**12. Nomenclature**


#### **13. References**

86 Modeling and Optimization of Renewable Energy Systems

1. Optimization process leads to 3.2% increase in the exergetic efficiency and 3.82%

2. Optimization leads to the 2.73% reduction on the fuel exergy, 5.71% reduction in the total exergy destruction and also 3.46% and 7.32% reductions in the fuel cost rate and

3. 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.

AC air compressor *Pr* pressure ratio BFP boiler feed pump ri rate of inflation c cost per exergy unit, \$/kWh s specific entropy, kJ/kgK *C* cost rate, \$/h SCA solar collector assembly

CC combustion chamber SH superheater CEP condensate extraction pump SHE solar heat exchanger COLL collector ST steam turbine COND condenser T temperature, K CP construction period, year *W* power, MW

DEA dearator *Z* investment cost rate, \$/h

exergetic efficiency

maintenance factor

isentropic efficiency

relative irreversibility

scaling factor

standard deviation

*E* exergy rate, MW Greek symbols

HCE heat collection element Subscripts

HP high pressure 1,2,3…,38 state points HRSG heat recovery steam generator CH chemical exergy HTF heat transfer fluid D destruction I equipment investment, \$ e outlet

in interest rate i inlet/ith flow stream k plant lifetime, year k kth component LHV lower heating value L loss LP low pressure F fuel *m* mass flow rate, kg/sec P product OILP oil pump PH physical exergy P pressure, bar sys system *P* offspring tot total

decrease of the rate of product cost.

**12. Nomenclature** 

ECO economizer

EVA evaporator

GT gas turbine

h specific enthalpy, kJ/kg

H operation period, hour

<sup>ƒ</sup>exergoeconomic factor/ annuity

factor

cost rate relating to the exergy destruction, respectively.


**5** 

Karim Bourouni

*Tunisia* 

**Optimization of Renewable Energy Systems:** 

The application of renewable energies (RE) for driving desalination units (DES) is very promising in isolated areas (i.e. islands, villages in the desert, etc.), where the electricity production is very expensive, potable water resources are inexistent and the potential of renewable energies (solar and wind) is very important (Koroneos et al., 2007; Kalogirou, 2005). The application of renewable energies in the desalination industry does not face the same barriers as in the case of RES for electricity power production (expensive storage systems to compensate the stochastic characteristics of the renewable energies). In the case of RES/DES coupling, the energy is consumed directly for water production, the water can be stored, cheaply in large quantities and for long periods (Koroneos et al.,

The operational performances, the cost and the reliability of RES/DES units depend on the design and calibration of such systems. Optimal use of RES potential is necessary in order to reduce the cost of produced water. In this frame considering hybrid configurations (PV and

On the other hand, the design of such systems is complex because of uncertain renewable energy supplies, load demands and the non-linear characteristics of some components.

Despite the great effort done to improve the efficiency of RES/DES systems the desalinated water cost still relatively high compared to conventional desalination units and more effort

In this chapter we present different methods to optimize renewable energy systems driving desalination unit, with a particular interest to the RO driven by hybrid PV/Wind systems. For this last configuration we will present a new methodology based on Genetic

The objective function used in this optimization is the unit cost of desalinated water during the life cycle of the plant (20 years). The presented methodology consists in selecting from available components in the market, the optimal number and the type of each unit (PV panels, wind turbines, membranes, etc.) in such way that the water needs are satisfied and the production cost is minimized. The total water cost for the life cycle of the plant is equal

**1. Introduction** 

2007).

Algorithms.

Wind) is the best way to optimize the use of RE.

should be done to optimize this kind of systems.

to the sum of the capital and maintenance costs.

**The Case of Desalination** 

*Ecole Nationale d'Ingénieurs de Tunis,* 

