Most issues carried out about building integrated photovoltaic (PV) system performance show average losses of about 20%-25% in electricity production. The causes are varied, e.g., mismatching losses, partial shadows, variations in current-voltage (I-V) characteristics of PV modules due to manufacturing processes, differences in the orientations and inclinations of solar surfaces, and temperature effects. These losses can be decreased by means of suitable electronics. This paper presents the intelligent PV module concept, a low-cost high-efficiency dc-dc converter with maximum power point tracking (MPPT) functions, control, and power line communications (PLC). In addition, this paper analyses the alternatives for the architecture of grid-connected PV systems: centralized, string, and modular topologies. The proposed system, i.e., the intelligent PV module, fits within this last group. Its principles of operation, as well as the topology of boost dc-dc converter, are analyzed. Besides, a comparison of MPPT methods is performed, which shows the best results for the incremental conductance method. Regarding communications, PLC in every PV module and its feasibility for grid-connected PV plants are considered and analyzed in this paper. After developing an intelligent PV module (with dc-dc converter) prototype, its optimal performance has been experimentally confirmed by means of the PV system test platform. This paper describes this powerful tool especially designed to evaluate all kinds of PV systems

Solar photovoltaics (PV) is already the cheapest form of electricity generation in many countries and market segments. Market prices of PV modules and systems have developed so fast that it is difficult to find reliable up to date public data on real PV capital (CAPEX) and operational expenditures (OPEX) on which to base the levelised cost of electricity (LCOE) calculations. This paper projects the future utility‐scale PV LCOE until 2050 in several European countries. It uses the most recent and best available public input data for the PV LCOE calculations and future projections. Utility‐scale PV LCOE in 2019 in Europe with 7% nominal weighted average cost of capital (WACC) ranges from 24 €/MWh in Malaga to 42 €/MWh in Helsinki. This is remarkable since the average electricity day‐ahead market price in Finland was 47 €/MWh and in Spain 57 €/MWh in 2018. This means that PV is already cheaper than average spot market electricity all over Europe. By 2030, PV LCOE will range from 14 €/MWh in Malaga to 24 €/MWh in Helsinki with 7% nominal WACC. This range will be 9 to 15 €/MWh by 2050, making PV clearly the cheapest form of electricity generation everywhere. Sensitivity analysis shows that apart from location, WACC is the most important input parameter in the calculation of PV LCOE. Increasing nominal WACC from 2 to 10% will double the LCOE. Changes in PV CAPEX and OPEX, learning rates, or market volume growth scenarios have a relatively smaller impact on future PV LCOE.

Multiple energy‐related crises require a fast transition towards a sustainable energy system. The European Green Deal aims for zero CO₂ emission by 2050, while accelerating climate change impacts obligate a faster phase‐out of fossil fuels. Energy transition studies for Europe at and near 100% renewable energy are used as a benchmark for two newly introduced scenarios for Europe reaching zero CO₂ emissions by 2050 and 2040. A technology‐rich energy system model was applied in hourly resolution for Europe in 20 interconnected regions and in full sector coupling covering all energy demands. The results reveal a cost‐neutral energy transition towards 2050 based on declining levelised cost of electricity and a pathway with 9% higher energy costs leading to 17% lower total CO₂ emissions with an accelerated energy transition by 2040. The two scenarios find shares of solar photovoltaic (PV) in total generation of 61%–63% by 2050, the highest ever estimated for Europe, still below the highest global average shares ranging between 75% and 77% form three independent studies. The central energy system components are solar PV, wind power, batteries, electrolysers and CO₂ direct air capture for carbon capture and utilisation. The core characteristic of the European energy future may be best described by a power‐to‐X economy, which may evolve on the global scale to a solar‐to‐X economy.

More and more distributed maximum power point tracking (DMPPT) architectures, based on a dc-dc converter for every single photovoltaic (PV) panel, are being released and launched to the market as an alternative to traditional MPPT configurations. Ideally, they can solve any mismatching problem, but actually, this is not always feasible. This paper analyzes the static performance of this kind of architectures in order to determine the key factors to bear in mind in the design of the system. Above all, this paper points out the inverter-voltage influence on the overall performance of the PV system.

In this paper, we investigate the use of monofacial PV models to simulate the production of bifacial PV systems over different albedos. Analytical and empirical models were evaluated using measured data obtained from three identical bifacial PV arrays: (1) with the backside covered by white plastic, (2) with normal albedo, and (3) with high albedo. The front-and rear-side irradiances were measured in order to integrate bifaciality of the modules into the models. The models showed good performance for non-real-time monitoring, especially under clear skies, and the analytical model was more accurate than the empirical model. The heatmap visualization technique was applied to six months of data in order to investigate the site conditions on the rear side of the modules as well as the accuracy of the models. The heatmap results of the rear- and front-sides irradiances showed that the installation conditions, such as the azimuth angles of the sun and the surrounding obstacles, had a strong impact on the energy received from the back of the modules. The heatmap results of the models validated the performance of the analytical model. The average daily errors for the analytical model were less than 1% and 3% for normal and high albedos, respectively.