Evaluating the Economic Feasibility for utilizing PV Power Optimizers in Large-scale PV Plants for The Cases of Soiling, Mismatching, and Degradation

Abstract: The solar PV modules are influenced by a variety of loss mechanisms by which the energy yield is affected. A PV system is the sum of individual PV modules which should ideally operate similarly, however, inhomogeneous soiling, mismatching, and degradation, which are the main focus in this study, lead to dissimilarities in PV modules operating behavior and thus, lead to losses which will be assessed intensively in terms of energy yield. The dissimilarities in PV modules are referred to the ambient conditions or the PV modules characteristics which result in different modules’ maximum power point (MPP) and thus, different currents generated by each PV modules which cause the mismatching. However, the weakest PV module current governs the string current, and the weakest string voltage governs the voltage. Power optimizers are electronic devices connected to the PV modules which adjust the voltages of the PV modules in order to obtain the same current as the weakest module and thus, extract the modules’ MPP. Hence, the overall performance of the PV plant is enhanced. On the other hand, the power optimizers add additional cost to the plant’s investment cost and thus, the extra energy yield achieved by utilizing the power optimizers must be sufficient to compensate the additional cost of the power optimizers. This is assessed by designing three systems, a reference system with SMA inverters, a system utilizes Tigo power optimizers and SMA inverters, and a system utilizes SolarEdge power optimizers and inverters. The study considers four different locations which are Borlänge, Madrid, Abu Dhabi, and New Delhi. An Excel model is created and validated to emulate the inhomogeneous soiling and to evaluate the economic feasibility of the power optimiz ers. The model’s inputs are obtained from PVsyst and the precipitation data is obtained from Meteoblue and SMHI database. The economic model is based on the relation between Levelized Cost of Electricity (LCOE) which will be used to derive the discount rate. Graphs representing the discounted payback period as a function of the feed-in tariff for different discount rates is created in order to obtain the discounted payback period. The amount of extra energy yielded by the Tigo and the SolarEdge systems is dependent on the soiling accumulated on the PV modules. Relative to the reference system, 6.5 % annual energy gain by the systems utilizing the power optimizers in soiling conditions, up to 2.1 % in the degradation conditions, and up to 9.7 % annual energy gain at 10 % mismatching rate. The extra energy yield is dependent on the location, however, the Tigo and the SolarEdge systems have yielded more energy than the reference system in all cases except one case when the mismatch losses is set to zero. The precipitation pattern is very influential, and a scare precipitation leads to a reduction in the energy yield, in this case, the Tigo and the SolarEdge systems overall performance is enhanced and the extra energy gain becomes greater. The Tigo system yield slightly more energy than the SolarEdge system in most cases, however, during the plant’s lifetime, the SolarEdge system could become more efficient than the Tigo system which is referred to the system’s sizing ratio. The degradation of the system or the soiling accumulation decreases the irradiation and thus, a slightly oversized PV array become suitable and deliver an optimal power to the inverters. The SolarEdge system is feasible in all scenarios in terms of LCOE and discounted payback period, although its slightly lower performance relative to the Tigo system, this is referred to its low initial cost in comparison to the other systems. The Tigo system is mostly infeasible although it yields more energy than the reference and the SolarEdge systems, this is referred iii to its relatively high initial cost. However, feed- in tariffs higher than 20 € cent / kWh make all systems payback within less than 10 years. The results have overall uncertainty within ± 6.5 % including PVsyst, Excel model, and the precipitation uncertainties. The uncertainty in the degradation and the mismatching calculations is limited to PVsyst uncertainty which is ± 5 %. The uncertainties in LCOE in the location of New Delhi, since it is the worst-case scenario, are 5.1 % and 4 % for the reference and the systems utilizing power optimizers, respectively. Consequently, accommodating the uncertainties to the benefits gained by utilizing power optimizers indicates that the energy gain would oscillate in the range of 6 % - 6.9 % for the soiling calculations, 2 % - 2.2 % for the degradation simulations, and 9.2 % - 10.2 % for the mismatching simulations at 10 % mismatchrate.