Design of a methodology to identify optimum PV configurations for small-scale modular solutions for tropical conditions

Abstract: The development of small-scale solar photovoltaic (PV) power plants is accelerating in most isolated regions, such as the Caribbean. Indeed, they offer a beneficial, ecological and modular alternative to reduce the important fossil fuel dependency of these countries. However, since modules are facing a harsh environment in these areas, the PV project configuration cannot follow the same rules as in a “classical” PV project: the project specifications and design techniques are different as more constraints must be taken into consideration. Therefore, designing PV configurations for such projects requires a specific methodology adapted to the tropical and cyclonic conditions. This thesis work aims to design such a methodology for a 100 kWc solar shed: using the available literature on PV modules’ behavior under tropical conditions, a set of adapted specifications is defined, and several technical components are selected followingly. A cost analysis is undertaken and assesses the total CAPEX (∼220,000 €) and the OPEX (5,650 €/year) of the project. In order to select the module that fits best the customer’s requirements, an economical and ecological grading system is used, this choice is followed by the PV field design. The project performance is assessed using PVSyst, and the results are used to compute the different economic indicators using a MATLAB program. This methodology is tested on a real case study, the main outputs are total yield of 166.0 MWh, capacity factor of 0.19, project LCOE of 147.93€/MWh, IRR of 5.35% and a potential annual rent to the customer equal to 2,900€/year. Several sensitivity analyses are conducted to judge and explore the potential enhancements to this methodology, it appears that the ecological grading index definition should be modified, and that orientation and inverter choice optimization could be added to the methodology as long as they don’t add too much complexity.