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PV-GO: A multiobjective and robust optimization approach for the grid metallization design of Si-based solar cells and modules

Abstract:

This paper proposes a new optimization approach for the metallization design of solar cells and modules. While previous works have only optimized a maximum of two parameters at a time (which might lead to the loss of the optimal solution), we developed a graphic user interface tool in MATLAB called Photovoltaic Grid Optimizer (PV-GO) which is able to optimize several parameters in parallel (fingers, busbars, silver pads, and ribbons) with the objectives to enhance the efficiency of the solar cell/module and to reduce the silver consumption. The fabrication cost is subsequently estimated. Two multiobjective optimization algorithms are used: Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-objective Particle Swarm Optimization (MOPSO) as well as a robust condition to ensure that the results are not severely affected by slight changes on the input parameters. The solar cell/module performance is estimated based on the two-diode model considering the influence of the optimization variables under standard test conditions (STC). To evaluate our methodology, we present the optimization design for industrial Cz-Si–based p-type Al-BSF monofacial and PERT bifacial, full-cell (15.6 × 15.6 cm2) and half-cell (15.6 × 7.8 cm2), solar cells and modules. Our methodology provides an optimal design suitable for different market situations that should be considered by the PV manufactures to enhance their electrical performance and reduce their silver consumption, and ultimately improve their profitability.