Minimizing Generation Fuel Cost in Thermal Solar Power Plant ‎Utilizing Evolutionary Programming Approach

  • Authors

    • Mihirkumar C. Rathod Research Scholar, Engineering & Technology, Kadi Sarva Vishwavidyalaya, Gujarat, India
    • Sanjay R. Vyas Department of Electrical Engineering, LDRP Institute of Technology & Research, Gujarat
    https://doi.org/10.14419/a1bj7r81

    Received date: May 15, 2025

    Accepted date: May 31, 2025

    Published date: July 8, 2025

  • Fuel Cost Reduction; Solar Energy Integration; Optimization Scheduling; Evolutionary Algorithm

  • Abstract

    Minimizing generation fuel costs in thermal solar power plants is crucial for enhancing their economic viability and operational efficiency. ‎This paper addresses the problem of generating unit scheduling in a TPS with integrated solar energy systems to achieve fuel cost reduction. ‎Load dispatch scheduling, combined with solar energy, can significantly reduce overall fuel cost while maintaining the same power output. ‎The challenge of predicting real-time power requirements is managed by optimizing the schedule to account for uncertainties, thereby ‎improving fuel cost reduction. This paper presents an innovative approach to optimize fuel consumption in hybrid thermal solar power ‎systems by employing Evolutionary Programming (EP). EP, a powerful evolutionary algorithm inspired by natural evolution, is utilized to ‎determine the optimal set of operational parameters that minimize the overall fuel cost while maintaining system performance. The approach ‎is based on evolving a population of potential solutions through processes of mutation and selection, where the fitness function reflects the ‎fuel cost associated with power generation and system constraints. The proposed methodology is implemented within a thermal solar power ‎plant model framework. The results indicate the efficacy of the EP approach in attaining cost-effective solutions, underscoring substantial ‎reductions in fuel consumption and operational expenditures. This investigation significantly contributes to the progression of sustainable ‎energy solutions by offering a robust and efficient optimization instrument for the management of thermal solar power plants. Overall, the ‎proposed method markedly enhances the operational efficiency of thermal power systems‎.

  • References

    1. Beerbaum, S., & Weinrebe, G. (2000). Solar thermal power generation in India—a techno–economic analysis. Renewable energy, 21(2), 153-174. https://doi.org/10.1016/S0960-1481(00)00006-9.
    2. Rahim, A. R., Utami, D. R., & Budi, S. (2023). Optimizaton quality of Agar Gracilaria verrucosa Seaweed with different density in extensive poly-culture system. International Journal of Aquatic Research and Environmental Studies, 3(2), 1-16. https://doi.org/10.70102/IJARES/V3I2/1.
    3. Jamel, M. S., Abd Rahman, A., & Shamsuddin, A. H. (2013). Advances in the integration of solar thermal energy with conventional and non-conventional power plants. Renewable and Sustainable Energy Reviews, 20, 71-81. https://doi.org/10.1016/j.rser.2012.10.027.
    4. Sethi, K., & Jain, R. (2024). Smart Grid Technologies and Renewable Integration: Contributions to the Periodic Series in Electrical and Environ-mental Innovation. In Smart Grid Integration (pp. 26-32). Periodic Series in Multidisciplinary Studies.
    5. Platzer, W. (2014). PV–enhanced solar thermal power. Energy Procedia, 57, 477-486. https://doi.org/10.1016/j.egypro.2014.10.201.
    6. Karimov, N., Sarybaev, M., Kaipnazarov, A., Djumageldiev, N., Reymbaev, R., & Kholdarova, F. (2024). Historical Development of Construction Techniques: from Ancient Architecture to Modern Engineering. Archives for Technical Sciences, 2(31), 36–48. https://doi.org/10.70102/afts.2024.1631.036.
    7. Reddy, S. S. (2017). Optimal scheduling of thermal-wind-solar power system with storage. Renewable energy, 101, 1357-1368. https://doi.org/10.1016/j.renene.2016.10.022.
    8. Bhardwaj, S., & Ramesh, T. (2024). Advanced Nanofiber Filters for Sterile Filtration in Biopharmaceutical Processes. Engineering Perspectives in Filtration and Separation, 5-7.
    9. SAIDOU, S. Y., & Gamze, G. E. N. Ç. (2024). Thermodynamic, economical and environmental performance evaluation of a 330 MW solar-aided coal-fired power plant located in Niger. Process Safety and Environmental Protection, 191, 1324-1338. https://doi.org/10.1016/j.psep.2024.09.027.
    10. Kapoor, A., & Gupta, R. (2023). Development of a Real-Time Multilingual Medical Terminology Translator for Emergency Settings. Global Jour-nal of Medical Terminology Research and Informatics, 1(1), 16-19.
    11. Wagner, M. J., Hamilton, W. T., Newman, A., Dent, J., Diep, C., & Braun, R. (2018). Optimizing dispatch for a concentrated solar power tow-er. Solar Energy, 174, 1198-1211. https://doi.org/10.1016/j.solener.2018.06.093.
    12. Suvarna, N. A., & Bharadwaj, D. (2024). Optimization of System Performance through Ant Colony Optimization: A Novel Task Scheduling and Information Management Strategy for Time-Critical Applications. Indian Journal of Information Sources and Services, 14(2), 167–177. https://doi.org/10.51983/ijiss-2024.14.2.24.
    13. Zaman, M. F., Elsayed, S., Ray, T., & Sarker, R. (2015). An evolutionary approach for scheduling solar-thermal power generation system. In International Conference on Computers & Industrial Engineering (CIE45) (Vol. 45).
    14. Vardhan, H., & Bhattacharya, R. (2025). The Impact of Sustainable Practices on Business Performance. International Journal of SDG’s Prospects and Breakthroughs, 3(1), 15-21.
    15. Salkuti, S. R. (2019). Day-ahead thermal and renewable power generation scheduling considering uncertainty. Renewable energy, 131, 956-965. https://doi.org/10.1016/j.renene.2018.07.106.
    16. Aqlan, A., Saif, A., & Salh, A. (2023). Role of IoT in urban development: A review. International Journal of Communication and Computer Tech-nologies, 11(2), 13-18.
    17. Carvalho, F. M., & Perscheid, T. (2025). Fault-tolerant embedded systems: Reliable operation in harsh environments approaches. SCCTS Journal of Embedded Systems Design and Applications, 2(2), 1–8.
    18. Jara, A. J., Zamora, M. A., & Gómez-Skarmeta, A. F. (2010). An Initial Approach to Support Mobility in Hospital Wireless Sensor Networks based on 6LoWPAN (HWSN6). Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 1(2/3), 107-122. https://doi.org/10.1109/ICWMC.2010.54.
    19. Rahimi, M., Ardakani, F. J., & Ardakani, A. J. (2021). Optimal stochastic scheduling of electrical and thermal renewable and non-renewable re-sources in virtual power plant. International Journal of Electrical Power & Energy Systems, 127, 106658. https://doi.org/10.1016/j.ijepes.2020.106658.
    20. Ahmed, I., Bano, A., & Siddique, S. (2022). Relative gut length and gastro-somatic index of Acanthopagrus arabicus (Iwatsuki, 2013) from the Offshore Waters of Pakistan. Natural and Engineering Sciences, 7(1), 67-79. https://doi.org/10.28978/nesciences.1098678.
    21. Giuntoli, M., & Poli, D. (2013). Optimized thermal and electrical scheduling of a large scale virtual power plant in the presence of energy storag-es. IEEE Transactions on Smart Grid, 4(2), 942-955. https://doi.org/10.1109/TSG.2012.2227513.
    22. Rajan, V., & Chawla, R. (2024). Anthropometric Variations and Adaptations across Diverse Ecological Zones. Progression journal of Human De-mography and Anthropology, 2(1), 1-4.
    23. Patel, V., Saha, B., & Chatterjee, K. (2014, December). Fuel saving in coal-fired power plant with augmentation of solar energy. In 2014 Interna-tional Conference on Power, Control and Embedded Systems (ICPCES) (pp. 1-5). IEEE. https://doi.org/10.1109/ICPCES.2014.7062811.
    24. Purohit, I., & Purohit, P. (2017). Technical and economic potential of concentrating solar thermal power generation in India. Renewable and Sus-tainable Energy Reviews, 78, 648-667. https://doi.org/10.1016/j.rser.2017.04.059.
    25. González-Portillo, L. F., Muñoz-Antón, J., & Martínez-Val, J. M. (2017). An analytical optimization of thermal energy storage for electricity cost reduction in solar thermal electric plants. Applied energy, 185, 531-546. https://doi.org/10.1016/j.apenergy.2016.10.134.
    26. Martinek, J., Jorgenson, J., Mehos, M., & Denholm, P. (2018). A comparison of price-taker and production cost models for determining system value, revenue, and scheduling of concentrating solar power plants. Applied energy, 231, 854-865. https://doi.org/10.1016/j.apenergy.2018.09.136.
    27. Soomro, M. I., Mengal, A., Shafiq, Q. N., Ur Rehman, S. A., Soomro, S. A., & Harijan, K. (2019). Performance improvement and energy cost re-duction under Different scenarios for a parabolic Trough Solar Power Plant in the Middle-East Region. Processes, 7(7), 429. https://doi.org/10.3390/pr7070429.
    28. Tafuni, A., Giannotta, A., Mersch, M., Pantaleo, A. M., Amirante, R., Markides, C. N., & De Palma, P. (2023). Thermo-economic analysis of a low-cost greenhouse thermal solar plant with seasonal energy storage. Energy Conversion and Management, 288, 117123. https://doi.org/10.1016/j.enconman.2023.117123.
    29. Sun, J. (2024). Self-operation and low-carbon scheduling optimization of solar thermal power plants with thermal storage systems. Energy Infor-matics, 7(1), 30. https://doi.org/10.1186/s42162-024-00332-4.
    30. Rahim, R. (2024). Scalable architectures for real-time data processing in IoT-enabled wireless sensor networks. Journal of Wireless Sensor Networks and IoT, 1(1), 44-49. https://doi.org/10.31838/WSNIOT/01.01.07.
    31. Ariunaa, K., Tudevdagva, U., & Hussai, M. (2025). The need for chemical sustainability in advancing sustainable chemistry. Innovative Reviews in Engineering and Science, 2(2), 33-40.
    32. Madhanraj. (2025).AI-Powered Energy Forecasting Models for Smart Grid-Integrated Solar and Wind Systems.National Journal of Renewable En-ergy Systems and Innovation, 1-7.

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  • How to Cite

    Rathod, M. C. ., & Vyas, S. R. . (2025). Minimizing Generation Fuel Cost in Thermal Solar Power Plant ‎Utilizing Evolutionary Programming Approach. International Journal of Basic and Applied Sciences, 14(SI-1), 220-227. https://doi.org/10.14419/a1bj7r81