Application of metaheuristic algorithms in DC-optimal power flow

References

• Ajayi, O. 2019. “ DC OPTIMAL POWER FLOW: A Comparative Study of a Classical Optimization Method and Metaheuristic Solution Algorithms.” Master of Engineering (MENG). University of Johannesburg.
   Google Scholar
• Assareh, E., M. A. Behrang, and A. Ghanbarzadeh. 2012. “The Integration of Artificial Neural Networks and Particle Swarm Optimization to Forecast World Green Energy Consumption.” Energy Sources, Part B: Economics, Planning, and Policy 7 (4): 398–410. doi: 10.1080/15567241003792341
   Web of Science ®Google Scholar
• Attia, A., Y. Al-Turki, and A. Abusorrah. 2012. “Optimal Power Flow Using Adapted Genetic Algorithm with Adjusting Population Size.” Electric Power Components and Systems 40 (11): 1285–1299. doi: 10.1080/15325008.2012.689417
   Web of Science ®Google Scholar
• Bouchekara, H. R. E. H., M. A. Abido, and M. Boucherma. 2014. “Optimal Power Flow Using Teaching-Learning-Based Optimization Technique.” Electric Power Systems Research 114: 49–59. doi: 10.1016/j.epsr.2014.03.032
   Web of Science ®Google Scholar
• Bouchekara, H. R. E. H., A. E. Chaib, M. A. Abido, and R. A. El-Sehiemy. 2016. “Optimal Power Flow Using an Improved Colliding Bodies Optimization Algorithm.” Applied Soft Computing 42: 119–131. doi: 10.1016/j.asoc.2016.01.041
   Web of Science ®Google Scholar
• Boussaïd, I., J. Lepagnot, and P. Siarry. 2013. “A Survey on Optimization Metaheuristics.” Information Sciences 237: 82–117. doi: 10.1016/j.ins.2013.02.041
   Web of Science ®Google Scholar
• Capitanescu, F. 2016. “Critical Review of Recent Advances and Further Developments Needed in AC Optimal Power Flow.” Electric Power Systems Research 136: 57–68. doi: 10.1016/j.epsr.2016.02.008
   Web of Science ®Google Scholar
• Dall’Anese, E., and A. Simonetto. 2018. “Optimal Power Flow Pursuit.” IEEE Transactions on Smart Grid 9 (2): 942–952. doi: 10.1109/TSG.2016.2571982
   Web of Science ®Google Scholar
• Damisa, U., N. Nwulu, and Y. Sun. 2018. “Microgrid Energy and Reserve Management Incorporating Prosumer Behind-the-Meter Resources.” IET Renewable Power Generation 12 (8): 910–919. doi: 10.1049/iet-rpg.2017.0659
   Web of Science ®Google Scholar
• El-Fergany, A. A., and H. M. Hasanien. 2015. “Single and Multi-Objective Optimal Power Flow Using Grey Wolf Optimizer and Differential Evolution Algorithms.” Electric Power Components and Systems 43 (13): 1548–1559. doi: 10.1080/15325008.2015.1041625
   Web of Science ®Google Scholar
• Frank, S., and S. Rebennack. 2016. An Introduction to Optimal Power Flow: Theory, Formulation, and Examples Figshare.
   Google Scholar
• Fahrioglu, M., and N. I. Nwulu. 2012. “Investigating a Ranking of Loads in Avoiding Potential Power System Outages.” Journal of Electrical Review (Przeglad Elektrotechniczny). 88 (11a): 239–242.
   Google Scholar
• Gandomi, A. H., X. Yang, S. Talatahari, and A. H. Alavi. 2013. “1 - Metaheuristic Algorithms in Modeling and Optimization.” In Metaheuristic Applications in Structures and Infrastructures, 1–24. Elsevier Inc.
   Google Scholar
• Gbadamosi, S., N. Nwulu, and Y. Sun. 2018. “Multi-Objective Optimization for Composite Generation and Transmission Expansion Planning Considering Offshore Wind Power and Feed-in Tariffs.” IET Renewable Power Generation 12 (14): 1687–1697. doi: 10.1049/iet-rpg.2018.5531
   Web of Science ®Google Scholar
• Ghorbani, N., and E. Babaei. 2014. “Exchange Market Algorithm.” Applied Soft Computing 19: 177–187. doi: 10.1016/j.asoc.2014.02.006
   Web of Science ®Google Scholar
• Ghorbani, N., E. Babaei, and F. Sadikoglu. 2017. “BEMA: Binary Exchange Market Algorithm.” Procedia Computer Science 120: 656–663. doi: 10.1016/j.procs.2017.11.292
   Google Scholar
• Goudarzi, A., M. Kazemi, and M. Nabavi. 2016. “DC Optimal Power Flow through the Linear Programming – in Context of Smart Grid.” Conference Proceedings of the 24th Southern African Universities Power Engineering Conference, held in Vereeniging, South Africa.
   Google Scholar
• Karra, P. K., and S. Kong. 2010. “Diesel Engine Emissions Reduction Using Particle Swarm Optimization.” Combustion Science and Technology 182 (7): 879–903. doi: 10.1080/00102200903418260
   Web of Science ®Google Scholar
• Kennedy, J. 1995. “Particle Swarm Optimization.” International Conference on Neural Networks.
   Google Scholar
• Mahdavi, S., M. E. Shiri, and S. Rahnamayan. 2015. “Metaheuristics in Large-Scale Global Continues Optimization: A Survey.” Information Sciences 295: 407–428. doi: 10.1016/j.ins.2014.10.042
   Web of Science ®Google Scholar
• Mirjalili, S. 2015a. “The ant Lion Optimizer.” Advances in Engineering Software 83: 80–98. doi: 10.1016/j.advengsoft.2015.01.010
   Web of Science ®Google Scholar
• Mirjalili, S. 2015b. “Moth-Flame Optimization Algorithm: A Novel Nature-Inspired Heuristic Paradigm.” Knowledge-Based Systems 89: 228–249. doi: 10.1016/j.knosys.2015.07.006
   Web of Science ®Google Scholar
• Mirjalili, S., P. Jangir, and S. Saremi. 2017. “Multi-Objective ant Lion Optimizer: A Multi-Objective Optimization Algorithm for Solving Engineering Problems.” Applied Intelligence 46 (1): 79–95. doi: 10.1007/s10489-016-0825-8
   Web of Science ®Google Scholar
• Mohamed, Y. S., A. A. Mohamed, A. A. M. El-Gaafary, and A. M. Hemeida. 2017. “Optimal Power Flow Using Moth Swarm Algorithm.” Electric Power Systems Research 142: 190–206. doi: 10.1016/j.epsr.2016.09.025
   Web of Science ®Google Scholar
• Moorthy, C. B., and M. K. Deshmukh. 2015. “A New Approach to Optimise Placement of Wind Turbines Using Particle Swarm Optimisation.” International Journal of Sustainable Energy 34 (6): 396–405. doi: 10.1080/14786451.2013.860140
   Web of Science ®Google Scholar
• Nazir, R., K. Kanada, and P. Coveria. 2015. “Optimization Active and Reactive Power Flow for PV Connected to Grid System Using Newton Raphson Method.” Energy Procedia 68: 77–86. doi: 10.1016/j.egypro.2015.03.235
   Google Scholar
• Nwulu, N. I., and M. Fahrioglu. 2011. “A Neural Network Model for Optimal Demand Management Contract Design.” Proceedings of the 10th International Conference on Environmental & Electrical Engineering (EEEIC2011), Rome, Italy, 8-11 May 2011.
   Google Scholar
• Radosavljević, J., M. Jevtić, and D. Klimenta. 2016. “Energy and Operation Management of a Microgrid Using Particle Swarm Optimization.” Engineering Optimization 48 (5): 811–830. doi: 10.1080/0305215X.2015.1057135
   Web of Science ®Google Scholar
• Surender Reddy, S., and C. Srinivasa Rathnam. 2016. “Optimal Power Flow Using Glow-Worm Swarm Optimization.” International Journal of Electrical Power and Energy Systems 80: 128–139. doi: 10.1016/j.ijepes.2016.01.036
   Web of Science ®Google Scholar
• Teng, J. 2002. “A Modified Gauss–Seidel Algorithm of Three-Phase Power Flow Analysis in Distribution Networks.” International Journal of Electrical Power & Energy Systems 24 (2): 97–102. doi: 10.1016/S0142-0615(01)00022-9
   Web of Science ®Google Scholar
• Tian, D., and Z. Shi. 2018. “MPSO: Modified Particle Swarm Optimization and Its Applications.” Swarm and Evolutionary Computation 41: 49–68. doi: 10.1016/j.swevo.2018.01.011
   Web of Science ®Google Scholar
• Trivedi, I. N., P. Jangir, and S. A. Parmar. 2016. “Optimal Power Flow with Enhancement of Voltage Stability and Reduction of Power Loss Using ant-Lion Optimizer.” Cogent Engineering 3 (1): 1–27. doi: 10.1080/23311916.2016.1208942
   Web of Science ®Google Scholar
• World energy outlook. 2017. International Energy Agency.
   Google Scholar
• Yang, Hong-Tzer, Pai-Chuan Yang, and Ching-Lien Huang. 1996. “Evolutionary Programming Based Economic Dispatch for Units with Non-Smooth Fuel Cost Functions.” IEEE Transactions on Power Systems 11 (1): 112–118. doi: 10.1109/59.485992
   Web of Science ®Google Scholar