisciplinary Digital Publishing Institute, 2019, vol. 11, No. 7, DOI: 10.3390/SYM11070876.
22. 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.
23. Ullah Z., et al. A Solution to the Optimal Power Flow Problem Considering WT and PV Generation. – IEEE Access. 2019, vol. 7, pp. 46763–46772, DOI:10.1109/ACCESS.2019.2909561.
24. Das T., et al. Optimal Reactive Power Dispatch Incorporating Solar Power Using Jaya Algorithm. – Computational advancement in communication circuits and systems, 2020, pp. 37–48, DOI: 10.1007/978-981-13-8687-9_4.
25. 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.
26. 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.
27. Zhou W., Yang H., Fang Z. A Novel Model for Photovoltaic Array Performance Prediction. – Applied Energy, 2007, vol. 84, No. 12, pp. 1187–1198, DOI:10.1016/j.apenergy.2007.04.006.
28. Ahmed M.K., Osman M.H., Korovkin N.V. Optimal Location and Size of Multiple Renewable Distributed Generation Units in Power Systems Using an improved Version of Particle Swarm Optimization. – Электричество, 2021, №. 12, с. 15–27.
29. Pfenninger S., Staffell I. Long-Term Patterns of European PV Output Using 30 Years of Validated Hourly Reanalysis and Satellite Data. – Energy, 2016, vol. 114, pp. 1251–1265, DOI:10.1016/j.energy.2016.08.060.
---
Исследователь [Мамдух К. Ахмед] финансируется за счет стипендии [PhD] в рамках стратегического сотрудничества между Арабской Республикой Египет и Российской Федерацией
#
1. Dommel H.W., Tinney W.F. Optimal Power Flow Solutions. – IEEE Transactions on Power Apparatus and Systems, 1968, No. 10, pp. 1866–1876, DOI: 10.1109/TPAS.1968.292150.
2. Liang R. et al. Multi‐Objective Dynamic Optimal Power Flow Using Improved Artificial Bee Colony Algorithm Based on Pareto Optimization. – International Transactions on Electrical Energy Systems, 2016, vol. 26, No 4, pp. 692–712, DOI: 10.1002/ETEP.2101.
3. Niknam T., et al. Modified Honey Bee Mating Optimisation to Solve Dynamic Optimal Power Flow Considering Generator Constraints. – IET Generation, Transmission and Distribution, 2011, vol. 5, No. 10, pp. 989–1002, DOI:10.1049/iet-gtd.2011.0055.
4. Chung C.Y., Yan W., Liu F. Decomposed Predictor-Corrector Interior Point Method for Dynamic Optimal Power Flow. – IEEE Transactions on Power Systems, 2010, vol. 26, No. 3, pp. 1030–1039, DOI: 10.1109/TPWRS.2010.2080326.
5. Costa A.L., Costa A.S. Energy and Ancillary Service Dispatch Through Dynamic Optimal Power Flow. – Electric power systems research, 2007, vol. 77, No. 8, pp. 1047–1055, DOI:10.1109/PTC.2003. 1304717.
6. Yamin H.Y., Al-Tallaq K., Shahidehpour S.M. New Approach for Dynamic Optimal Power Flow Using Benders Decomposition in a Deregulated Power Market. – Electric Power Systems Research, 2003, vol. 65, No. 2, pp. 101–107, DOI: 10.1016/S0378-7796(02)00224-9.
7. Nejdawi I.M., Kimball L.M. Dynamic Optimal Power Flow with Minimal Inter-Temporal Control Variable Changes. – Electric Power Components and Systems, 2005, vol. 33, No. 10, pp. 1071–1080, DOI:10.1080/15325000590933474.
8. Xie K., Song Y.H. Dynamic Optimal Power Flow by Interior Point Methods. – IEE Proceedings-Generation, Transmission and Distribution, 2001, vol. 148, No. 1, pp. 76–84, DOI:10.1049/ip-gtd:20010026.
9. Pu G.-C., Chen N. Sensitivity Factor Based Short Term Dynamic Optimal Power Flow. – Journal of the Chinese Institute of Engineers, 1997, vol. 20, No. 5, pp. 585–592, DOI:10.1080/02533839.1997.9741865.
10. Niknam T., Narimani M.R., Jabbari M. Dynamic Optimal Power Flow Using Hybrid Particle Swarm Optimization and Simulated Annealing. – International Transactions on Electrical Energy Systems, 2013, vol. 23, No. 7, pp. 975–1001, DOI: 10.1002/ETEP.1633.
11. Liu B.. et al. Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables. – IEEE Access, 2020, vol. 8, pp. 194260–194268, DOI:10.1109/ACCESS.2020.3033224.
12. Awad A.S.A., Turcotte D., El-Fouly T.H.M. Impact Assessment and Mitigation Techniques for High Penetration Levels of Renewable Energy Sources in Distribution Networks: Voltage-Control Perspective. – Journal of Modern Power Systems and Clean Energy, 2022, vol. 10, iss. 2, pp. 450–458, DOI:10.35833/MPCE.2020.000177.
13. Korovkin N.V, Potienko A.A. The Use of a Genetic Algorithm for Solving Electric Engineering Problems. – Electrical Technology Russia, 2002(4).
14. Toma S. et al. Optimal Control of Voltage in Distribution Systems by Voltage Reference Management. – 2008 IEEE 2nd International Power and Energy Conference, 2008, pp. 1239–1244, DOI: 10.1002/TEE.20452.
15. Agalgaonkar Y.P., Pal B.C., Jabr R.A. Distribution Voltage Control Considering the Impact of PV Generation on Tap Changers and Autonomous Regulators. – IEEE Transactions on Power Systems, 2014, vol. 29, No. 1, pp. 182–192, DOI:10.1109/TPWRS.2013.2279721.
16. Korovkin N.V, Odintsov M.V, Frolov O.V. Operational Planning in Power Systems Based on Multi-Objective Optimization. – Power Technology and Engineering, 2016, vol. 50, No. 1, pp. 75–78, DOI:10.1007/s10749-016-0662-2.
17. Belyaev N.А., et al. Methods for Optimization of Power System Operation Modes. – Russian Electrical Engineering, 2013, No. 2, DOI: 10.3103/S1068371213020028.
18. Hu Z., et al. Volt/VAr Control in Distribution Systems Using a Time-Interval Based Approach. – IEEE Proceedings-Generation, Transmission and Distribution, 2003, vol. 150, No. 5, pp. 548–554, DOI:10.1049/ip-gtd:20030562.
19. Niknam T., Azizipanah-Abarghooee R., Narimani M.R. Reserve Constrained Dynamic Optimal Power Flow Subject to Valve-Point Effects, Prohibited Zones and Multi-Fuel Constraints. – Energy, 2012, vol. 47, No. 1, pp. 451–464, DOI: 10.1016/J.ENERGY.2012.07.053.
20. 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.
21. Ma Z., et al. Improved Chaotic Particle Swarm Optimization Algorithm with More Symmetric Distribution for Numerical Function Optimization. – Symmetry. Multidisciplinary Digital Publishing Institute, 2019, vol. 11, No. 7, DOI: 10.3390/SYM11070876.
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The researcher [Mamdouh K. Ahmed] is funded by a scholarship [PhD] under the Joint (Executive Program between the Arab Republic of Egypt and the Russian Federation)
Published
2022-02-26
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