Solving Optimal Reactive Power Dispatch Problem by Population Distinction and Pandemic Virus Algorithms

In this paper Noble, Depraved and Abhorrent (NDA) optimization algorithm and United Kingdom B117 Pandemic Virus algorithm (UPA) are applied for solving the power loss lessening problem. Power loss reduction has been done by with and without considering the voltage stability. In both cases power loss reduction has been achieved effectively. In NDA approach population passages in the direction of the noble member and evades the depraved member. Then abhorrent member plays a vital role in modernizing the population. In a perplexing change, the abhorrent member guides the population in circumstances opposite to people crusade. Position of the members in population is modernized in three subsequent segments. In the preliminary segment, population transfers in the direction of the noble member. Then UPA method is based on the idea of hoi polloi protection as a stratagem to battle the B117 COVID-19 coronavirus pandemic. Spreading of B117 COVID-19 variant is more influenced by the infested persons unswervingly come across other public associates. Communal separation is endorsed by health specialists to protect other populaces from the B117 COVID-19 variant infection. Hoi polloi protection progression is the Preliminary augmentation procedure. Rendering to the Fundamental Facsimile rate, the genetic factor unchanged or prejudiced by communal separation. Authenticity of the NDA optimization algorithm and UPA algorithm is substantiated in IEEE 30 bus system (with and without L-index). Factual power loss lessening is reached. Proportion of actual power loss lessening is augmented

References

[1] Alsac O., Scott B. Optimal load flow with steady state security. IEEE Trans. Power App. Syst., 1974, vol. PAS-93, no. 3, pp. 745--751. DOI: https://doi.org/10.1109/TPAS.1974.293972

[2] Lee K.Y., Paru Y.M., Oritz J.L. A united approach to optimal real and reactive power dispatch. IEEE Trans. Power App. Syst., 1985, vol. PAS-104, no. 5, pp. 1147--1153. DOI: https://doi.org/10.1109/TPAS.1985.323466

[3] Monticelli A., Pereira M.V.F., Granville S. Security-constrained optimal power flow with post-contingency corrective rescheduling. IEEE Trans. Power App. Syst., 1987, vol. 2, no. 1, pp. 175--180. DOI: https://doi.org/10.1109/TPWRS.1987.4335095

[4] Deeb N., Shahidehpur S.M. Linear reactive power optimization in a large power network using the decomposition approach. IEEE Trans. Power App. Syst., 1990, vol. 5, no. 2, pp. 428--438. DOI: https://doi.org/10.1109/59.54549

[5] Hobson E. Network constrained reactive power control using linear programming. IEEE Trans. Power App. Syst., 1980, vol. PAS-99, no. 3, pp. 868--877. DOI: https://doi.org/10.1109/TPAS.1980.319715

[6] Lee K.Y., Park Y.M., Oritz J.L. Fuel-cost optimization for both real- and reactive-power dispatches. IEEE Proc., 1984, vol. 131, iss. 3, pp. 85--93. DOI: https://doi.org/10.1049/ip-c.1984.0012

[7] Mangoli M.K., Lee K.Y., Park Y.M. Optimal real and reactive power control using linear programming. Electr. Power Syst. Res., 1993, vol. 26, iss. 1, pp. 1--10. DOI: https://doi.org/10.1016/0378-7796(93)90063-K

[8] Duong T.L., Duong M.Q., Phan V.-D., et al. Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm. J. Electr. Comput. Eng., 2020, vol. 20, no. 1-11, art. ID 6382507. DOI: https://doi.org/10.1155/2020/6382507

[9] Tran H.V., Pham T.V., Pham L.H., et al. Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm. TELKOMNIKA, 2019, vol. 17, no. 5, pp. 2517--2526.

[10] Packiasudha M., Suja S., Jerome J. A new Cumulative Gravitational Search algorithm for optimal allocation of FACT device to minimize system loss in deregulated electrical power environment. Int. J. Electr. Power Energy Syst., 2017, vol. 84, pp. 34--46. DOI: https://doi.org/10.1016/j.ijepes.2016.04.049

[11] Naderi E., Narimani H., Fathi M., et al. A novel fuzzy adaptive configuration of particle swarm optimization to solve large-scale optimal reactive power dispatch. Appl. Soft Comput., 2017, vol. 53, pp. 441--456. DOI: https://doi.org/10.1016/j.asoc.2017.01.012

[12] Mini V., Kumar S.T.K. An improved solution for reactive power dispatch problem using diversityenhanced particle swarm optimization. Energies, 2020, vol. 13, iss. 11, art. 2862. DOI: https://doi.org/10.3390/en13112862

[13] Muthukumaran E., Kalyani S. Development of smart controller for demand side management in smart grid using reactive power optimization. Soft Comput., 2021, vol. 25, no. 2, pp. 1581--1594. DOI: https://doi.org/10.1007/s00500-020-05246-3

[14] Ravisekar R., Srinivasan K. Optimal reactive power dispatch with series and shunt facts devices using sine cosine algorithm. IJARET, 2020, vol. 11, no. 1, pp. 90--109.

[15] Medania K.B.O., Sayah S., Bekrar A. Whale optimization algorithm based optimal reactive power dispatch: a case study of the Algerian power system. Electr. Power Syst. Res., 2018, vol. 163, part B, pp. 696--705. DOI: https://doi.org/10.1016/j.epsr.2017.09.001

[16] Maleki A., Rosen M.A. Design of a cost-effective on-grid hybrid wind-hydrogen based CHP system using a modified heuristic approach. Int. J. Hydrog. Energy, 2017, vol. 42, iss. 25, pp. 15973--15989. DOI: https://doi.org/10.1016/j.ijhydene.2017.01.169

[17] Zhang W., Maleki A., Rosen M.A., et al. Sizing a stand-alone solar-wind-hydrogen energy system using weather forecasting and a hybrid search optimization algorithm. Energy Convers. Manag., 2019, vol. 180, pp. 609--621. DOI: https://doi.org/10.1016/j.enconman.2018.08.102

[18] Hatata A.Y., Osman G., Aladl M.M. An optimization method for sizing a solar/wind/battery hybrid power system based on the artificial immune system. Sustain. Energy Technol. Assess., 2018, vol. 27, pp. 83--93. DOI: https://doi.org/10.1016/j.seta.2018.03.002

[19] Kamel S., Abdel-Fatah S., Ebeed M., et al. Solving optimal reactive power dispatch problem considering load uncertainty. IEEE ISGT, 2019, pp. 1335--1340. DOI: https://doi.org/10.1109/ISGT-Asia.2019.8881322

[20] Illinois Center for a Smarter Electric Grid (ICSEG): website. Available at: https://icseg.iti.illinois.edu (accessed: 25.02.2019).

[21] Hussainn A.N., Abdullah A.A., Neda O.M. Modified particle swarm optimization for solution of reactive power dispatch. Res. J. Appl. Sci. Eng. Technol., 2018, vol. 15, no. 8, pp. 316--327. DOI: http://dx.doi.org/10.19026/rjaset.15.5917

[22] Pandya S., Roy R. Particle swarm optimization based optimal reactive power dispatch. Proc. IEEE ICECCT, 2015. DOI: https://doi.org/10.1109/ICECCT.2015.7225981

[23] Dai C., Chen W., Zhu Y., et al. Seeker optimization algorithm for optimal reactive power dispatch. IEEE T. Power Syst., 2009, vol. 24, no. 3, pp. 1218--1231. DOI: https://doi.org/10.1109/TPWRS.2009.2021226

[24] Subbaraj P., Rajnarayan P.N. Optimal reactive power dispatch using self-adaptive real coded Genetic algorithm. Electr. Power Syst. Res., 2009, vol. 79, iss. 2, pp. 374--381. DOI: https://doi.org/10.1016/j.epsr.2008.07.008