Многоцелевая оптимизация распределения потоков мощности в ЭЭС с ВИЭ при минимальном числе переключений устройства РПН
Ключевые слова:
модель DOPF, интеграция ВИЭ, устройство РПН трансформатора, улучшенный PSO
Аннотация
Интеграция возобновляемых источников энергии (ВИЭ), таких как энергия ветра и солнца, в традиционные энергосистемы дает многочисленные технические и экологические преимущества. Однако неоптимальная эксплуатация электрических сетей с ВИЭ может привести к высоким эксплуатационным расходам и повышенным системным потерям, колебаниям напряжения. Для решения этих проблем в работе предложен подход к оптимизации распределения мощности в энергосистеме за заданный интервал времени (в работе – 24 часа, общепринятый английский термин: Dynamic Optimal Power Flow, поэтому далее используется аббревиатура DOPF). В качестве не рассматриваемого ранее ограничения взято максимально допустимое ежедневное количество переключений устройства регулирования под нагрузкой (РПН) трансформаторов в течение 24 часов. Целевыми функциями являлись: минимизация общих эксплуатационных затрат (в том числе стоимость топлива и потерь мощности), минимизация потерь мощности, минимизация ежедневного количества переключений РПН. Минимизация ежедневного количества операций для трансформаторов принята в качестве цели для увеличения срока службы трансформаторов, сокращения технического обслуживания и амортизации. Минимизация целей выполнялась по дискретным и непрерывным переменным управления, в качестве которых рассматривались: мощность и напряжение генераторов, номера отпаек устройств РПН и величины шунтирующих конденсаторов. Также для решения задачи DOPF с дискретными и непрерывными переменными в работе предложен и успешно использован улучшенный алгоритм оптимизации роя частиц (PSO).
Литература
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 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.
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
---
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.
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
---
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)
