Investigating the Effects of Increased Thermal Generation by Unit Commitment Optimization in Hydrothermal Power Systems Using Lagrange Algorithm.

  • Lambe M. Adesina Department of Electrical and Computer Engineering, Kwara State University, Malete, Nigeria.
  • Olalekan Ogunbiyi Electrical and Computer Engineering Dept, Kwara State University, Malete, Nigeria
  • Bilkisu Jimada-Ojuolape Electrical and Computer Engineering Dept., Kwara State University, Malete.
  • Luqman O. Issa
Keywords: Lagrange Algorithm, Load Scheduling, Operating Cost, Optimization Analysis, Quadratic, cost functions

Abstract

Optimization assists power utility in operating the system conveniently, meeting customer’s load demand, reducing peak load consumption, and minimizing transmission system losses. It involves determining the best operating levels for electric power plants to meet the needs of a given network. This paper aims to present an optimization analysis of the economic distribution of generated electricity among the various generation units to meet load demand at the minimum possible cost using the Economic Load Dispatched. Nigerian existing hydrothermal network (a deregulated system in 2013) along with the newly added unit (Olorunsogo thermal unit) was used as the case study.  The obtained data from Nigerian power utility were processed via MATLAB to generate the Quadratic Cost Function (QCF) used to model the generator to get the Cost Coefficients. The Lagrange algorithm developed was also applied to the data using MATLAB to have Dynamic Load Scheduling (DLS) for both hydro and thermal plants. The results indicate cost reduction which enhanced the overall performance and reliability of the system. Observation shows that the newly added thermal unit (Olorunsogo Unit) is efficient and cost-effectively operating. Results also show that hydro plants were allocated high loads, and the system’s smooth operation would require bringing the old thermal units such as Afam, Calabar, Egbin, and Sapele plants online to enhance an improvement in generation demand. Other thermal plants like AES, Delta, and Okapi run at lower power implying their uneconomical unit operation implying their uneconomical unit operation.

References

Abanihi V. K. and Ndunagu, J. N. (2019). Optimal Load Scheduling of Nigerian Power System with Valve Point Effect using Jaya Optimization Algorithm. Nigerian Journal of Technology (NIJOTECH), 38(3), pp. 688–694. DOI: 10.4314/njt. v38i3.21

Abdou, I. and Tkiouat, M. (2018). Unit Commitment Problem in Electrical Power System: A Literature Review. International Journal of Electrical and Computer Engineering (IJECE), 8(3), pp. 1357 – 1372. DOI: 10.11591/ijece. v8i3.

Ade-Ikuesan, O. O., Olabode, O. E., Okakwu, I. K. and Okelola, M. O. (2019). State-of-the-Art Approach to Economic Load Dispatch on Nigerian Hydrothermal Electric Power System: A Review. Journal of Science and Technology Research, 1(2), pp. 87-99.

Adesina, L. M. (2022). Contingency Assessment of Medium Voltage Distribution System. Nigerian Research Journal of Engineering and Environmental Sciences (RJEES), 7(1), pp. 136-146. http://doi.org/10.5281/zenodo.6722319.

Amosi, C., Musa, S. Y. and Thuku, I. T. (2017). Particles Swarm Optimization Based Economic Load Dispatch of Nigeria Hydrothermal Considering Hydro Cost Functions. International Journal of Engineering Science and Computing, 7(8), pp. 14689 – 14696.

Banerjee, S. and Sarkar, D. (2017). Comparative Analysis of Jaya Optimization Algorithm for Economic Dispatch Solution. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 5(VIII), pp. 909 – 922.

Buraimoh, E., Ejidokun T. O. and Ayamolowo, O. J. (2017). Optimization of an Expanded Nigeria Electricity Grid System using Economic Load Dispatch. ABUAD Journal of Engineering Research and Development (AJERD), 1(1), pp. 61–66.

Bello, S. A., Akorede, M. F. Pouresmaeil, E., Ibrahim, O. (2016). Unit commitment optimization of hydro-thermal power systems in the day-ahead electricity market. Cogent Engineering, 3: 1251009, pp. 1 – 17. http://dx.doi.org/10.1080/23311916.2016.1251009

Dutt, A. and Dhamanda, A. (2013). Classical Approach to Solve Economic Load Dispatch Problem of Thermal Generating Unit in MATLAB Programming. International Journal of Engineering Research and Technology (IJERT), 2(10), pp. 1384–1389.

Hansen, T. M. (2015). Heuristic Optimization for an Aggregator-based Resource Allocation in the Smart Grid. IEEE Transactions on Smart Grid, IEEE Xplore, Digital - Library, pp.1-10. DOI: 10.1109/TSG.2017.2399359.

Kandasamy, L. & Selvara, S. K. (2017). Lambda optimization of constraint violating units in short-term thermal unit commitment using modified dynamic programming. Turkish Journal of Electrical Engineering and Computer Sciences, 25(2), Article 52, pp.1311 – 1325, https://doi.org/10.3906/elk-1409-16.

Kumar, R., Garg, V. and Lal, B. (2013). A Review Paper on Hydro -Thermal Scheduling International Journal of Emerging Technologies in Computational and Applied Sciences (IJETCAS), University Institute of Engineering and Technology, India, IJETCAS 13-405, 2013. https://www.academia.edu/5406535/A_Review_Paper_on_Hydro_Thermal_Schedu

Kumar, V. S. and Mohan, M. R. (2003). Two Level Crossover Genetic Algorithm for Unit Commitment Problem. School of Electrical and Electronics Engineering, College of Engineering, Anna University, Chennai 600025, Tamil Nadu INDIA, pp.1-5. Corpus ID:221068167.

Lewi, D. D., APatrick, A., Jones, E. S., Alden, R. E., Hadi, A. A., McCulloch, M. D., Ionel, D. M. (2023). Decarbonization Analysis for Thermal Generation and Regionally Integrated Large-Scale Renewables Based on Minutely Optimal Dispatch with a Kentucky Case Study. MDPI – Energies, 16(4), pp.1-23, 2023. https://doi.org/10.3390/en16041999.

Liaquat, S., Zia, M. F. and Benbouzid, M. (2021). Modeling and Formulation of Optimization Problems for Optimal Scheduling of Multi-Generation and Hybrid Energy Systems: Review and Recommendations. MDPI Electronics, 10(14): 1688, https://doi.org/10.3390/electronics10141688

Maifeld, T. T. and Sheble, G. (1996). Genetics - Based Unit Commitment Algorithm. IEEE Transactions on Power Systems, Corpus ID: 108 644161, DOI:10.1109/59.136120.

Montero, L., Bello, A. and Reneses, J. (2022). A Review on the Unit Commitment Problem: Approaches, Techniques, and Resolution Methods. MDPI – Energies, 15(4): 1296, pp.1 – 40. https://doi.org/10.3390/en15041296.

Muralikrishnan, N., Jebaraj, L. & Rajan. C. (2020). A comprehensive Review on Evolutionary Optimization Techniques Applied for Unit Commitment Problem. IEEE Access, DOI: 10.1109/ACCESS.2020.3010275

Oluseyi, P. O., Yellow, K. M., Akinbulire, T. O., Babatunde, O. M. and Alayande, A. S. (2019). Optimal Load Frequency Control of two area Power System. Nigerian Journal of Engineering, Faculty of Engineering, Ahmadu Bello University Samaru-Zaria, Nigeria, 26(2), pp.1-14.

Pereira, S., Ferreira, P. and Vaz, I. F. (2012). Short-term scheduling model for a wind-hydrothermal electricity system. Proceedings of 25th International

Salman, D., Kusaf, M., Elmi, Y. K. & Almasri, A. (2022). Optimal Power Systems Planning for IEEE-14 Bus Test System Application In: Proceedings of 2022 10th International Conference on Smart Grid (icSmartGrid). IEEE Xplore: Istanbul, Turkey, DOI: 10.1109/icSmartGrid55722.2022.9848574.

Shuaib, Y. H., Yisah, Y. A., Bakare, G. A., Haruna, M. S., Oodo, S. O. (2017). Optimal Economic Load Dispatch of the Nigerian Thermal Power Stations Using Particle Swarm Optimization (PSO). International Journal of Engineering and Science (IJES), 6(1), pp. 17–23. DOI:10.9790/1813-0601021723

Tijani, M. A., Adepoju, G. A. and Okelola, M. O. (2022). Optimization Approaches to Generation Dispatch Problems: Review of Nigerian Power System. Nigerian Journal of Technological Development, 19(2), pp. 156 -163.

Published
2024-07-01