Optimizing Generating Unit Scheduling for Emission Reduction ‎in Thermal Power Stations with Integrated Solar Energy ‎Systems

  • 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, India
    https://doi.org/10.14419/e2fxpg15

    Received date: May 15, 2025

    Accepted date: May 31, 2025

    Published date: July 8, 2025

  • Emission Reduction; Solar Energy Integration; Optimization Scheduling; Evolutionary Programming
  • Abstract

    Air pollution control from thermal power stations (TPS) is achieved by minimizing flue gases emission. There are many optimization ‎techniques which can be used towards effective emission reduction, but these techniques may often require additional arrangement or ‎devices. This study presents an alternative approach to emissions minimization by scheduling generating units in a strategic manner without ‎the need to add extra equipment. Increasingly expensive fossil fuels and the improving technology for renewable energy can be picked up ‎by solar energy systems to reduce emissions. In this paper, the question of generating unit schedule for a TPS with integrated solar energy ‎‎(SE) systems in order to reduce emissions is addressed. Thus, load dispatch scheduling associated with solar energy can decrease overall ‎emissions with the same power output. The schedule is optimized to account for uncertainties to manage the challenge of predicting real ‎power consumption to improve emission reduction under changing conditions. In the study, reduction of NOx, SO2, and CO2 emissions is ‎evaluated using evolutionary programming. Results show that by adding solar energy systems into the mix, better emission reductions can ‎be achieved compared to the conventional TPS systems alone. In particular, there are major reductions in NOx, SO2, and CO2 emissions ‎when solar energy is combined with marginal adjustments in the generation schedule. Furthermore, the approach enables the understanding ‎of cost consequences from generating unit scheduling. All in all, the proposed method makes TPS operations more efficient by improving ‎environmental benefits‎.

  • References

    1. Cui-Mei, M. A., & Quan-Sheng, G. E. (2014). Method for calculating CO2 emissions from the power sector at the provincial level in Chi-na. Advances in Climate Change Research, 5(2), 92-99. https://doi.org/10.3724/SP.J.1248.2014.092.
    2. Gandhi, N., Prakruthi, B., & Vijaya, C. (2024). Effect of Industrial Emissions on Haematological and Biochemical Parameters of Channa striata Fresh Water Fish. International Journal of Aquatic Research and Environmental Studies, 4(1), 115-139. https://doi.org/10.70102/IJARES/V4I1/10.
    3. Agrawal, K. K., Jain, S., Jain, A. K., & Dahiya, S. (2014). Assessment of greenhouse gas emissions from coal and natural gas thermal power plants using life cycle approach. International Journal of Environmental Science and Technology, 11, 1157-1164. https://doi.org/10.1007/s13762-013-0420-z.
    4. Ramona, P., & Danica, G. (2023). Analysis, Cost Estimation and Optimization of Reinforced Concrete Slab Strengthening by Steel and CFRP Strips. Archives for Technical Sciences, 2(29), 35-48. https://doi.org/10.59456/afts.2023.1529.035P.
    5. Tan, Q., Ding, Y., & Zhang, Y. (2017). Optimization model of an efficient collaborative power dispatching system for carbon emissions trading in China. Energies, 10(9), 1405. https://doi.org/10.3390/en10091405.
    6. Bhattacharya, R., & Kapoor, T. (2024). Advancements in Power Electronics for Sustainable Energy Systems: A Study in the Periodic Series of Multidisciplinary Engineering. In Smart Grid Integration (pp. 19-25). Periodic Series in Multidisciplinary Studies.
    7. Ahmed, I., Rehan, M., Basit, A., & Hong, K. S. (2022). Greenhouse gases emission reduction for electric power generation sector by efficient dis-patching of thermal plants integrated with renewable systems. Scientific Reports, 12(1), 12380. https://doi.org/10.1038/s41598-022-15983-0.
    8. Wei-Liang, C., & Ramirez, S. (2023). Solar-Driven Membrane Distillation for Decentralized Water Purification. Engineering Perspectives in Filtra-tion and Separation, 16-19.
    9. Tyagi, N., Dubey, H. M., & Pandit, M. (2016). Economic load dispatch of wind-solar-thermal system using backtracking search algo-rithm. International Journal of Engineering, Science and Technology, 8(4), 16-27. https://doi.org/10.4314/ijest.v8i4.3.
    10. Mehta, P., & Malhotra, K. (2024). Natural Language Processing for Automated Extraction of Medical Terms in Electronic Health Records. Global Journal of Medical Terminology Research and Informatics, 2(2), 1-4.
    11. 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.
    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. Kaur, S., Brar, Y. S., & Dhillon, J. S. (2020, October). Solar-thermal power scheduling by inserting α-constrained method to nonlinear simplex method with mutations. In 2020 International Conference on Smart Grid and Clean Energy Technologies (ICSGCE) (pp. 27-34). IEEE. https://doi.org/10.1109/ICSGCE49177.2020.9275629.
    14. Srimuang, C., Srimuang, C., & Dougmala, P. (2023). Autonomous flying drones: Agricultural supporting equipment. International Journal of Com-munication and Computer Technologies, 11(2), 7-12.
    15. Ismail, K., & Khalil, N. H. (2025). Strategies and solutions in advanced control system engineering. Innovative Reviews in Engineering and Sci-ence, 2(2), 25-32.
    16. Wang, Y., Xiao, M., Miao, Y., Liu, W., & Huang, Q. (2019). Signature Scheme from Trapdoor Functions. Journal of Internet Services and Infor-mation Security, 9(2), 31-41.
    17. Shao, W., Yan, X., Li, P., Zhang, T., & Xia, Q. (2023). Optimal scheduling of thermal-photovoltaic power generation system considering carbon emission. Energy Reports, 9, 1346-1356. https://doi.org/10.1016/j.egyr.2023.04.205.
    18. Hunker, J., & Probst, C.W. (2011). Insiders and Insider Threats-An Overview of Definitions and Mitigation Techniques. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications, 2(1), 4-27.
    19. Kaur, G., & Dhillon, J. S. (2023). Electricity generation scheduling of thermal-wind-solar energy systems. Electrical Engineering, 105(6), 3549-3579. https://doi.org/10.1007/s00202-023-01873-9.
    20. Yıldız, H., & Miçooğulları, Ü. (2022). Effects of Propolis on Serum Biochemical Parameters in Azaserine Treated Rats. Natural and Engineering Sciences, 7(2), 89-96. https://doi.org/10.28978/nesciences.1142700.
    21. Sun, J. (2024). Self-operation and low-carbon scheduling optimization of solar thermal power plants with thermal storage systems. Energy Informat-ics, 7(1), 30. https://doi.org/10.1186/s42162-024-00332-4.
    22. Rathore, N., & Shaikh, A. (2023). Urbanization and Fertility Transitions: A Comparative Study of Emerging Economies. Progression journal of Human Demography and Anthropology, 17-20. https://doi.org/10.1016/S0378-7796(99)00021-8.
    23. Hota, P. K., Chakrabarti, R., & Chattopadhyay, P. K. (1999). Short-term hydrothermal scheduling through evolutionary programming tech-nique. Electric Power Systems Research, 52(2), 189-196. https://doi.org/10.1002/etep.413.
    24. Hemamalini, S., & Simon, S. P. (2011). Dynamic economic dispatch using artificial bee colony algorithm for units with valve‐point ef-fect. European Transactions on Electrical Power, 21(1), 70-81. https://doi.org/10.1002/etep.413
    25. Fang, N., Zhou, J., Zhang, R., Liu, Y., & Zhang, Y. (2014). A hybrid of real coded genetic algorithm and artificial fish swarm algorithm for short-term optimal hydrothermal scheduling. International Journal of Electrical Power & Energy Systems, 62, 617-629. https://doi.org/10.1016/j.ijepes.2014.05.017.
    26. Das, S., Bhattacharya, A., & Chakraborty, A. K. (2018). Fixed head short-term hydrothermal scheduling in presence of solar and wind pow-er. Energy strategy reviews, 22, 47-60. https://doi.org/10.1016/j.esr.2018.08.001.
    27. Alquthami, T., Butt, S. E., Tahir, M. F., & Mehmood, K. (2020). Short-term optimal scheduling of hydro-thermal power plants using artificial bee colony algorithm. Energy Reports, 6, 984-992. https://doi.org/10.1016/j.egyr.2020.04.003.
    28. Kaushal, R. K., & Kaur, H. (2023, February). Particle Swarm Optimization for Short-Term Scheduling of Thermal-Hydro-Solar Power Generation Systems. In IOP Conference Series: Earth and Environmental Science (Vol. 1110, No. 1, p. 012026). IOP Publishing. https://doi.org/10.1088/1755-1315/1110/1/012026.
    29. Kumar, T. M. S. (2024). Low-power communication protocols for IoT-driven wireless sensor networks. Journal of Wireless Sensor Networks and IoT, 1(1), 37-43. https://doi.org/10.31838/WSNIOT/01.01.06.
    30. Orozco, L., & Ttofis, H. (2025). Energy harvesting techniques for sustainable embedded systems: Design and applications. SCCTS Journal of Em-bedded Systems Design and Applications, 2(1), 67–78.
    31. Poornimadarshini, S. (2025). Topology Optimization of Brushless DC Machines for Low-Noise and High-Torque Applications. National Journal of Electrical Machines & Power Conversion, 45-51.
    32. Achehboune, M., Sani, A., & Boukdir, M. (2025). A note on maximal regularity in relation with measure theory. Results in Nonlinear Analy-sis, 8(1), 193-203.
  • Downloads

  • How to Cite

    Rathod, M. C. ., & Vyas, S. R. . (2025). Optimizing Generating Unit Scheduling for Emission Reduction ‎in Thermal Power Stations with Integrated Solar Energy ‎Systems. International Journal of Basic and Applied Sciences, 14(SI-1), 211-219. https://doi.org/10.14419/e2fxpg15