Applications of genetic algorithms

Table 5 summarizes various applications of genetic algorithms for solar energy systems. Larbes et al. (2009) investigated the use of intelligent control techniques for maximum power point tracking in order to improve the efficiency of PV systems, under different temperature and irradiance conditions. Initially, the design and simulation of a fuzzy logic – based maximum power point tracking controller was proposed. Compared to the perturbation and observation controller, the proposed fuzzy logic controller has improved the transitional state and reduced the fluctuations in the steady state. To improve the design and further improve the performances of the proposed fuzzy logic-based maximum power point tracking controller, genetic algorithms were then used to obtain the best subsets of the membership functions as they are very fastidious to be achieved by the designer. The obtained optimized fuzzy logic maximum power point tracking controller was then simulated under different temperature and irradiance conditions. Compared to the fuzzy logic controller, this optimized controller showed much better performance and robustness. It has not only improved the response time in the transitional state but has also reduced considerably the fluctuations in the steady state.




Fig. 11. The proposed ANFIS-based prediction for monthly clearness index proposed by Mellit et al. (2008)






Larbes et al. Zagrouba et al.



Photovoltaic solar energy systems

§en et al.


Determination of Angstrom equation coefficients

Loomans and Visser Kalogirou



Solar hot water systems

Koutroulis et al. Yang et al.



Hybrid solar-wind system

Bala and Siddique Dufo-Lopez and Bernal-Agustin



PV-diesel hybrid system

Lin and Phillips


Solar cell



Flat plate solar air heater

Zagrouba et al. (2010) proposed to perform a numerical technique based on genetic algorithms (GAs) to identify the electrical parameters of photovoltaic (PV) solar cells and modules. These parameters were used to determine the corresponding maximum power point from the illuminated current-voltage (I-V) characteristic. The one diode type approach is used to model the AM1.5 I-V characteristic of the solar cell. To extract electrical parameters, the approach is formulated as a non convex optimization problem. The GAs approach was used as a numerical technique in order to overcome problems involved in the local minima in the case of non convex optimization criteria. Compared to other methods, they found that the GAs is a very efficient technique to estimate the electrical parameters of PV solar cells and modules. The electrical parameters resulting from the use of the GA-based fitting procedure, with those given by the Pasan cell tester software is shown in Table 6.

Electrical parameters

Pasan software

Genetic algorithms

Is (A)

Not performed

1.2170 x 10-2

Iph (A)



Rs (0)



Rsh (0)




Not performed


Table 6. Comparison between the electrical parameters of the solar cell determined using GAs and those given by the Pasan software (Zagrouba et al., 2010)

§en et al. (2001) used a genetic algorithm for the determination of Angstrom equation coefficients. Good correlation is obtained in all the cases, showing the validity of the Angstrom equation for Turkish locations. The authors have presented a new way of estimating the Angstrom equation parameters using GAs.

Loomans and Visser (2002) used a genetic algorithm for the optimization of large solar hot water systems. The genetic algorithm tool calculates the yield and the costs of solar hot water systems based on technical and financial data of the system components. The genetic algorithm allows for optimization of separate variables such as the collector type, the number of collectors, the heat storage mass and the collector heat exchanger area. The applicability of the genetic algorithm was tested for the optimization of large solar hot water systems. Among others, the sensitivity of the optimum system design to the tap water draw­off and the draw-off pattern has been determined using the optimization algorithm. As the genetic algorithm is a discrete optimization tool and is implemented in the design tool through the use of databases, the number of variables in principle is free of choice.

Kalogirou (2004) used artificial intelligence methods like artificial neural-networks and genetic algorithms, to optimize a solar-energy system in order to maximize its economic benefits. The system is modelled using a TRNSYS computer program and the climatic conditions of Cyprus, included in a typical meteorological year (TMY) file. An artificial neural-network is trained using the results of a small number of TRNSYS simulations, to learn the correlation of collector area and storage-tank size on the auxiliary energy required by the system from which the life-cycle savings can be estimated. Subsequently, a genetic algorithm is employed to estimate the optimum size of these two parameters, for maximizing life-cycle savings; thus the design time is reduced substantially. As an example, the optimization of industrial process heat-system employing flat-plate collectors is presented. The results are shown in Table 7, where the actual results of the genetic algorithm program are presented together with the results of the traditional method. The optimum solutions obtained from the present methodology give increased life-cycle savings of 4.9 and 3.1% when subsidized and non-subsidized fuel prices are used respectively, as compared to solutions obtained by the traditional trial-and-error method.

Fuel price




obtained from GA

Practical selection to that of GA (1)




Percentage difference between (1) and (2)

29.6 €/L (Subsidized)

Area (m2) Volume (m3) LCS (€)











48.4 €/L (non-subsidized)

Area (m2) Volume (m3) LCS (€)











Table 7. Results of the solar-system optimization (Kalogirou, 2004)

Koutroulis et al. (2006) developed a methodology for the optimal sizing of stand-alone photovoltaic (PV)/wind-generator (WG) systems using genetic algorithms. The cost (objective) function minimization was implemented using genetic algorithms, which, compared to conventional optimization methods such as dynamic programming and gradient techniques, have the ability to attain the global optimum with relative computational simplicity. The proposed method has been applied for the design of a power generation system which supplies electricity to a residential household. The simulation results verify that hybrid PV/WG systems feature lower system cost compared to the cases where either exclusively WG or exclusively PV sources are used.

An optimal sizing method used to optimize the configurations of a hybrid solar-wind system employing battery banks is proposed by Yang et al. (2008). Based on a genetic algorithm, which has the ability to attain the global optimum with relative computational simplicity, an optimal sizing method was developed to calculate the optimum system configuration that can achieve the customers required loss of power supply probability (LPSP) with a minimum annualized cost of system (ACS). The decision variables included in the optimization process are the PV module number, wind turbine number, battery number, PV module slope angle and wind turbine installation height. The proposed method has been applied to the analysis of a hybrid system which supplies power to a telecommunication relay station, and good optimization performance has been found. Furthermore, the relationships between system power reliability and system configurations were also given. Although a solely solar or a wind turbine solution can also achieve the same desired LPSP, it represents a higher cost. The relationships between system power reliability and system configurations have been studied, and the hybrid system with 3-5 days’ battery storage is found to be suitable for the desired LPSP of 1% and 2% for the studied case.

Bala and Siddique (2009) carried out the optimal sizing of PV array, storage battery capacity, inverter capacity, backup diesel generator set capacity and operational strategy of a solar – diesel mini-grid of an isolated island-Sandwip in Bangladesh using genetic algorithms. This study reveals that the major share of the costs is for solar panels and batteries. Technological development in solar photovoltaic technology and development in batteries production technology make rural electrification in isolated islands more promising and demanding. Dufo-Lopez and Bernal-Agustin (2005) developed the HOGA (hybrid optimization by genetic algorithms), which is a program that uses a genetic algorithm (GA) to design a PV – diesel system (sizing, operation and control of a PV-diesel system). The program has been developed in C++. A PV-diesel system optimized by HOGA is compared with a stand-alone PV-only system that has been dimensioned using a classical design method based on the available energy under worst-case conditions. In both cases, the demand and solar irradiation are the same. The computational results show the economical advantages of the PV-hybrid system. HOGA is also compared with a commercial program for optimization of hybrid systems.

Lin and Phillips (2008) used a genetic algorithm to optimize the multi-level rectangular and arbitrary gratings. Solar cells with optimized multi-level rectangular gratings exhibit a 23% improvement over planar cells and 3.8% improvement over the optimal cell with periodic gratings. Solar cells with optimized arbitrarily shaped gratings exhibit a 29% improvement over planar cells and 9.0% improvement over the optimal cell with periodic gratings. The enhanced solar cell efficiencies for multi-level rectangular and arbitrary gratings are attributed to improved optical coupling and light trapping across the solar spectrum.

Varun (2010) used GAs for estimating the optimal thermal performance of a flat plate solar air heater having various system and operating parameters. The present work facilitates the domain of optimized values for different parameters which are decisive for ultimately finding the best performance of such a system. The basic values like number of glass covers, irradiance and Reynolds number are the key inputs on the basis of which the entire set of optimized values of parameters like wind velocity, panel tilt angle, emissivity of plate and ambient temperature are estimated by the proposed algorithm and finally the efficiency is calculated. Different optimized parameters for Reynold numbers ranging from 2000 to 20000 have been evaluated.