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Abstract: This paper presents an intelligent sizing technique for sizing grid-connected photovoltaic (GCPV) system using evolutionary programming (EP). EP was used to select the optimal set of photovoltaic (PV) module and inverter for the system such that the technical or economic performance of the system could be optimized. The decision variables for the optimization process are the PV module and inverter which had been encoded as specific integers in the respective database. On the other hand, the objective function of the optimization task was set to be either to optimize the technical performance or the economic performance of the system. Before implementing the intelligent-based sizing algorithm, a conventional sizing model had been presented which later led to the development of an iterative-based sizing algorithm, known as ISA. As the ISA tested all available combinations of PV modules and inverters to be considered for the system, the overall sizing process became time consuming and tedious. Therefore, the proposed EP-based sizing algorithm, known as EPSA, was developed to accelerate the sizing process. During the development of EPSA, different EP models had been tested with a non-linear scaling factor being introduced to improve the performance of these models. Results showed that the EPSA had outperformed ISA in terms of producing lower computation time. Besides that, the incorporation of non-linear scaling factor had also improved the performance of all EP models under investigation. In addition, EPSA had also shown the best optimization performance when compared with other intelligent-based sizing algorithms using different types of Computational Intelligence. [Copyright &y& Elsevier]

This paper presents a multi-objective-evolutionary programming (MOEP) algorithm for sizing grid-connected photovoltaic (GCPV) system. Unlike previous studies which focused on the sizing of GCPV system based on a single objective optimization problem, the sizing of GCPV system in this study was initially converted into a three different bi-objective optimization problems. The objectives were formulated based on the aim to maximize the expected technical and economic performance indicators for the system design. Apart from that, in each bi-objective optimization problem, the proposed MOEP was used to select the optimal combinations of photovoltaic (PV) module and inverter such that the optimal values of the two performance indicators could be achieved in a single run. The performance of the proposed MOEP-based sizing algorithm was later compared with the performance of the weighted sum method (WSM)- based sizing algorithm. In addition, when illustrating the effectiveness of the proposed MOEP in approximating the Pareto front, the non-dominated solutions from MOEP were compared with a benchmark algorithm which produced all possible design solutions. The proposed MOEP-based algorithm was found to outperform WSM in providing faster computations and better approximation of the Pareto front. [ABSTRACT FROM AUTHOR]

This paper presents a new intelligent algorithm for sizing grid-connected photovoltaic (GCPV) systems. The sizing process involves the selection of PV module and inverter as well as the estimation of the technical and economic output performance of the system. Although many existing sizing software have been beneficial in assisting the GCPV system design, the application of these software might be limited due to the rigid algorithm embedded in the software. On the other hand, the manual calculation-based design approach could be another viable option but the design process could be extremely tedious especially when various design options need to be evaluated. In this study, the PV module and the inverter were selected from the respective databases whereas the sizing algorithm was developed evolutionary programming (EP). Besides selecting the PV module and inverter, the new algorithm, known as EP-based sizing algorithm (EPSA) was used to determine the techno-economic performance of the system. The EPSA was tested using the classical EP and meta-EP models before the best model was selected for EPSA. The proposed EPSA with meta-EP model was found to outperform a proposed conventional iterative sizing algorithm (ISA) in terms of computation time. [ABSTRACT FROM AUTHOR]