Abstract
Energy generation industry is growing rapidly to meet the increasing energy demand. One of the most usable raw materials for energy production is natural gas. Combined cycle gas turbine power plants have a big portion among the power plants using natural gas for energy production. Therefore, in this chapter combine cycle gas turbine power plants have been analyzed with respect to the effects of market price changes to the amount of natural gas which a combined cycle gas turbine power plant would need for production throughout a year. The proposed model takes the costs of natural gas, ignition and maintenance of the turbine into account. It utilizes an evolutionary algorithm to minimize the power plant’s risk and helps managers to find the optimal amount of natural gas consumption to achieve maximum profit. Also the proposed model is applied on a medium scale natural gas power plant for validation and the results are given.
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References
Aguiar, A. B. M., Pinto, J. O. P., Andrea, C. Q., & Nogueira, L. (2007). Modeling and simulation of natural gas microturbine application for residential complex aiming technical and economical viability analysis. In Electrical Power Conference (EPC 2007) (pp. 376–381). Canada: IEEE.
Azmy, A. M., & Mohamed, M. R. (2005). Decision tree-based approach for online management of fuel cells supplying residential loads. In Power Tech (pp. 1–7). Russia: IEEE.
Back, T. (2000) Introduction to evolutionary algorithms, In: T. Baeck, D. B Fogel, Z. Michalewicz (Eds), Evolutionary computation 1: Basic algorithms and operators (pp. 59–63). London: Taylor and Francis Group.
Barukcic, M., Hederic, Z., & Miklosevic, K. (2014). Multi objective optimization of energy production of distributed generation in distribution feeder. In Energy Conference (ENERGYCON) (pp. 1325–1333), IEEE International.
Bhavani, N. P. G., Sujatha, K., Kumaresan, M., Pongamal, R. S., & Reddy, S. B. (2014). Soft sensor for temperature measurement in gas turbine power plant. International Journal of Applied Engineering Research, 9(23), 21305–21316.
Box, G. E. P. (1957). Evolutionary operation: A method of increasing industrial productivity. Applied Statistics, 6, 81–101.
Cetin, B., Erdem, H. H., & Sevilgen, S. H. (2014) Energy based thermoeconomic analysis of a combined cycle system with steam extraction (cogeneration system). Advances in Mechanical Engineering, 794561.
Coello, C. A. C. (2005). An introduction to evolutionary algorithms and their applications. In F. F. Ramos, V. L. Rosillo, & H. Unger (Eds.), Advanced distributed systems (pp. 425–442). Berlin: Springer.
Demirbas, A. (2009). Biofuels: Securing the planet’s future energy needs. London: Springer.
Engelbrecht, A. P. (2007) Computational intelligence—An introduction (2nd ed.). New York: Wiley.
Fazlollahi, S., & Maréchal, F. (2013). Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP). Applied Thermal Engineering, 50(2), 1504–1513.
Ferreira, A. C., Vaz, A. I. F., Teixeira, S. F., & Teixeira, J. C. (2015) Thermal-economic design of a micro-gas turbine CHP system using a multi-objective optimization approach. In PANACM 2015—1st Pan-American Congress on Computational Mechanics, in Conjunction with the 11th Argentine Congress on Computational Mechanics (MECOM 2015) (pp. 1203–1214).
Fleming, P. J., & Purshouse, R. C. (2002). Evolutionary algorithms in control systems engineering: A survey. Control Engineering Practice, 10(11), 1223–1241.
Fraser, A. S. (1957). Simulation of genetic systems by automatic digital computers. Australian Journal of Biological Science, 10, 484–491.
Gassner, M., & Maréchal, F. (2012). Thermo-economic optimisation of the polygeneration of synthetic natural gas (SNG), power and heat from lignocellulosic biomass by gasification and methanation. Energy & Environmental Science, 5(2), 5768–5789.
Kantardzic, M., & Gant, J. (2007) Mining sequences in distributed sensors data for energy production. In Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2007) (pp. 484–489).
Masters, G. M. (2013). Renewable and efficient electric power systems. New York: Wiley.
Rovira, A., Sánchez, C., Munoz, M., Valdés, M., & Durán, M. D. (2011). Thermoeconomic optimisation of heat recovery steam generators of combined cycle gas turbine power plants considering off-design operation. Energy Conversion and Management, 52(4), 1840–1849.
San Jose, R., Pérez, J. L., Morant, J. L., Prieto, F., & González, R. M. (2008) The use of third generation air quality modeling systems for web operational realtime forecasting decision support systems: Spain case. In Proceedings of iEMSs 4th Biennial Meeting—International Congress on Environmental Modelling and Software: Integrating Sciences and Information Technology for Environmental Assessment and Decision Making (iEMSs 2008) (Vol. 2, pp. 1154–1161).
Şevik, S. (2015). An analysis of the current and future use of natural gas-fired power plants in meeting electricity energy needs: The case of Turkey. Renewable and Sustainable Energy Reviews, 52, 572–586.
Tsoutsanis, E., Meskin, N., Benammar, M., & Khorasani, K. (2015). Transient gas turbine performance diagnostics through nonlinear adaptation of compressor and turbine maps. Journal of Engineering for Gas Turbines and Power, 137(9), 091201.
Weissenbacher, M. (2009), Sources of power: How energy forges human history. In ABC-CLIO.
World Bank Group (Ed.). (2015). World development indicators 2012. New York: World Bank Publications.
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Tinç, H.K., Sarı, İ.U. (2017). Planning of Efficient Natural Gas Consumption in a Combined Cycle Gas Turbine Power Plant Using Evolutionary Algorithms. In: Kahraman, C., Sari, İ. (eds) Intelligence Systems in Environmental Management: Theory and Applications. Intelligent Systems Reference Library, vol 113. Springer, Cham. https://doi.org/10.1007/978-3-319-42993-9_9
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DOI: https://doi.org/10.1007/978-3-319-42993-9_9
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