Research Article
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Year 2022, , 957 - 967, 01.09.2022
https://doi.org/10.35378/gujs.942680

Abstract

References

  • [1] International Energy Agency, “Status of power system transformation 2019- Power system flexibility”, Paris, France, (2019).
  • [2] Schwele, A., Kazempour, J., Pinson, P., “Do unit commitment constraints affect generation expansion planning?. A scalable stochastic model”, Energy Systems, 11: 247-282, (2020).
  • [3] Koltsaklis, N.E., Dagoumas, A.S., “State-of-the-art generation expansion planning: A review”, Applied Energy, 230: 563-589, (2018).
  • [4] Jeong, Y-W., Park, J-B., Shin, J-R., Kwang, Y.L., “A thermal unit commitment approach using an improved quantum evolutionary algorithm”, Electric Power Components and Systems, 37(7): 770-786, (2009).
  • [5] Saravanan, B., Das, S., Sıkrı, S., Kotharı, D.P., “A solution to the unit commitment problem-a review”, Frontiers in Energy, 7(2): 223-236, (2013).
  • [6] Kazarlis, S.A., Bakirtzis, A.G., Petridis, V., “A genetic algorithm solution to the unit commitment problem”, IEEE Transactions on Power Systems, 11(1): 83-92, (1996).
  • [7] Uyar, A.Ş., Türkay, B.,“Evolutionary algorithms for the unit commitment problem”, Turkish Journal of Electrical Engineering and Computer Science, 16(3): 239-255, (2008).
  • [8] Najafı, A., Farshad, M., Falaghi, H., “A new heuristic method to solve unit commitment by using a time-variant acceleration coefficients particle swarm optimization algorithm”, Turkish Journal of Electrical Engineering & Computer Sciences, 23: 354-369, (2015).
  • [9] Simon, S.P., Padhy, N.P., Anand, R.S., “An ant colony system approach for unit commitment problem”, International Journal of Electrical Power & Energy Systems, 28(5): 315-323 (2006).
  • [10] Nemati, M., Braun, M., Tenbohlen S., “Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming”, Applied Energy, 210: 944-963, (2018).
  • [11] Alkanoğlu, U., “Short term unit commitment by using genetic algorithms”, MSc Thesis, Kocaeli University, Kocaeli, Turkey, (2007).
  • [12] Gil, E., Bustos, J., Rudnick, H., “Short-term hydrothermal generation scheduling model using a genetic algorithm”, IEEE Transactions on Power Systems, 18(4): 1256-1264, (2003).
  • [13] “Average Spot Price for Sulfur Dioxide (SO2) Emissions Allowances– 2019”, United States Environmental Protection Agency, (2020). https://www.epa.gov/airmarkets/2019-so2-allowance-auction, 2019.Access date:13.09.2020.
  • [14] “State and Trends of Carbon Pricing 2019”, International Bank for Reconstruction and Development, Washinghton DC, USA, (2019). https://openknowledge.worldbank.org/handle/10986/31755. Access date: 14.09.2020.
  • [15] “2018 CSAPR NOX Annual Program Allowances”, United States Environmental Protection Agency, 018 Power Sector Programs - Progress Report, (2020). https://www3.epa.gov/airmarkets/progress/reports/index.html. Access date: 13.09.2020. September 2020].
  • [16] Sapmaz, M.E., “A genetic algorithms solution to the unit commitment problem”, MSc, Istanbul Technical University, Istanbul, Turkey, (2004).
  • [17] “Balancing and Frequency Control Basics”, North American Electric Reliability Corporation, Princeton, USA, (2011). https://www.nerc.com/docs/oc/rs/NERC%20Balancing%20and%20Frequency%20Control%20040520111.pdf. Access date: 13.09.2020.
  • [18] “Lifecycle Emissions from Power Generating Technologies, The Cost of Power Generation- The Current and Future Competitiveness of Renewable And Traditional Technologies”, Business Insights, (2010).

Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm

Year 2022, , 957 - 967, 01.09.2022
https://doi.org/10.35378/gujs.942680

Abstract

A power system’s operating cost needs to be minimized by satisfying varying load demand while taking into account the prevailing constraints in a multiple unit electrical power system. In this study, by using genetic algorithms (GA), a short-term thermal unit commitment problem was solved and an economical generating unit schedule was made with the solution obtained. Taking into account the negative effects of emissions due to the use of fossil fuels, emission costs were added to the objective function together with fuel and start-up costs. The GA chromosome structure was formed by binary encoding, new generations were selected by roulette wheel selection mechanism and single point crossover was applied. The representation, formulation and the simulation results of the problem for a 5-unit test system during the scheduling hours of the period are presented. The number and the operating hours of the generating units to be committed were determined by satisfying the prevailing constraints. During the planning period, 13360 MW of power demand was met by 755 MW of spinning reserve. Total operating cost was calculated as $430330. Of the total operating cost, 32% consists of emission costs.

References

  • [1] International Energy Agency, “Status of power system transformation 2019- Power system flexibility”, Paris, France, (2019).
  • [2] Schwele, A., Kazempour, J., Pinson, P., “Do unit commitment constraints affect generation expansion planning?. A scalable stochastic model”, Energy Systems, 11: 247-282, (2020).
  • [3] Koltsaklis, N.E., Dagoumas, A.S., “State-of-the-art generation expansion planning: A review”, Applied Energy, 230: 563-589, (2018).
  • [4] Jeong, Y-W., Park, J-B., Shin, J-R., Kwang, Y.L., “A thermal unit commitment approach using an improved quantum evolutionary algorithm”, Electric Power Components and Systems, 37(7): 770-786, (2009).
  • [5] Saravanan, B., Das, S., Sıkrı, S., Kotharı, D.P., “A solution to the unit commitment problem-a review”, Frontiers in Energy, 7(2): 223-236, (2013).
  • [6] Kazarlis, S.A., Bakirtzis, A.G., Petridis, V., “A genetic algorithm solution to the unit commitment problem”, IEEE Transactions on Power Systems, 11(1): 83-92, (1996).
  • [7] Uyar, A.Ş., Türkay, B.,“Evolutionary algorithms for the unit commitment problem”, Turkish Journal of Electrical Engineering and Computer Science, 16(3): 239-255, (2008).
  • [8] Najafı, A., Farshad, M., Falaghi, H., “A new heuristic method to solve unit commitment by using a time-variant acceleration coefficients particle swarm optimization algorithm”, Turkish Journal of Electrical Engineering & Computer Sciences, 23: 354-369, (2015).
  • [9] Simon, S.P., Padhy, N.P., Anand, R.S., “An ant colony system approach for unit commitment problem”, International Journal of Electrical Power & Energy Systems, 28(5): 315-323 (2006).
  • [10] Nemati, M., Braun, M., Tenbohlen S., “Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming”, Applied Energy, 210: 944-963, (2018).
  • [11] Alkanoğlu, U., “Short term unit commitment by using genetic algorithms”, MSc Thesis, Kocaeli University, Kocaeli, Turkey, (2007).
  • [12] Gil, E., Bustos, J., Rudnick, H., “Short-term hydrothermal generation scheduling model using a genetic algorithm”, IEEE Transactions on Power Systems, 18(4): 1256-1264, (2003).
  • [13] “Average Spot Price for Sulfur Dioxide (SO2) Emissions Allowances– 2019”, United States Environmental Protection Agency, (2020). https://www.epa.gov/airmarkets/2019-so2-allowance-auction, 2019.Access date:13.09.2020.
  • [14] “State and Trends of Carbon Pricing 2019”, International Bank for Reconstruction and Development, Washinghton DC, USA, (2019). https://openknowledge.worldbank.org/handle/10986/31755. Access date: 14.09.2020.
  • [15] “2018 CSAPR NOX Annual Program Allowances”, United States Environmental Protection Agency, 018 Power Sector Programs - Progress Report, (2020). https://www3.epa.gov/airmarkets/progress/reports/index.html. Access date: 13.09.2020. September 2020].
  • [16] Sapmaz, M.E., “A genetic algorithms solution to the unit commitment problem”, MSc, Istanbul Technical University, Istanbul, Turkey, (2004).
  • [17] “Balancing and Frequency Control Basics”, North American Electric Reliability Corporation, Princeton, USA, (2011). https://www.nerc.com/docs/oc/rs/NERC%20Balancing%20and%20Frequency%20Control%20040520111.pdf. Access date: 13.09.2020.
  • [18] “Lifecycle Emissions from Power Generating Technologies, The Cost of Power Generation- The Current and Future Competitiveness of Renewable And Traditional Technologies”, Business Insights, (2010).
There are 18 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Electrical & Electronics Engineering
Authors

Mehmet Yıldırım 0000-0003-1676-1560

Mustafa Özcan 0000-0001-6436-6368

Publication Date September 1, 2022
Published in Issue Year 2022

Cite

APA Yıldırım, M., & Özcan, M. (2022). Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm. Gazi University Journal of Science, 35(3), 957-967. https://doi.org/10.35378/gujs.942680
AMA Yıldırım M, Özcan M. Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm. Gazi University Journal of Science. September 2022;35(3):957-967. doi:10.35378/gujs.942680
Chicago Yıldırım, Mehmet, and Mustafa Özcan. “Unit Commitment Problem With Emission Cost Constraints by Using Genetic Algorithm”. Gazi University Journal of Science 35, no. 3 (September 2022): 957-67. https://doi.org/10.35378/gujs.942680.
EndNote Yıldırım M, Özcan M (September 1, 2022) Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm. Gazi University Journal of Science 35 3 957–967.
IEEE M. Yıldırım and M. Özcan, “Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm”, Gazi University Journal of Science, vol. 35, no. 3, pp. 957–967, 2022, doi: 10.35378/gujs.942680.
ISNAD Yıldırım, Mehmet - Özcan, Mustafa. “Unit Commitment Problem With Emission Cost Constraints by Using Genetic Algorithm”. Gazi University Journal of Science 35/3 (September 2022), 957-967. https://doi.org/10.35378/gujs.942680.
JAMA Yıldırım M, Özcan M. Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm. Gazi University Journal of Science. 2022;35:957–967.
MLA Yıldırım, Mehmet and Mustafa Özcan. “Unit Commitment Problem With Emission Cost Constraints by Using Genetic Algorithm”. Gazi University Journal of Science, vol. 35, no. 3, 2022, pp. 957-6, doi:10.35378/gujs.942680.
Vancouver Yıldırım M, Özcan M. Unit Commitment Problem with Emission Cost Constraints by Using Genetic Algorithm. Gazi University Journal of Science. 2022;35(3):957-6.