Multi-objective evolutionary algorithms for the design of grid-connected solar tracking systems

The decentralization of electrical power production is conducive to a more effective and harmonious use of energy resources. For this reason, photovoltaic grid-connected plants (PVGCPs) as well as other renewable energy sources have come into the spotlight in recent years since they improve the supply of electrical power to the grid. The optimization of PVGCP design has been previously addressed in terms of electrical losses with successful results. However, PVGCP performance can be further enhanced if other characteristics, such as power capacity, are taken into consideration. This paper focuses on the optimization of the design of photovoltaic plants with solar tracking. The research described had the following two objectives: (i) the maximization of power capacity; (ii) the minimization of electrical losses. This problem was solved with multi-objective evolutionary algorithms, which have proved to be powerful optimization techniques that are useful for a wide range of objectives. This paper focuses on the NSGA-II and SPEA2, two well-known multi-objective algorithms, and describes how they were used to optimize PVGCPs. The resulting sets of solutions provide the flexibility and adaptability needed to build a PVGCP. These algorithms were thus found to be an effective tool for enhancing PVGCP performance.