No abstract

No abstract

In many applications, the current versus voltage curve of a photovoltaic cell, module, string, or field is acquired. A high number of samples are usually acquired, but the curve contains the main information in the open-and short-circuit points, as well as where it has a strong change in the slope. In this paper, these parts are called "the fingerprint" of the photovoltaic generator. The fingerprint allows us to recognize the working conditions of the photovoltaic generator, e.g., if it is affected by a partial shadowing or not. Saving the fingerprint and discarding the other points of the original curve allows us to minimize the memory needs for storing the curve without losing the main information content. In this paper, a numerical technique for selecting, from among the samples of the acquired current versus voltage curve of any photovoltaic generator, the ones to be included in the fingerprint is proposed. The processing steps and the memory needed to achieve the result are minimized in order to allow an implementation of the algorithm also in a low-cost processor for on-field real-time applications. The technique is validated through curves generated by using analytical models as well as by means of some curves acquired experimentally in outdoor conditions.

Electrification and digitization are two significant trends in the energy sector. Photovoltatronics unites these trends by combining solar electricity generation and information communication in PV-based intelligent energy agents.

Non-uniform conditions within a PV module lead to significant energy losses in conventional topologies. Some of the energy lost due to partial shading can be recovered by installing reconfigurable modules. In order to explore the potential benefits of reconfigurable topologies in installations where non-uniform conditions are dominant, three shading scenarios are simulated throughout the year, both for reconfigurable and conventional modules. This allows the evaluation of the performance of each topology and estimation of the long-term gains. Using a cost function, which includes the additional investment cost, the energy gain can be translated to financial terms. Through this, locations where reconfigurable topologies can be beneficial, both in energy and financial terms, can be identified. The methodology is applied to the case of reconfigurable PV modules with a snake-like configuration. Simulation results show that these PV modules allow for higher energy production compared to conventional modules under all the simulated shading scenarios. However, they remain economically profitable over 20 years only in locations where partial shading is expected to occur daily, either throughout the full year or at least in the period of the year where energy generation is high.