**11. Conclusion**

84 Modeling and Optimization of Renewable Energy Systems

Fig. 9. Sensitivity of Pareto optimum solutions to the specific fuel cost

The presented chapter demonstrates the basic of exergoeconomic modeling of any thermal power plant and application of the exergoeconomic concept to single and multi objective optimization of an Integrated Solar Combined Cycle System (ISCCS). The exergy-costing method is applied to a 400 MW Integrated Solar Combined Cycle System to estimate the unit costs of electricity produced from combined gas and steam turbines.

The application of single objective optimization process shows that exergy and exergoeconomic analysis improved significantly for optimum operation as follows:


Also, it is found that multi-criteria optimization approach, which is a general form of single objective optimization, enables us to consider various and ever competitive objectives for more improvement of any thermal power plant. An example of decision-making process for selection of the final optimal solution from the Pareto frontier in the multi objective optimization is presented. This final optimum solution requires a process of decisionmaking, which depends on the preferences and criteria of each decision-maker. Each decision maker may select different points as optimum solution which better suits with their desires. The final optimum solution for a typical ISCCS is determined and compared to the base case design and discussed. The analysis of the ISCCS shows that:

Exergoeconomic Analysis and Optimization of Solar Thermal Power Plants 87

Baghernejad, A. Yaghoubi, M. (2010). Exergy Analysis of an Integrated Solar Combined Cycle System. *Renewable Energy*, Vol.35, No.10, pp. 2157-2164, ISSN 0960-1481 Baghernejad, A. Yaghoubi, M. (2011a). Exergoeconomic Analysis and Optimization of an

*conversion and Management,* Vol.52, No.5, pp. 2193-2203, ISSN 0196-8904 Baghernejad, A. Yaghoubi, M. (2011b). Multi objective exergoeconomic optimization of an

Bejan, A. Tsatsaronis, G & Moran, M. (1996). Thermal design and optimization, John Wiley

Beghi, A. Cecchinato, L & Rampazo, M. (2011). A multi-phase genetic algorithm for the

Cammarata, G. Fichera, A & Marletta, L. (1998). Using genetic algorithms and the

Johansson, A. (2002). Entropy and the cost of complexity in industrial production. *Exergy an* 

Kearney, D. (1999). Parabolic-Trough Technology Roadmap A Pathway for Sustained

Lazzaretto, A. Tsatsaronis, G. (2006). SPECO: a systematic and general methodology for

Lazzaretto, A. Toffolo, A. (2004). On the thermoeconomic approach to the diagnosis of

Lozano, M. Valero, A. (1993). Theory of the exergetic cost. *Energy*, Vol.18, No.9, pp. 939–960. Moran, M. Sciubba, E. (1994). Exergy analysis: principles and practice. *Gas Turbines Power,*

Rao SS. (1996). Engineering optimization: theory and practice, John Wiley and Sons, New

Rezende, M. Costa, C. Costa, A. Maciel, M & Filho, R. (2008). Optimization of a large scale

Schwarzenbach, A. Wunsch, AK. (1989). Flexible power generation systems and their

Status Report on Solar Thermal Power Plants, Pilkington Solar International: 1996. Report

Schwefel HP. (1995). Evolution and optimum seeking, John Wiley and Sons, New York Singh, N. Kaushik, SC. (1994). Technology assessment and economic evaluation of solar

industrial reactor by genetic algorithms. *Chemical Engineering Science*. Vol.63. No.2,

*Journal of Energy Research,* Vol.6, No.7, pp. 601-615

*Energy Resource Technology*, Vol.120, No.3, pp. 241–246

Concentrated solar thermal power-now. (2005). www.greenpeace.org. Sep. 2005.

Vol.52, No.3, pp. 1650-1661, ISSN 0196-8904

*International Journal.* Vol.2, No.4, pp. 295–299.

Pareto V. (1896). Cours d'economie politique. Lausanne, Switzerland

planning, ABB Review No.6, pp. 19-26

thermal power generation. IIT, Delhi: CES

and Sons, New York

SunLab NREL.

Vol.116, No.2, pp. 285–290

1289

49.

York

pp. 330-341

ISBN 3-9804901-0-6.

Integrated Solar Combined Cycle System (ISCCS) Using Genetic Algorithm. *Energy* 

integrated solar combined cycle system using evolutionary algorithms. *International* 

efficient management of multichiller systems, *Energy Conversion and Management.* 

exergonomic approach to optimize district heating networks, *ASME: Journal of* 

Commercial Development and Deployment of Parabolic-Trough Technology.

calculating efficiencies and costs in thermal systems. *Energy*, Vol.31, No.8, pp. 1257–

energy system malfunctions. Indicators to diagnose malfunctions: Application of a new indicator for the location of causes*. Int. J. Thermodynamics*, Vol.7, No.2, pp. 41–

**13. References** 

