Mitigation Impact of Critical Contingencies in Electric Power Grid using PSO-Based Maximum Constrained Load-Shedding Technique
Main Article Content
Abstract
This study presents a Particle Swarm Optimization (PSO)-based scheme for optimal targeted load shedding and contingency severity assessment in the electric power grid (EPG). The IEEE 14 EPG was used as a testbed. The study identified critical branches and quantitatively evaluated the operational performance of an EPG under base case, outage without and with targeted load shedding schemes, utilizing convergence characteristics, voltage magnitudes and angles, and branch load flows as diagnostic metrics. The base case demonstrated excellent numerical stability, with convergence achieved in fewer than 5 iterations, and all bus voltages maintained within the IEEE standard range. A critical outage scenario caused severe difficulty, as evidenced by prolonged convergence (exceeding 15 iterations), a drastic voltage at Bus 1 to 0.7214pu, and overloading of Line 2 to 2.0524pu, approximately 275% of its base case loading. These conditions signified an unstable operational state, posing severe risks to system security. Implementation of targeted load shedding significantly improved system conditions: convergence iterations reduced to approximately 6, Bus 1 voltage restored to 0.9682pu, and Line 2 loading decreased to 0.5683pu. Other buses consistently maintained voltages within acceptable margins, and branch flows on non-critical lines remained insignificant across all cases. Voltage angle profiles further corroborate the systemic stress during outage and the stabilization effect post-load shedding. The proposed technique quantitatively demonstrates that selective load shedding is an effective corrective control strategy, not only restoring voltage stability but also alleviating transmission line overloading, thus enhancing the EPG’s ability to maintain secure and reliable operation under severe contingency conditions.
Article Details
Section
How to Cite
References
Adetona, S., John, M. and Umar, S., 2022. Optimal reactive power dispatch using improved Chaotic PSO algorithm with the Wingbeat frequency. Acta Marisiensis. Seria Technologica., 19(2). pp. 20-29, https://doi.org/10.2478/amset-2022-0013
Adetona, S.O., Olaboyin, E.B., Salawu, R.I. and Okafor, F.N., 2024. Upshot of unified power flow controller on the minimization of the severity of overloading on electric power grid. Journal of King Saud University-Engineering Sciences, 36(6), pp. 400-408. https://doi.org/10.1016/j.jksues.2021.10.010
Bioki, M.H., Rashidinejad, M., Fadaeinedjad, R. and Esmaeilian, H.R., 2012, August. An application of PSO in optimal load shedding considering voltage stability. In Global Conference on Power Control and Optimization.
Chivunga, J.N., Longatt, F.G., Lin, Z. and Blanchard, R., 2024. Transmission line redundancy for grid resilience enhancement: The concept of Transmission Lines contributing to Energy Not Supplied (TLENS) on Malawi’s transmission grid. Energy Reports, 12, pp. 4670-4685. https://doi.org/10.1016/j.egyr.2024.10.047.
Choi, Y., Lim, Y. and Kim, H.M., 2017. Optimal load shedding for maximizing satisfaction in an islanded microgrid. Energies, 10(1), P. 45. https://doi.org/10.3390/en10010045
Das, D., 2006, Electrical Power Systems, New Age International (P) Ltd, New-Delhi. India, https://books.google.com.ng/books?id=9CYS_krSvMUC
Gao, H., Chen, Y., Xu, Y. and Liu, C.C., 2016. Dynamic load shedding for an islanded microgrid with limited generation resources. IET Generation, Transmission & Distribution, 10(12), pp. 2953-2961. https://doi.org/10.1049/iet-gtd.2015.1452
Grainger, J.J., and Stevenson, W.D., Jr. 1994, Power System Analysis, McGraw-Hill, Inc., Singapore, https://books.google.com.ng/books?id=NBIoAQAAMAAJ
Gubina, F. and Strmcnik, B., 1995. Voltage collapse proximity index determination using voltage phasors approach. IEEE transactions on power systems, 10(2), pp. 788-794. https://doi.org/10.1109/59.387918
IImai, S., 2005, June. Undervoltage load shedding improving security as reasonable measure for extreme contingencies. In IEEE Power Engineering Society General Meeting, pp. 1754-1759. IEEE. https://doi.org/10.1109/PES.2005.1489318.
Iweh, C.D., Gyamfi, S., Effah-Donyina, E. and Tanyi, E., 2024. Analysis of contingency scenarios towards a suitable transmission pathway in the southern interconnected grid (SIG) of Cameroon. e-Prime-Advances in Electrical Engineering, Electronics and Energy, 7, P. 100486. https://doi.org/10.1016/j.prime.2024.100486.
Javadi, M. and Amraee, T., 2018. Mixed integer linear formulation for undervoltage load shedding to provide voltage stability. IET Generation, Transmission & Distribution, 12(9), pp. 2095-2104. https://doi.org/10.1049/iet-gtd.2017.1118
Kennedy, J. and Eberhart, R., 1995, November. Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks, 4, pp. 1942-1948. https://doi.org/10.1109/ICNN.1995.488968.
Ketabi, A. and Hajiakbari Fini, M., 2017. Adaptive underfrequency load shedding using particle swarm optimization algorithm. Journal of applied research and technology, 15(1), pp. 54-60. https://doi.org/10.1016/j.jart.2016.12.003.
Kisengeu, S.M., Muriithi, C.M. and Nyakoe, G.N., 2021. Under voltage load shedding using hybrid ABC-PSO algorithm for voltage stability enhancement. Heliyon, 7(10). https://doi.org/10.1016/j.heliyon.2021.e08138
Kiran, S.H., Dash, S.S., Subramani, C. and Pathy, S., 2016. An efficient swarm optimization technique for stability analysis in IEEE–14 Bus System. Indian journal of Science and Technology, 9(13), pp. 1-6. https://doi.org/10.17485/ijst/2016/v9i13/80524.
Kwang, Y.L, and Zita A.V., 2020. Applications of modern heuristic optimization methods in power and energy systems, John Wiley & Sons. https:/doi.org/10.1002/9781119602286
Le, T.N., Nguyen, H.M.V., Hoang, T.T. and Nguyen, N.A., 2023. Optimizing the power system operation problem towards minimizing generation and damage costs due to load shedding. Engineering, Technology & Applied Science
Research, 13(5), pp. 11643-11648. https://doi.org/10.48084/etasr.6221
Malakar, S. and Maharana, M.K., 2014. Contingency assessment of electric power system by calculation of unequal priority factors for static severity indices using analytic hierarchy process. system, 9, P. 13. https://www.doi.org/10.70729/IJSER15131
Marouani, I., Guesmi, T., Abdallah, H.H. and Ouali, A., 2011, March. Optimal location of multi type FACTS devices for multiple contingencies using genetic algorithms. In Eighth International Multi-Conference on Systems, Signals & Devices, pp. 1-7. IEEE. https://doi.org/10.5923/j.ijee.20120202.05
MathWorks, 2024. MATLAB (Version R2024b) [Software]. https://www.mathworks.com
Nazir, N., Gupta, N. and Farishta, R., 2023, November. contingency analysis in power system studies: A critical review. In International Conference on Machine Learning, Advances in Computing, Renewable Energy and Communication, pp. 85-95. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5227-0_9
Saadat, H., 1999, Power System Analysis, New York: Mc Graw Hill, 1999.
Salomon, C.P., Coutinho, M.P., Lambert-Torres, G. and Ferreira, C., 2011, June. Hybrid particle swarm optimization with biased mutation applied to load flow computation in electrical power systems. In International Conference in Swarm Intelligence, pp. 595-605. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_70
Seyedi, H. and Sanaye-Pasand, M., 2009. New centralised adaptive load-shedding algorithms to mitigate power system blackouts. IET generation, transmission & distribution, 3(1), pp. 99-114. https://doi.org/10.1049/iet-gtd:20080210
Slochanal, S.M.R., Saravanan, M. and Devi, A.C., 2005, November. Application of PSO technique to find optimal settings of TCSC for static security enhancement considering installation cost. In 2005 International Power Engineering Conference, pp. 1-394. IEEE. https://doi.org/10.1109/IPEC.2005.206940
Sutha, S. and Kamaraj, N., 2008. Optimal location of multi type facts devices for multiple contingencies using particle swarm optimization. Electrical Engineering, 17, P. 18.
Tamilselvan, V., 2020. A hybrid PSO-ABC algorithm for optimal load shedding and improving voltage stability. International Journal of Manufacturing Technology and Management, 34(6), pp. 577-597. https://doi.org/10.1504/IJMTM.2020.109999
University of Washington, 1999, Power Systems Test Case Archive. [Online]. Available: https://labs.ece.uw.edu/pstca/pf14/pg_tca14bus.htm
Verayiah, R., Mohamed, A., Shareef, H. and Abidin, I.Z., 2014. Under voltage load shedding scheme using meta-heuristic optimization methods. Przegląd Elektrotechniczny, 90(11), pp. 162-168. https://doi.org/10.12915/pe.2014.11.43
Xu, Y., Liu, W. and Gong, J., 2011. Stable multi-agent-based load shedding algorithm for power systems. IEEE Transactions on Power Systems, 26(4), pp. 2006-2014. https://doi.org/10.1109/TPWRS.2011.2120631
Zhou, Y., Park, J. and Zhu, H., 2022, July. Scalable learning for optimal load shedding under power grid emergency operations. In 2022 IEEE Power & Energy Society General Meeting (PESGM), pp. 1-5. IEEE. https://doi.org/10.48550/arXiv.2111.11980
Zimmerman R.D., and Murillo-Sánchez, C., 2024, MATPOWER User’s Manual. [Online]. Available: http://www.pserc.cornell.edu/matpower/
Zimmerman, R.D., Murillo-Sánchez, C.E. and Thomas, R.J., 2010. MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education. IEEE Transactions on Power Systems, 26(1), pp. 12-19. https://doi.org/10.1109/TPWRS.2010.2051168