# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 1 Introduction  
Solar energy is one of most reliable, cleanest, and easy to harvest energy sources among all renewables. Solar energy can be converted into other forms of energy, mostly electricity and heat. Solar cells convert sunlight directly into electricity, while concentrating solar power systems use mirrors to concentrate the energy from the sun to heat a working fluid that drives traditional steam turbines connected to an electrical power generator. Over the last two decades, worldwide solar photovoltaic (PV) installations have exponentially grown and their cost has significantly reduced due to the improvements in technology and the economies of scale. Besides direct economic and environmental advantages, the use of solar energy produces other indirect environmental and social benefits, such as the stabilization of degraded land, the increasing of energy independence, new job opportunities, acceleration of remote rural areas electrification, and improved quality of life in developing countries [1]. The environmental issues related to the installation of solar power facilities have been comprehensively addressed in [2]: more than thirty environmental impacts falling in the fields of land use intensity, human health and wellbeing, plant and animal life, geo-hydrological resources, and climate change. In this context, the integration of solar energy into islanded micro-grids can play a key role both in decreasing the dependence on fossil fuels in energy intensive applications, for example in the desalination of plants [3], and to speed up access to electricity in developing countries [4]. The high penetration of renewables in the power grid presents several challenges, mainly related to the uncertainty in generation output due to weather fluctuations. In recent years, several Computational Intelligence (CI) techniques have been developed for renewable energies [5], with the aim of helping grid operators to better manage the electric balance between power demand and supply. In particular, the efficiency of a whole PV system depends on many factors at the same time: the efficiency of each single PV module [6; 7], mismatch losses in the PV generator [8; 9], losses in the wiring, switches, transformers and power converters, and the extraction efficiency of the Maximum Power Point Tracking (MPPT) algorithm. Moreover, the efficiency of the hardware components is neither easily or cheaply improved, but the improvement of the MPPT with new control algorithms allows for increasing power generation performance. In this context, CI techniques can be successfully employed to tackle various problems in system optimization and power extraction strategy [10]. In this light, the Particle Swarm Optimization (PSO) is a well-known evolutionary algorithm based on a model of group interaction between independent agents (particles) that uses social behavior (or _swarm knowledge_) in order to find a global maximum or minimum of a specific fitness function [11]. This computational technique adopts a biological-like approach, and takes its rationale from the emulation of social behaviors such as those related to bird flocking and fish schooling. The PSO algorithm explores the problem domain with moving particles represented by a set of coordinates in the N-dimensional solution space, which represents a specific problem solution corresponding to a particular value of the objective function. This work proposes a novel MPPT algorithm based on the PSO evolutionary approach and compares the results with traditional MPPT methods, previously tested by the authors [12], with the aim of increasing the overall conversion efficiency in particular conditions characterized by relevant changes and high dynamism with an affordable computational burden. This optimization of solar power extraction requires operating the photovoltaic generator at its maximum efficiency and can naturally benefit evolutionary computation.