Contract-Oriented Optimal Economic Dispatch of Thermal Generators in a Multi-Area Deregulated Power System
DOI:
https://doi.org/10.63746/njtd.v22i5.3300Keywords:
Cuckoo Search Algorithm, Disco Participation Matrix, Distribution Companies, Economic Dispatch, Generation Companies, Nigerian Power SystemAbstract
Bilateral contract between the Distribution Companies (DISCOs) and Generation Companies (GENCOs), modelled using DISCO Participation Matrix (DPM), has significant impact on the economic scheduling scheme of thermal generators in Multi-Area Power Systems (MAPS). Neglecting the contract while formulating the scheme can results in suboptimal solutions and inefficient utilization of resources. Thus, this paper proposed a DPM-constrained optimal Economic Dispatch (ED) scheme for multi-fuel type thermal generators in MAPS. The ED scheme minimized the fuel cost of the generators of each GENCO subject to the DPM, generation-demand balance, generation limits, and line transmission capacity as constraints. Due to its fast convergence, flexibility and global optimum searching mechanism, Cuckoo Search Algorithm (CSA) is used to solve the ED problem. The developed scheme is implemented on IEEE 39-bus and 52-bus Nigerian Power System (NPS) both partitioned into three CAs with multiple DISCOs and GENCOs. The effectiveness of the scheme is evaluated by simulating the systems in MATLAB environment. The performance of the ED on the IEEE 39-bus is evaluated by comparing it with those obtained using Grey Wolf Optimizer (MOGWO). It is observed that the fuel cost is reduced by 22.34%, from 5.655×106$/h obtained using the MOGWO to 4.392×106$/h with the developed CSA-based ED schemes. This signifies a substantial improvement over the MOGWO-based solution. Likewise, the ED scheme’s performance on NPS is evaluated by comparing its performance with that obtained using Bat Algorithm (BA). For the 24-hour ED on the NPS, the fuel cost is reduced from 2.467×106$/h to 1.953×106$/h, representing 20.85% reduction. These cost reductions emphasize the significance of imposing the DPM constraint as well as the superior optimization capability of the CSA algorithm in generating schedules that minimize fuel costs compared to the MOGWO and BA-based methods.
References
Abanihi, V. K., & Ovabor, K. O., (2019). Economic Load Dispatch of Nigeria Integrated High Voltage Generation and Transmission Grid Using Bat Algorithm. Nigerian Journal of Technology (NIJOTECH), Vol. 38, No. 3, pp. 680 – 687.
Ahmed, I., Rehan, M., Basit, A., Malik, S. H., & Alvi, U.-E.-H. (2022). Multi-area economic emission dispatch for large-scale multi-fueled power plants contemplating inter-connected grid tie-lines power flow limitations. Energy, 261, 1-16.
Albadi, M. H., Al Farsi, F. N., Hosseinzadeh, N., Al-Badi, A., 2023. Effect of Considering Transmission Losses in Economic Dispatch – A Case Study of Oman’s Main Interconnected System. Journal of Engineering Research, 15(1), pp. 1 – 13.
Anil, A., & Srikanth, N. (2018). Frequency Control in an Autonomous Two-area Hybrid Microgrid using Grasshopper Optimization based Robust PID Controller. IEEE India International Conference on Power Electronics (IICPE). India.
Athab, F. & Majeed, W., 2020. Solving Economic Load Dispatch with Reliability Indicators. Journal of Engineering and SustainableDevelopment, 24(6), pp. 103 - 114.
Azadani, E. N., Hosseinian, S. H., Moradzadeh, B. & Astero, P., 2018. Economic dispatch in multi-area using particle swarm optimization in electricity market. Aswan, Egypt, IEEE.
Azizivahed, A., Karandeh, R., Cecchi, V., Naderi, E., Li, L., & Zhang, J. (2020). Multi-Area Dynamic Economic Dispatch Considering Water Consumption Minimization, Wind Generation, and Energy Storage System. 2020 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT). Washington, DC, USA.
Basu, M., 2019. Multi-area dynamic economic emission dispatch of hydro-wind-thermal power system. Renewable Energy Focus, Volume 28, pp. 11-35.
Chen, C.-L., & Chen, N. (2021). Multi-area economic generation and reserve dispatch. PICA 2001. Innovative Computing for Power - Electric Energy Meets the Market. 22nd IEEE Power Engineering Society. International Conference on Power Industry Computer Applications. Sydney, NSW, Australia.
Chen, R., Zhou, L., Cai, J., Xiong, Y., Zhou, Y., Zhang, X., Chen, X. (2023). Multi-region dynamic economic dispatch with active power real-time balance constraint. Energy Reports, 9(10), 1353-1362.
Chen, X. & Tang, G., 2022. Solving static and dynamic multi-area economic dispatch problems using an improved competitive swarm optimization algorithm. Energy, Volume 238, p. 122035.
Cheng, T., Yang, Z., Dong, B., Li, B., Zhang, Y., Zeng, L., Huang, W., (2021). Dynamic Multi objective Optimal Dispatch of Distribution Network Considering Time of Use–Based Demand Response. Journal of Energy Engineering, 148(2).
Guo, F., Wen, C., Li, G., & Chen, J. (2015). Distributed economic dispatch for a multi-area power system. 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA). Auckland, New Zealand.
Guo, Y., Tong, L., Wu, W., Zhang, B., & Sun, H. (2017). Coordinated Multi-Area Economic Dispatch via Critical Region Projection. IEEE Transactions on Power Systems, 32(5), 3736 - 3746.
Hoballah, A. & Azmy, A. M., 2023. Constrained economic dispatch following generation outage for hot spinning reserve allocation using hybrid grey wolf optimizer. Alexandria Engineering Journal, Volume 62, p. 169–180.
Jadoun, V. K., Gupta, N., Niazi, K. R. & Swarnkar, A., 2015. Multi-area economic dispatch with reserve sharing using dynamically controlled particle swarm optimization. Electrical Power and Energy Systems, Volume 73, p. 743–756.
Jian, L., & Zhou-Jing, W. (2019). Multi-area economic dispatch using an improved stochastic fractal search algorithm. Energy, 166, 47-58.
Karri, C. (2013). Multi Area Economic Dispatch with Tie Line Loss using Secant Method and Tie Line Matrix. International Journal of Applied Power Engineering (IJAPE), 2(3), 115-124.
Kumar, D., Jain, N., & Nangia, U. (2022). Multi area Economic Emission load dispatch using perfectly convergent Particle swarm optimization. 2022 IEEE Delhi Section Conference (DELCON). New Delhi, India.
Kunya, A. B., Abubakar, A. S. & Yusuf, S. S., 2023. Review of economic dispatch in multi-area power system: State-of-the-art and future prospective. Electric Power Systems Research, Volume 217, p. 109089.
Lai, W., Zheng, X., Song, Q., Hu, F., Tao, Q., Chen, H., 2022. Multi-objective membrane search algorithm: A new solution for economic emission dispatch, Applied Energy, 326, 119969.
Lekshmi, R. R. & Balamuruga, S., 2017. Area and Economic Participation Factor Calculation of GENCOs in a Multi Area Competitive Power System, IEEE International Conference on circuits Power and Computing Technologies.
Li, C., 2021. Multi-objective optimization of space adaptive division for environmental economic dispatch. Sustainable Computing: Informatics and Systems, Volume 30, p. 100500.
Musau, M. P., Odero, N. A., & Wekesa, C. W. (2016). Multi area multi objective dynamic economic dispatch with renewable energy and emissions. 2016 IEEE International Energy Conference (ENERGYCON). Leuven, Belgium.
NERC, "Power Generation in Nigeria," Nigerian Electricity Regulatory Commission, 2023. [Online]. Available: https://nerc.gov.ng/index.php/home/nesi/403-generation.
Nguyen, K. P. & Fujita, G., 2017. Multi-Area Economic Dispatch in bulk system using Self-Learning Cuckoo Search Algorithm. Heraklion, Greece, IEEE 52nd International Universities Power Engineering Conference.
Nordström, H., Söder, L. & Eriksson, R., 2024. Continuous power imbalance assessment from multi-area economic dispatch models. Renewable Energy, Volume 225, p. 120277.
Sakthivel, P. V. & Sathya, D. P., 2021. Single and multi-area multi-fuel economic dispatch using a fuzzified squirrel search algorithm. Protection and Control of Modern Power Systems, 6(11), pp. 1-13.
Sakthivel, V., Suman, M. & Sathya, P. D., 2021. Combined economic and emission power dispatch problems through multi-objective squirrel search algorithm. Applied Soft Computing Journal, Volume 100, p. 106950.
Sehrawat, M., & Lather, J. (2019). Multi Area Economic Dispatch using Distributed Intelligence. 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). Noida, India.
Shao, S., Gao, F. & Wu, J., 2023. Distributed Multi-Area Intraday Economic Dispatch Using Modified Critical Region Projection Algorithm. Volume 10.1109/TASE.2023.3252659, pp. 1-14.
Shao, Z., Si, F., Wua, H. & Tong, X., 2021. An agile and intelligent dynamic economic emission dispatcher based on multi-objective proximal policy optimization. Applied Soft Computing Journal, Volume 102, p. 107047.
Sharifian, Y., & Abdi, H., (2025), “Multi-area dynamic economic dispatch considering the demand response and price uncertainty”, Energy, Volume 322, https://doi.org/10.1016/j.energy.2025.135532.
Spea, S. R., 2024. Optimizing economic dispatch problems in power systems using manta ray foraging algorithm: an oppositional-based approach. Computers and Electrical Engineering, Volume 117, p. 109279.
Tan, W., Zhang, H., Yu, M., 2012. Decentralized load frequency control in deregulated environments. Electrical Power and Energy Systems, Volume 41, pp. 16-26.
Wang, Y., Xiong, G., Xu, S. & Suganthan, P. N., 2024. Large-scale power system multi-area economic dispatch considering valve point effects with comprehensive learning differential evolution. Swarm and Evolutionary Computation, Volume 89, p. 101620.
Yang, X. S., & Deb, S. (2009). Cuckoo search via Lévy flights. Proceedings of the 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC) (pp. 210-214). IEEE. https://doi.org/10.1109/NABIC.2009.5393690
Yazdandoost, M., Khazaei, P., Saadatian, S. & Kamali, R., 2018. Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm. Stevenson, WA, USA, IEEE, World Automation Congress.
Yin, L. & Cai, Z., 2024(a). Multimodal multi-objective hierarchical distributed consensus method for multimodal multi-objective economic dispatch of hierarchical distributed power systems. Energy, 295, p. 130996.
Yin, L. & Cai, Z., 2024(b). Multimodal hierarchical distributed multi-objective moth intelligence algorithm for economic dispatch of power systems. Journal of Cleaner Production, 434, p. 140130.
Yin, L. & Sun, Z., 2022. Distributed multi-objective grey wolf optimizer for distributed multi-objective economic dispatch of multi-area interconnected power systems. Applied Soft Computing, Volume 117, pp. 1-15.
Yu, L., Li, D. & Li, N., 2023. Offline economic dispatch for multi-area power system via hierarchical reinforcement learning. Electrical Power and Energy Systems, Volume 152, pp. 1-16.
Zare, M., Narimani, M. R., Malekpour, M., Abarghooee, R. A., Terzija, V., 2021. Reserve constrained dynamic economic dispatch in multi-area power systems: An improved fireworks algorithm. Electrical Power and Energy Systems, Volume 126, p. 106579.
Zhao, J., He, Y., Fang, Y., Weng, Y., Ma, W., Xiao, S., Liang, Y., 2021. Multi-source optimal dispatch considering ancillary service cost of pumped storage power station based on cooperative game, Energy Reports, Volume 7, p. 173–186.
Zhou, Y., Wang, J., Wei, C. & Li, Y., 2024. A novel two-stage multi-objective dispatch model for a distributed hybrid CCHP system considering source-load fluctuations mitigation. Energy, Volume 300, p. 131557.
Zhu, J. & Momoh, J. A., 2014. Multi-area power systems economic dispatch using nonlinear convex network flow programming. Electric Power Systems Research, Vol. 59, p. 13–20.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Nigerian Journal of Technological Development
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
In accordance with the Copyright Act of 1976, which became effective January 1, 1978, the following statement signed by each author must accompany the manuscript submitted: "I, the undersigned author, transfer all copyright ownership of the manuscript referenced above to the Nigerian Journal of Technological Development, in the event the work is published. I warrant that the article is original, does not infringe upon any copyright or other proprietary right of any third party, is not under consideration by another journal, and has not been published previously. I have reviewed and approved the submitted version of the manuscript and agree to its publication in the Nigerian Journal of Technological Development." A copyright transfer form can be downloaded from the NJTD Website (http://njtd.com.ng/index.php/njtd). Author(s) will be consulted, whenever possible, regarding republication of material. All authors must have access to the data presented, and the authors and sponsor (if applicable) must agree to share original data with the editor if requested.
