• Energy Research

Abstract As cost reductions have made photovoltaics (PV) a favorable choice also in colder climates, the number of PV plants in regions with snowfalls is increasing rapidly. Snow coverage on the PV modules will lead to significant power losses, which must be estimated and accounted for in order to achieve accurate energy yield assessment and production forecasts. Additionally, detection and separation of snow loss from other system losses is necessary to establish robust operation and maintenance (O&M) routines and performance evaluations. Snow loss models have been suggested in the literature, but developing general models is challenging, and validation of the models are lacking. Characterization and detection of snow events in PV data has not been widely discussed. In this paper, we identify the signatures in PV data caused by different types of snow cover, evaluate and improve snow loss modeling, and develop snow detection. The analysis is based on five years of data from a commercial PV system in Norway. In an evaluation of four snow loss models, the Marion model yields the best results. We find that system design and snow depth influence the natural snow clearing, and by expanding the Marion model to take this into account, the error in the modeled absolute loss for the tested system is reduced from 23% to 3%. Based on the improved modeling and the identified data signatures we detect 97% of the snow losses in the dataset. Endogenous snow detection constitutes a cost-effective improvement to current monitoring systems.

When participating in the Scandinavian wholesale energy markets with intermittent PV power, imbalances between market-committed and actual energy generation need to be economically accounted for in the imbalance settlement. The market-committed energy is based on forecasts issued at specific timestamps defined by the market. The economic value of such forecasts can be quantified for the past, present and future energy market regulations by comparing the achieved income using perfect forecasts to the income with imperfect forecasts. Differences between these regulations include changing from 60 min to 15 min market intervals, and from a dual-price to a single-price imbalance settlement structure. PV power production data were modelled from meteorological measurements carried out at 11 locations in each of the Nord Pool bidding areas. Forecasts generated from a smart persistence model and a more sophisticated numerical weather prediction (NWP) model were compared for these locations, and the value of these forecasts was estimated from historical market data for each of the 11 bidding areas. The results show that the estimated income, and income losses, are dependent on the electricity mix of the bidding areas. In addition, the transition from the dual-price to the single-price imbalance settlement structure reduces the value of providing accurate forecasts. Moreover, the added incentive to provide accurate forecasts when settling the markets and imbalances on 15 min instead of 60 min intervals is negligible with the single-price imbalance settlement. Finally, the results show that intraday trading is generally beneficial for PV power plants in Scandinavia.

Despite being one of the major issues that photovoltaic systems face worldwide, estimating the energy and economic magnitude of soiling of solar collectors still represents a challenge. This work presents a first European assessment of the soiling loss and of the cost-effectiveness of soiling mitigation. New soiling maps are generated through the interpolation of reanalysis data, calibrated against ground-measured losses from sensors installed across the continent. The results show that Europe experiences an average annual soiling loss of 0.9 % if rain is considered a perfect cleaning agent. However, if a cleaning effectivity by rain of 10 % is assumed, this annual loss increases up to 5.3 %. In some southern locations, soiling losses are markedly seasonal, while these are more consistent in central Europe. These losses can have repercussions on the economics of photovoltaics, increasing the levelized cost of electricity up to 4 % or 15 % depending on the cleaning effectivity of rain. The losses are also found to vary significantly in some sites from year to year, highlighting the need for continuous monitoring. The study concludes that implementing adequate soiling mitigation measures is strongly recommended in most of the regions due to the high electricity prices and the comparatively low cleaning costs.