Annual energy losses due to partial shading in PV modules with cut wafer-based Si solar cells

To further improve the efficiency of the wafer-based silicon photovoltaic (PV) module, producers are introducing new module designs with cut-cells. Since smaller solar cells might be affected by partial shading even more and earlier than full-size cells, the energy performance simulations of partially shaded modules are crucial. A detailed shading analyses of partially shaded modules with different cut cell designs are presented not only on a single case scenario but on annual energy yield simulations using Spice, where a shading scenario over the whole module by the use of a new 3D shading horizon profile of selected shading objects is calculated. The annual simulations reveal that regardless the module design almost all cells in the module are confronted by reverse bias, which can deteriorate the module performance significantly. Simulation results with three different shading objects on five different module topologies at five locations showed that the best cut-cell module design depends strongly by the micro location and shading objectshowever, in general the string of solar cells connected in series should be aligned with the shading shape around noontime as much as possible. A comprehensive annual energy performance evaluation of partially shaded cut-cell modules revealedthat with a correct cell layout of cut-cells in a PV module, the shading losses can be reduced by 30e50% if comparing to the standard PV module design

Photovoltaics (PV) System Energy Forecast on the Basis of the Local Weather Forecast: Problems, Uncertainties and Solutions

When integrating a photovoltaic system into a smart zero-energy or energy-plus building, or just to lower the electricity bill by rising the share of the self-consumption in a private house, it is very important to have a photovoltaic power energy forecast for the next day(s). While the commercially available forecasting services might not meet the household prosumers interests due to the price or complexity we have developed a forecasting methodology that is based on the common weather forecast. Since the forecasted meteorological data does not include the solar irradiance information, but only the weather condition, the uncertainty of the results is relatively high. However, in the presented approach, irradiance is calculated from discrete weather conditions and with correlation of forecasted meteorological data, an RMS error of 65%, and a R2 correlation factor of 0.85 is feasible

Development of a Stochastic Hourly Solar Irradiation Model

We have developed a new solar irradiation model and implemented it in the SunIrradiance photovoltaic cell/module simulator. This model uses stochastic methods to generate the hourly distribution of solar irradiation on a horizontal or inclined surface from monthly irradiation values on the horizontal surface of a selected location and was verified with the measured irradiance data in Ljubljana, located in Central Europe. The new model shows better simulation results with regard to the share of the diffuse irradiation in the region than the other models. The simulation results show that the new solar irradiation model is excellent for photovoltaic system simulations of single junction PV technologies

Examination of photovoltaic silicon module degradation under high-voltage bias and damp heat by electroluminescence

The photovoltaic (PV) modules are in PV arrays normally connected in series and thus some of them are exposed to high system voltages since frames of the PV modules are grounded. To predict the long-term PV system energy output and PV module lifetime, it is very important to understand and take into account the degradation process of PV modules under high-voltage stress. Accelerated tests under damp heat (over 1300 h of DH85/60; RH = 85%, T = 60 °C) of in-house developed monocrystalline silicon PV modules with p-type solar cells were preformed while connected to a positive or negative voltage bias of 1000 V. The negative biased modules exhibited just a little degradation, while the positive biased modules degraded rapidly. We identified three degradation mechanisms: cell degradation, silver corrosion, and EVA evaporation. The degradation mechanisms contribute to almost 15% of the performance loss of the 1000 V positive biased modules after more than 1300 h of DH85/60 testing, while the power degradation of the negative biased modules remains below 3%.</jats:p

Advanced PV performance modelling based on different levels of irradiance data accuracy

In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class measurement devices for irradiance and other weather conditions. For this reason, the use of reanalysis-based or satellite-based data is currently of significant interest in the PV community and combining the data with decomposition and transposition irradiance models, the actual Plane-of-Array operating conditions can be determined. In this paper, we are proposing an efficient and accurate approach for PV output energy modelling by combining a new data filtering procedure and fast machine learning algorithm Light Gradient Boosting Machine (LightGBM). The applicability of the procedure is presented on three levels of irradiance data accuracy (low, medium, and high) depending on the source or modelling used. A new filtering algorithm is proposed to exclude erroneous data due to system failures or unreal weather conditions (i.e., shading, partial snow coverage, reflections, soiling deposition, etc.). The cleaned data is then used to train three empirical models and three machine learning approaches, where we emphasize the advantages of the LightGBM. The experiments are carried out on a 17 kW roof-top PV system installed in Ljubljana, Slovenia, in a temperate climate zone