A novel generation expansion planning in older power plants : hybrid spotted hyena-particle swarm optimization

• Autorzy: Arun Kumar A , Suresh S , Ramkumar A , Bhuvanesh A

Identyfikatory

DOI

10.24425/opelre.2024.150612

• Języki publikacji: EN

Abstrakty

EN

Due to their lower productivity, lower reliability, and lower economic stability, older power plants are leading to higher carbon emissions. Rather than simply focusing on the retirement and recuperation of power plants, this study focuses on generation expansion planning (GEP). Considering recuperation is economically and environmentally beneficial to power the power generating company. These criteria have made the GEP problem more complex. Hence, the applications of optimization algorithms are required to solve these complex, constrained, and large-scale problems. In this study, an effective hybrid spotted hyena-particle swarm optimization (HSHPSO) algorithm is proposed to handle the GEP problem for the Tamil Nadu power system. This case study addresses the GEP problem for a 7-year planning horizon (2020-2027), as well as a 14-year planning horizon (2020-2034). A significant reduction in total cost and pollution occurs by including retirement and recuperation in GEP. To prove the effectiveness of the proposed HSHPSO technique, it is compared with the existing technologies such as particle swarm optimization (PSO) and differential evolution (DE). Compared to GEP with no recuperation or retirement, the total cost and CO₂ emissions of the GEP have been reduced by 11.07% and 9.48%, respectively. Also, the results demonstrate that the HSHPSO algorithm outperformed other algorithms.

Słowa kluczowe

EN

generation expansion planning; particle swarm optimization; spotted hyena optimization; retirement; recuperation; Tamil Nadu

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2024
• Tom: Vol. 32, No. 3
• Strony: art. no. e150612
• Opis fizyczny: Bibliogr. 45 poz., rys., tab., wykr.

Twórcy

autor

Arun Kumar A

• Department of Electrical and Electronics Engineering, Ramco Institute of Technology, Rajapalayam, Tamil Nadu, India

autor

Suresh S

• Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

autor

Ramkumar A

• Department of Electrical and Electronics Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamil Nadu, India

autor

Bhuvanesh A

• Department of Electrical and Electronics Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India

Bibliografia

• [1] Kerzner, H. Project Management: A Systems Approach to Planning, Scheduling, and Controlling. 12th Edition. (Wiley, 2019).
• [2] Kaldellis, J., Vlachou, D. S. & Korbakis, G. Techno-economic evaluation of small hydro power plants in Greece: a complete sensitivity analysis. Energy Policy 33, 1969-1985 (2005). https://doi.org/10.1016/j.enpol.2004.03.018.
• [3] IEEE Guide for the Rehabilitation of Hydroelectric Power Plants. in IEEE Std 1147-2005) (Revision of IEEE Std 1147-1991) 1-63 (IEEE, 2006). https://doi.org/10.1109/ieeestd.2006.99379.
• [4] Kannan, S., Slochanal, S. M. R. & Padhy, N. P. Application and comparison of metaheuristic techniques to generation expansion planning problem. IEEE Trans. Power Syst. 20, 466-475 (2005). https://doi.org/10.1109/tpwrs.2004.840451.
• [5] Bhuvanesh, A., Christa, S. T. J., Kannan, S., Pandiyan, M. K. & Gangatharan, K. Application of optimization algorithms to generation expansion planning problem. J. Intell. Fuzzy Syst. 35, 1387-1398 (2018). https://doi.org/10.3233/jifs-169681.
• [6] Sanz-Bermejo, J., Gallardo-Natividad, V., Gonzalez-Aguilar, J. & Romero, M. Comparative system performance analysis of direct steam generation central receiver solar thermal power plants in megawatt range. J. Sol. Energy Eng. 136, 010908-1 (2014). https://doi.org/10.1115/1.4026279.
• [7] Lu, J., Li, G., Cheng, C. & Yu, H. Risk analysis method of cascade plants operation in medium term based on multi-scale market and settlement rules. IEEE Access 8, 90730-90740 (2020). https://doi.org/10.1109/access.2020.2994093.
• [8] Ding, H., Hu, Z. & Song, Y. Stochastic optimization of the daily operation of wind farm and pumped-hydro-storage plant. Renew. Energy 48, 571-578 (2012). https://doi.org/10.1016/j.renene.2012.06.008.
• [9] Weisser, D. & Garcia, R. S. Instantaneous wind energy penetration in isolated electricity grids: concepts and review. Renew. Energy 30, 1299-1308 (2005). https://doi.org/10.1016/j.renene.2004.10.002.
• [10] Kannan, S., Slochanal, S. M. R., Subbaraj, P. & Padhy, N. P. Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electr. Power Syst. Res. 70, 203-210 (2004). https://doi.org/10.1016/j.epsr.2003.12.009.
• [11] Neshat, N. & Amin-Naseri, M. R. Cleaner power generation through market-driven generation expansion planning: an agent-based hybrid framework of game theory and Particle Swarm Optimization. J. Clean. Prod. 105, 206-217 (2015). https://.doi.org/10.1016/j.jclepro.2014.10.083.
• [12] Moghddas-Tafreshi, S. M., Shayanfar, H. A., Saliminia Lahiji, A., Rabiee, A. & Aghaei, J. Generation expansion planning in Pool market: A hybrid modified game theory and particle swarm optimization. Energy Convers. Manag. 52, 1512-1519 (2011). https://doi.org/10.1016/j.enconman.2010.10.019.
• [13] Jin, Y.-X., Cheng, H.-Z., Yan, J.-y. & Zhang, L. New discrete method for particle swarm optimization and its application in transmission network expansion planning. Electr. Power Syst. Res. 77, 227-233 (2007). https://doi.org/10.1016/j.epsr.2006.02.016.
• [14] Abdmouleh, Z., Gastli, A., Ben-Brahim, L., Haouari, M. & Al-Emadi, N. A. Review of optimization techniques applied for the integration of distributed generation from renewable energy sources. Renew. Energy 113, 266-280 (2017). https://doi.org/10.1016/j.renene.2017.05.087.
• [15] Yousefpour, K., Molla, S. J. H. & Hosseini, S. A dynamic approach for distribution system planning using Particle Swarm Optimization. Int. J. Control Sci. Eng. 5, 10-17 (2015). http://article.sapub.org/10.5923.j.control.20150501.02.html
• [16] Lee, K. Y. & Park, J. Application of Particle Swarm Optimization to Economic Dispatch Problem: Advantages and Disadvantages. in 2006 IEEE Power Systems Conference and Exposition 188-192 (IEEE, 2006). https://doi.org/10.1109/psce.2006.296295.
• [17] Wong, L. Y., Rahim, S. R. A., Sulaiman, M. H. & Aliman, O. Distributed Generation Installation Using Particle Swarm Optimization. in 2010 4th International Power Engineering and Optimization Conference (PEOCO) 159-163 (IEEE, 2010). https://doi.org/10.1109/peoco.2010.5559168.
• [18] Bhuvanesh, A. et al. Application of differential evolution algorithm and its variants for solving energy storage technologies integrated generation expansion planning. Iran. J. Sci. Technol. Trans. Electr. Eng. 43, 883-896 (2019). https://doi.org/10.1007/s40998-019-00190-x.
• [19] Bhuvanesh, A., Jaya Christa, S. T., Kannan, S. & Karuppasamy Pandiyan, M. Aiming towards pollution free future by high penetration of renewable energy sources in electricity generation expansion planning. Futures 104, 25-36 (2018). https://doi.org/10.1016/j.futures.2018.07.002.
• [20] Rajesh, K., Kannan, S. & Thangaraj, C. Least cost generation expansion planning with wind power plant incorporating emission using Differential Evolution algorithm. Int. J. Electr. Power Energy Syst. 80, 275-286 (2016). https://doi.org/10.1016/j.ijepes.2016.01.047.
• [21] Franco, A. & Salza, P. Perspectives for the long-term penetration of new renewables in complex energy systems: the Italian Case. Environ. Dev. Sustain. 13, 309-330 (2011). https://doi.org/10.1007/s10668-010-9263-7.
• [22] Praso, N. & Dzindo, M. Rehabilitation and conversion of coal-based power plants to cogeneration plants for increased efficiency. Cogener. Distrib. Gener. J. 20, 48-69 (2005). https://doi.org/10.1080/15453660509509063.
• [23] Ardizzon, G., Cavazzini, G. & Pavesi G. A new generation of small hydro and pumped-hydro power plants: Advances and future challenges. Renew. Sustain. Energy Rev. 31, 746-761 (2014). https://doi.org/10.1016/j.rser.2013.12.043.
• [24] Adhau, S. P., Moharil, R. M. & Adhau, P. G. Mini-hydro power generation on existing irrigation projects: Case study of Indian sites. Renew. Sustain. Energy Rev. 16, 4785-4795 (2012). https://doi.org/10.1016/j.rser.2012.03.066.
• [25] Hernandez, T. & Diaz, E. Risk Analysis in the Rehabilitation of Hydro Unit at the Guri Plant. in 2006 IEEE/PES Transmission and Distribution Conference and Exposition 1-5 (IEEE, 2006). https://doi.org/10.1109/tdcla.2006.311483.
• [26] Fette, M., Weber, C., Peter, A. & Wehrli, B. Hydropower production and river rehabilitation: A case study on an alpine river. Environ. Model. Assess. 12, 257-267 (2007). https://doi.org/10.1007/s10666-006-9061-7.
• [27] Karunanithi, K., Ramesh, S., Raja, S. P. & Rowlo, P. K. Genera-tion expansion planning considering environmental impact and sustainable development for an Indian state using the LEAP platform. Util. Policy 86, 101702 (2024). https://doi.org/10.1016/j.jup.2023.101702.
• [28] Choubineh, K., Yousefi, H. & Moeini-Aghtaie, M. Developing a new flexibility-oriented model for generation expansion planning studies of renewable-based energy systems. Energy Rep. 11, 706-719 (2024). https://doi.org/10.1016/j.egyr.2023.12.019.
• [29] da Luz, T. An adaptive peak load day for bidirectional soft-linking approach in generation expansion planning considering short-term problem. Int. J. Electr. Power Energy Syst. 157, 109818 (2024). https//doi.org/10.1016/j.ijepes.2024.109818.
• [30] Tawney, R. K., Bonner, J. A. & Elgawhary, A. M. Economic and Performance Evaluation of Combined Cycle Repowering Options. in ASME Turbo Expo: Power for Land, Sea, and Air 457-464 (ASME, 2002). https://doi.org/10.1115/gt2002-30565.
• [31] Andreini, A. & Facchini, B. Gas Turbines Design and Off-Design Performance Analysis with Emissions Evaluation. in ASME Turbo Expo: Power for Land, Sea, and Air 271-281 (ASME, 2002). https://doi.org/10.1115/gt2002-30258.
• [32] Pandzic, H., Conejo, A., Kuzle, I. & Caro, E. Yearly maintenance scheduling of transmission lines within a market environment. IEEE Transactions on Power Systems 27, 407-415 (2012). https://doi.org/10.1109/tpwrs.2011.2159743.
• [33] Karunanithi, K., Kannan, S. & Thangaraj, C. Generation expansion planning for Tamil Nadu: a case study. Int. Trans. Electr. Energy Syst. 25, 1771-1787 (2015). https://doi.org/10.1002/etep.1929.
• [34] U.S. Energy information Administration. Capital Cost and Performance Characteristics for Utility-Scale Electric Power Generating Technologies (2024). https://www.eia.gov/analysis/studies/powerplants/capitalcost/
• [35] National Renewable Energy Laboratory. 2018 Annual Technology Baseline ATB Cost and Performance Data for Electricity Generation Technologies - Interim Data without Geothermal Updates. USA (2018). https://data.nrel.gov/submissions/89
• [36] Government of Tamil Nadu. The Vision Tamil Nadu 2023. Strategic Plan for Infrastructure Development in Tamil Nadu (2014). https://agritech.tnau.ac.in/pdf/2012/TN%20VISION%202023_Volume%20II.pdf.
• [37] Government of India. Ministry of Power. Central Electricity Authority. All India Installed Capacity (in MW) of Power Stations. New Delhi, India (2021). https://cea.nic.in/installed-capacity-report/?lang=en
• [38] Bhuvanesh, A., Jaya Christa, S. T., Kannan, S. & Karuppasamy Pandiyan, M. Multistage multiobjective electricity generation expansion planning for Tamil Nadu considering least cost and minimal GHG emission. Int. Trans. Electr. Energy Syst. 29, e2708 (2019). https://doi.org/10.1002/etep.2708.
• [39] Dhiman, G. & Kumar, V. Multi-objective spotted hyena optimizer: a multi-objective optimization algorithm for engineering problems. Knowl.-Based Syst. 150, 75-197 (2018). https://doi.org/10.1016/j.knosys.2018.03.011.
• [40] Yıldız, B. S. The spotted hyena optimization algorithm for weight-reduction of automobile brake components. Mater. Test. 62, 383-388 (2020). https://doi.org/10.3139/120.111495.
• [41] Das, S. R., Sahoo, A. K., Dhiman, G., Singh, K. K. & Singh, A. Photo voltaic integrated multilevel inverter-based hybrid filter using spotted hyena optimizer. Comput. Electr. Eng. 96, 107510 (2021). https://doi.org/10.1016/j.compeleceng.2021.107510.
• [42] Li, X. & Yao, X. Cooperatively coevolving particle swarms for large scale optimization. IEEE Trans. Evol. Comput. 16, 210-224 (2012). https://doi.org/10.1109/tevc.2011.2112662.
• [43] Zhou, J., Fang, W., Wu, X., Sun, J. & Cheng, S. An Opposition-Based Learning Competitive Particle Swarm Optimizer. in 2016 IEEE Congress on Evolutionary Computation (CEC) 515-521 (IEEE, 2016). https://doi.org/10.1109/cec.2016.7743837.
• [44] Deb, K., Mohan, M. & Mishra, B. A Fast Multi-Objective Evolutionary Algorithm for Finding Well-Spread Pareto-Optimal Solutions. KanGAL report no. 2003002 (2003). https://www.egr.msu.edu/~kdeb/papers/k2003002.pdf.
• [45] Ministry of Science and Technology. India Is Committed To Achieve The Net Zero Emissions Target By 2070 (2023). https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1961797

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).