Modeling and multi-objective optimal sizing of standalone photovoltaic system based on evolutionary algorithms

The standalone photovoltaic (SAPV) system is one of the most widely used applications of the PV system. However, the main drawbacks of this system are the high capital investment and low energy efficiency mainly due to the simple PV model and the ineffective sizing method of SAPV system. Therefore, precisely modeling method to determine the unknown parameters of the PV module is essential to give a realistic evaluation for the extracted energy of the PV array. Moreover, optimization of the standalone PV system is necessary to maximum the reliability and minimize the total cost of the system in both urban and rural areas. The research presented in this thesis is divided into two phases, namely, modeling of the PV module, and optimal sizing of the entire system to obtain reliable and cost- effectiveness SAPV system. Due to the effective attraction-repulsion mechanism of electromagnetic-like (EM) algorithm and reliable exploration and exploitation phases of differential evolution (DE), these two methods were used to determine parameters of the single diode PV model and finding optimal sizing of the SAPV system. Firstly, an improved EM (IEM) algorithm is presented to estimate the five parameters of the single PV-module system. The IEM algorithm uses the attraction-repulsion mechanism to change the positions of solutions towards the optimality. The key to improvement is performed by adding a nonlinear equation to adjust the length of the particle. Moreover, the total force formula is simplified to speed up the exploration for an optimal solution. Six statistical tools are used to show the superiority of the proposed PV model as compared to other models proposed in the literature. Secondly, the modeling method of the proposed PV module is validated by experimental data. In the sizing of the SAPV system, the mutation adaptive DE (MADE) algorithm based multi-objective functions minimizes three constraint objective functions. A new mutation vector inspired by the two-opposite path (2-Opt) algorithm with adaptive mutation scalar (F ) and crossover rate (CR) control parameters were employed to enhance the exploration and exploitation phases of the proposed algorithm. The objective functions are loss of load probability (LLP) and life cycle cost (LCC) and levelized cost of energy (LCE). In the current sizing optimization problem, the three individual objectives are normalized, weighted, and then aggregated by a single function which is minimized to select the optimal configuration of the SAPV system. Moreover, the performance of the SAPV system is carried out based on three types of storage batteries which are lead-acid battery, crown battery, and lithium-ion battery using hourly meteorological data for one year. Performance results show that the MADE algorithm based on lead-acid battery has a high level of LLP and minimum cost among other types of storage batteries. The LLP value of lead-acid is 0.0019 which describes the availability of the proposed SAPV system. Moreover, the LCC and LCE values are 54895.68 USD, and 1.5803 USD, respectively. Finally, the proposed sizing method is compared with a numerical sizing method to show the accuracy and efficiency of the proposed method. The results of the comparison indicated that the MADE method has an excellent level of accuracy and outperforms the iterative method in terms of CPU-execution time.

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