## 6 Grid Connected PV System Optimization Technique
### Genetic Algorithm (GA)  
GA is an artificial intelligence technique (AI), which was inspired from evolution and inheritance trait in living organism. The algorithm imitates population's evolution, based on survival-of-the-fittest strategy. GA has three operations, which is selection, crossover and mutation. GA method has ability to derive global optimum solution with relative computational simplicity, including complicated problems with non-linear function or constraint. However, GA may suffer excessive complexity if the problem is too large [1]. In 2009 [22], the authors developed an optimum sizing method using genetic algorithm (GA) among list of commercial system devices (PV modules and inverters) in Microsoft Visual C++ software. The sizing process was started by selection of PV module and inverter model, available land area, climatic parameters, cost and economy parameters. The best number of PV modules, inverters, tilt angle, the best arrangement of PV modules among inverters, and optimum arrangement of PV modules within site area was elected based on maximum net economic profit as GA objective function. In this study, total net profit was maximised using NPC analysis, by considering total capital cost of PV modules, inverters, cost of land area, mounting structure cost, cost of installation, and maintenance cost. However, the researchers did not include inverter to PV array sizing ratio as one of optimization criteria. In addition, several power losses were not taken into account, such as temperature, dirt and cables. Moreover, the PV modules arrangement calculation is not suitable if the land area is not face to south. In 2011 [25], a new sizing method was proposed to determine optimal PV modules and inverters by using GA. In this study, authors developed iterative sizing procedure as a benchmark to choose the best optimization methods between GA and evolutionary studies (ES). The proposed GCPV system capacity capable to fulfil energy requirement as specified by customer in kWh using preselect PV module model and inverter model. Peak Sun Hour at tilted panel angle, temperature reduction factor, manufacture's tolerance reduction factor, cable reduction factor, and inverter reduction factor was predefined was considered in sizing calculation. From possible PV modules arrangement among inverters, the highest value of inverter to array sizing factor and minimum excess factor was chosen as optimal design solution. From the methods comparison, GA method has lower percentage error compared to ES method. In 2011, authors in [110] proposed optimization methods for large PV plant design using Greece's time series of one minute average solar radiation data. The researchers optimize the proposed system by maximizing energy production. From simulation, optimum PV module distribution, optimum PV rows, distance between rows and tilt angle was determined. Then, LCOE was analyzed for the optimal solution. The proposed method can only be used to design PV generation plant at fixed tilted panel mounted on South faced ground. PV distribution system among inverters and PV arrangement layout at predefined space area was done similar with methods in [22; 111; 112], except the inverter used in design are Central Inverter, Multi-string Inverter and Mini-Central Inverter. In 2012, the same authors from [110] proposed a new optimization method for large PV plant in [113]. By using GA simulation, optimum PV module distribution, optimum PV rows, distance between rows and tilt angle was determined. However, the new technique was improved by including LCOE analysis within optimization process. The simulation result was compared to cases with non-optimized system, optimized for minimum cost only and optimized for maximum cost only. An inclusive GA method presented in 2014, in [3] where the authors use all technical, economical and environmental criteria in their analysis.