•Parameters extraction of five electrical models with Levenberg Marquardt algorithm.•Comparison of approach results with those reported in literature.•Parameters of five electrical models are ...estimated and their accuracy are compared.•Higher relative error between calculated and measured energy is about 14%.
In this study, a new theoretical approach offering a good prediction of the performance of photovoltaic modules/strings/arrays was developed. The approach implemented in the Matlab/Simulink environment, is based on Levenberg Marquardt (LM) algorithm and was used to estimate the parameters of five electrical models selected among the most used ones. First the problem of initial guess is considered. For parameters initial values, an analysis of six was performed and lead to the choice of the group which offers the best trade-off between accuracy and speed of calculation. To validate the effectiveness of the proposed approach, the five-parameter model (L5P) is used for a comparison with both a deterministic method and two heuristics methods. The results clearly show that, the accuracy achieved with LM method is comparable to the deterministic one, but higher than that of the heuristics methods. Furthermore, the five selected electrical models have been evaluated on four different PV modules technologies. The I–V characteristic curves obtained under Standard Test Conditions (STC) by each of them, are compared to the manufacturer data. It was shown that, when the LM algorithm is used, the fives electrical models predict the behavior of the photovoltaic silicon modules with close accuracy. The best trade-off is achieved with L5P model. This result is confirmed by the theoretical estimation of solar energy production performed for three real power plants by using the fives models. The maximum difference between calculated and measured energy is around 14%.
This paper proposes a new label called ECO2E/KEG for energy production units. So far, appliances and buildings labelling has been developed to support initiatives against global warming issues. ...Labels were then set up in order to help people in the choice of efficient electrical appliances or buildings from energy consumption point of view. From our knowledge, no labelling has been developed for energy production units yet. ECO2E/KEG is an alphanumeric index which gives information on the ecological performance of the energy production units, and on the relative cost of these clean units compared to conventional diesel generators. The alpha index is calculated by considering emissions avoided by the production units. A classification system in five groups, ranging from A (the most ecological system) to E (the least ecological system) allows green unit classification, according to their alpha index. The numerical index is obtained by comparing the LCOE of the energy produced by the green production units over their lifetime with the LCOE of the energy produced by an equivalent diesel generator over the same duration of the project. Five classes are also defined for this index ranging from 1 (the most economical system) to 5 (the least economical system).
•A review of existing label initiative for appliances and building is done.•A new labeling of green energy production unit is properly developed.•The labeling is applied to six existing green energy production unit.
Agrivoltaic systems, which consist of the combination of energy production by means of photovoltaic systems and agricultural production in the same area, have emerged as a promising solution to the ...constraints related to the reduction in cultivated areas due to solar panels used in agricultural production systems. They also enable optimization of land use and reduction in conflicts over land access, in order to meet the increasing demand for agricultural products and energy resulting from rapid population growth. However, the selected installation configurations, such as elevation, spacing, tilt, and choice of panel technology used, can have a negative impact on agricultural and/or energy production. Thus, this paper addresses the need for a review that provides a clear explanation of agrivoltaics, including the factors that impact agricultural and energy production in agrivoltaic systems, types of panel configurations and technologies to optimize these systems, and a synthesis of modelling studies which have already been conducted in this area. Several studies have been carried out in this field to find the appropriate mounting height and spacing of the solar panels that optimize crop yields, as this later can be reduced by the shade created with the solar panels on the plants. It was reported that yields have been reduced by 62% to 3% for more than 80% of the tested crops. To this end, an optimization model can be developed to determine the optimal elevation, spacing, and tilt angle of the solar panels. This model would take into account factors that influence crop growth and yield, as well as factors that affect the performance of the photovoltaic system, with the goal of maximizing both crop yield and energy production.
This paper presents a performance comparison study performed on four photovoltaic modules. Three silicon technologies are concerned: one monocrystalline module, two polycrystalline modules and one ...module of tandem structure (amorphous/microcrystalline) also known as micromorph module. The modules I–V data and meteorological data have been measured during one year using an outdoor monitoring test facility named “IV bench”. This set up is installed at Ouagadougou (Latitude 12.45° North, Longitude 1.56° West) in Sudano Sahelian climate. The actual maximum power, the average performance ratios, the series resistances and the maximum power temperature coefficient of tested modules are determined from the outdoor measurements and used for comparison study. The power of all the modules has been stabilized in outdoor conditions before the performance analysis. The results show that the micromorph module presents the best performance on the selected site, with an average performance ratio of 92%. The monocrystalline and polycrystalline modules from the same manufacturer, have both an average performance ratio of 84%. The second polycrystalline module from another manufacturer, strangely presents the lowest average performance ratio (80%) due to both its large series resistance and the high maximum power temperature coefficient in operating conditions.
•Performances of three PV technologies are compared under sudano sahelian climate.•Many parameters are measured on the modules and analysed for the technology comparison.•Micromorph tandem cells, modules perform consistently better than c-Si on the site.•Series resistances' effects must be investigated when analysing module performances.
•The model provides the best configuration to maximize crop yield and energy output.•The model gives the optimal height, spacing between tables, table size, and tilt.•The amount of solar irradiation ...available for crops under the panels are calculated.•The energy output and crop yield for each configuration of the system is evaluated.•The increase in crop yield is more sensitive to the expansion of panel row spacing.
The global population is experiencing rapid growth, leading to increased demand for energy and food resources, necessitating the expansion of cultivated land. The construction of photovoltaic power plants to meet energy needs may result in competition for land between the agriculture and energy sectors. To address this issue, agrivoltaics systems are perceived as a solution, allowing for the coexistence of agricultural and energy production in the same area. However, the shading caused by solar panels can potentially. Therefore, a model has been developed to determine the best configuration for maximising both crop yields and energy production from the photovoltaic field. The purpose of this paper is to develop a model that optimizes energy production and crop yield within an agrivoltaics system. The model integrates factors such as elevation, spacing, tilt, panel technology and size to enhance the radiation under the photovoltaic panels, as well as to increase crop yield and the efficiency of photovoltaic array. It is constructed based on the climatic condition and the relationship between the shaded area and the sunlight distribution below the photovoltaic panels. Furthermore, the model relies on the correlation between the configuration used and the energy power delivered by the photovoltaic array. A set of equations that link configuration, sunlight, crop yield, and photovoltaic panel power was developed, and the model was implemented in MATLAB, using genetic algorithm optimisation techniques. The initial step involves the determination of radiation values under the panels, followed by the identification of the best scenarios for subsequent simulations aimed at evaluating crop yield and power generation from the photovoltaic array. A case study was conducted in Kamboinsin village (12°27′ N, 1°33′ W), in Burkina Faso, focusing onusing corn cultivation to validate the model. The results show that the model effectively identifies the optimal configuration for maximizing both crop yield and photovoltaic field output. The simulation results reveals that the distribution of radiation under the panels is significantly influenced by factors such as panel elevation height, spacing between table, and spacing between rows of table. Notably, the yield is more sensitive to the spacing between rows of panels. When comparing the effects of the different panel sizes, it is evident that utilizing smaller tables leads to higher crop yields. However, this approach results in a decrease in energy production from the photovoltaic field. For instance, on 1 ha of land, a table consisting of a single 100 Wp panel generated 92.8 % of the crop yield achieved in full sun with a nominal power of 96.9 kWp, whereas a table comprising 2 panels of 260 Wp produced 80.1 % of the yield with a nominal power of 378.56 kWp.
