The Optimization of the Location and Capacity of Reactive Power Generation Units, Using a Hybrid Genetic Algorithm Incorporated by the Bus Impedance Power-Flow Calculation Method
Source abstract
Abstract
Dynamic and static reactive power resources have become an important means of maintaining the stability and reliability of power system networks. For example, if reactive power is not appropriately compensated for in transmission and distribution systems, the receiving end voltage may fall dramatically, or the load voltage may increase to a level that trips protection devices. However, none of the previous optimal power-flow studies for reactive power generation (RPG) units have optimized the location and capacity of RPG units by the bus impedance matrix power-flow calculation method. Thus, this study proposes a genetic algorithm that optimizes the location and capacity of RPG units, which is implemented by MATLAB. In addition, this study enhances the algorithm by incorporating bus impedance power-flow calculation method into the algorithm. The proposed hybrid algorithm is shown to be valid when applied to well-known IEEE test systems.
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Research context
OpenAlex topics: Optimal Power Flow Distribution · Microgrid Control and Optimization · Power System Optimization and Stability
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Electrical Engineering, Inha University, Incheon 22212, Korea
Funding
Inha University Research Grant
References (49)
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1. A two-stage robust reactive power optimization considering uncertain wind power integration in active distribution networks10.1109/TSTE.2015.2494587 ↗
2. Reactive power planning based on fuzzy clustering, gray code, and simulated annealing10.1109/TPWRS.2011.2109067 ↗
3. Recurrent multi-objective differential evolution approach for reactive power management10.1049/iet-gtd.2015.0648 ↗
4. Solution of reactive power optimisation including interval uncertainty using genetic algorithm10.1049/iet-gtd.2016.1195 ↗
5. Solution of optimal reactive power dispatch by chaotic krill herd algorithm10.1049/iet-gtd.2015.0077 ↗
6. Reactive power minimization in bidirectional DC–DC converters using a unified-phasor-based particle swarm optimization10.1109/TPEL.2018.2811711 ↗
7. Seeker optimization algorithm for optimal reactive power dispatch10.1109/TPWRS.2009.2021226 ↗
8. Optimal placement of TCSC and SVC for reactive power planning using whale optimization algorithm10.1016/j.swevo.2017.12.008 ↗
9. Integrated strategies of backtracking search optimizer for solving reactive power dispatch problem10.1109/JSYST.2016.2573799 ↗
10. Hybrid SMES based reactive power dispatch by cuckoo search algorithm10.1109/TIA.2018.2866575 ↗
11. A new optimal reactive power planning based on differential search algorithm10.1016/j.ijepes.2014.07.060 ↗
12. Optimisation of dynamic reactive power sources using mesh adaptive direct search10.1049/iet-gtd.2016.1912 ↗
13. Reactive power coordinated optimisation method with renewable distributed generation based on improved harmony search10.1049/iet-gtd.2015.1051 ↗
14. Optimal operation of radial distribution systems using extended dynamic programming10.1109/TPWRS.2017.2722399 ↗
15. Gradient-based adaptive stochastic search for non-differentiable optimization10.1109/TAC.2014.2310052 ↗
16. AC–DC optimal reactive power flow model via predictor–corrector primal-dual interior-point method10.1049/iet-gtd.2012.0497 ↗
17. Reactive power optimization under interval uncertainty by the linear approximation method and its modified method10.1109/TSG.2017.2664816 ↗
18. Linear inequalities convex transformation for optimal reactive power flow model based on MISOCP relaxations10.1049/iet-gtd.2017.1714 ↗
19. Multi-objective mixed-integer dynamic optimization method applied to optimal allocation of dynamic Var sources of power systems10.1109/TPWRS.2017.2724058 ↗
20. AC OPF for smart distribution networks: An efficient and robust quadratic approach10.1109/TSG.2017.2665559 ↗
21. Teaching learning based optimization algorithm for reactive power planning10.1016/j.ijepes.2016.02.042 ↗
22. Vita, V. (2017). Development of a decision-making algorithm for the optimum size and placement of distributed generation units in distribution networks. Energies, 10.10.3390/en10091433 ↗
23. Exact optimal power dispatch in unbalanced distribution systems with high PV penetration10.1109/TPWRS.2018.2869195 ↗
24. Multiobjective predictability-based optimal placement and parameters setting of UPFC in wind power included power systems10.1109/TII.2018.2818821 ↗
25. Reactive power optimization of wind farm based on improved genetic algorithm10.1016/j.egypro.2011.12.1102 ↗
26. Extended-optimal-power-flow-based hierarchical control for islanded AC microgrids10.1109/TPEL.2018.2813980 ↗
27. Begovic, M.M., and Kim, I. (2011, January 20–23). Distributed renewable PV generation in urban distribution networks. Proceedings of the 2011 IEEE Power Systems Conference and Exposition, Phoenix, AZ, USA.10.1109/PSCE.2011.5772614 ↗
28. Kim, I., Begovic, M., Jeong, H., and Crittenden, J. (2013, January 7–10). Impact of photovoltaic distributed generation on generation resource allocation. Proceedings of the 2013 46th Hawaii International Conference on System Sciences, Wailea, Maui, HI, USA.10.1109/HICSS.2013.259 ↗
29. Kim, I., and Begovic, M. (2016, January 5–8). On impact of randomly distributed PV systems on distribution networks. Proceedings of the 2016 49th Hawaii International Conference on System Sciences, Koloa, HI, USA.10.1109/HICSS.2016.302 ↗
30. Kim, I., Harley, R.G., and Regassa, R. (2014, January 7–9). Optimal distributed generation allocation on distribution networks at peak load and the analysis of the impact of volt/var control on the improvement of the voltage profile. Proceedings of the 2014 North American Power Symposium, Pullman, WA, USA.10.1109/NAPS.2014.6965451 ↗
31. Thallam, R.S., Suryanarayanan, S., Heydt, G.T., and Ayyanar, R. (2006, January 18–22). Impact of interconnection of distributed generation of electric distribution systems—A dynamic simulation perspective. Proceedings of the IEEE Power Engineering Society General Meeting, Montreal, QC, Canada.10.1109/PES.2006.1709140 ↗
32. Vita, V., Alimardan, T., and Ekonomou, L. (2015, January 6–8). The impact of distributed generation in the distribution networks’ voltage profile and energy losses. Proceedings of the 2015 IEEE European Modelling Symposium, Madrid, Spain.10.1109/EMS.2015.46 ↗
33. Kim, I., Harley, R.G., Regassa, R., and Valle, Y.D. (2015, January 10–13). The effect of the Volt/VAr control of photovoltaic systems on the time-series steady-state analysis of a distribution network. Proceedings of the 2015 Clemson University Power Systems Conference (PSC), Clemson, SC, USA.10.1109/PSC.2015.7101709 ↗
34. Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley.
35. Optimal distributed generation allocation for reactive power control10.1049/iet-gtd.2016.1393 ↗
36. Amini, M.H., Jaddivada, R., Hoseinzadeh, B., Mishra, S., and Mozafar, M.R. (2018). Optimal SVC allocation in power systems for loss minimization and voltage deviation reduction. Sustainable Interdependent Networks: From Theory to Application, Springer International Publishing.10.1007/978-3-319-74412-4_12 ↗
37. Optimizing reactive power flow of HVDC systems using genetic algorithm10.1016/j.ijepes.2013.08.006 ↗
38. Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage10.1016/j.energy.2018.08.135 ↗
39. Examination of the effect of the reactive power control of photovoltaic systems on electric power grids and the development of a voltage-regulation method that considers feeder impedance sensitivity10.1016/j.epsr.2019.106130 ↗
40. Power flow solution by impedance matrix iterative method10.1109/TPAS.1963.291392 ↗
41. Dugan, R.C., Arritt, R.F., Smith, J., and Rylander, M. (2013). OpenDSS Training Workshop, University of North Carolina at Charlotte.
42. A genetic algorithm for function optimization: A MATLAB implementation
43. Dabbagchi, I. (2019, February 08). IEEE 14-Bus System. Available online: http://labs.ece.uw.edu/pstca/pf14/pg_tca14bus.htm.
44. (1973). Working Group on a Common Format for Exchange of Solved Load Flow Data, Common format for exchange of solved load flow data. IEEE Trans. Power Appar. Syst., 92, 1916–1925.10.1109/TPAS.1973.293571 ↗
45. Dabbagchi, I. (2019, February 08). IEEE 30-Bus System. Available online: http://www.ee.washington.edu/research/pstca/pf30/pg_tca30bus.htm.
46. Dabbagchi, I. (2019, February 08). IEEE 57-Bus System. Available online: http://labs.ece.uw.edu/pstca/pf57/pg_tca57bus.htm.
47. Kim, I. (2014). Impact of stochastic renewable distributed generation on urban distribution networks. Doctor of Philosophy, Electrical and Computer Engineering, Georgia Institute of Technology.
48. Pregelj, A. (2003). Impact of distributed generation on power network operation. Doctoral Dissertation, Georgia Institute of Technology.
49. De Jong, K.A. (1975). An Analysis of the Behavior of a Class of Genetic Adaptive Systems. [Ph.D. Thesis, University of Michigan].