Heterogeneous Communication Network Architecture for the Management of Electric Vehicle Charging Stations: Multi-Aggregator Management in Microgrids with High Photovoltaic Variability Based on Multiple Solar Radiation Sensors.

Electric power systems with a high penetration of photovoltaic generation and a relevant fleet of electric vehicles face significant stability challenges, particularly in mountainous areas where the variability of photovoltaic resources is pronounced. This study presents a novel methodology to strategically place electric vehicle aggregators along a feeder. This approach considers electrical variables and the dynamics of cloud movements within the study area. This innovative methodology reduces the substation's power load demand and significantly improves the end user's voltage levels. The improvements in voltage regulation and reduced demand on the substation provide clear benefits, including increased system resilience, better integration of renewable energy sources, and enhanced overall efficiency of the electric grid. These advantages are particularly critical in regions with high levels of photovoltaic generation and are important in promoting sustainable electric vehicle charging infrastructure. When analyzing different load scenarios for the IEEE European Low Voltage Test Feeder system, the consideration of distributed aggregators based on cloud movements decreased the power required at the substation by 21.25%, and the voltage drop in loads was reduced from 6.9% to 4.29%. This research underscores the critical need to consider both the variability and geographical distribution of PV resources in the planning and operation of electrical systems with extensive PV generation.

Passive Electroluminescence and Photoluminescence Imaging Acquisition of Photovoltaic Modules.

In photovoltaic power plant inspections, techniques for module assessment play a crucial role as they enhance fault detection and module characterization. One valuable technique is luminescence. The present paper introduces a novel technique termed passive luminescence. It enhances both electroluminescence and photoluminescence imaging acquisition in photovoltaic power plants under normal operation in high irradiance conditions. This technique is based on the development of an electronic board, which allows the polarity of the module to be changed, enabling the current generated by the photovoltaic string to be injected into the module and producing electroluminescence effects. Additionally, the board can bypass the module and set an open circuit, inducing photoluminescence emission using sunlight as an excitation source. The proper coordination of the board and an InGaAs camera with a bandpass filter has allowed for the integration of a lock-in technique, which has produced electroluminescence and photoluminescence pictures that can be used for fault detection.

A Power-Line Communication System Governed by Loop Resonance for Photovoltaic Plant Monitoring.

Within this paper, a PLC system that takes advantage of the loop resonance of an entire DC-PV string configured as a circular signal path is developed and implemented. Low cost and extremely simple transceivers intended to be installed within each PV module of a string have been designed and successfully tested. In addition, an anti-saturation coil has been conceived to avoid saturation of the core when the entire DC current of the string flows through it. Bi-directional half-duplex communication was successfully executed with up to a 1 MHz carrier frequency (150 kbps bitrate), using a simple ASK modulation scheme. The transmission and reception performance are presented, along with the overall system cost in comparison to the previous literature.

Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting.

This work proposes a quantile regression neural network based on a novel constrained weighted quantile loss (CWQLoss) and its application to probabilistic short and medium-term electric-load forecasting of special interest for smart grids operations. The method allows any point forecast neural network based on a multivariate multi-output regression model to be expanded to become a quantile regression model. CWQLoss extends the pinball loss to more than one quantile by creating a weighted average for all predictions in the forecast window and across all quantiles. The pinball loss for each quantile is evaluated separately. The proposed method imposes additional constraints on the quantile values and their associated weights. It is shown that these restrictions are important to have a stable and efficient model. Quantile weights are learned end-to-end by gradient descent along with the network weights. The proposed model achieves two objectives: (a) produce probabilistic (quantile and interval) forecasts with an associated probability for the predicted target values. (b) generate point forecasts by adopting the forecast for the median (0.5 quantiles). We provide specific metrics for point and probabilistic forecasts to evaluate the results considering both objectives. A comprehensive comparison is performed between a selection of classic and advanced forecasting models with the proposed quantile forecasting model. We consider different scenarios for the duration of the forecast window (1 h, 1-day, 1-week, and 1-month), with the proposed model achieving the best results in almost all scenarios. Additionally, we show that the proposed method obtains the best results when an additive ensemble neural network is used as the base model. The experimental results are drawn from real loads of a medium-sized city in Spain.

Infrared Thermography for the Detection and Characterization of Photovoltaic Defects: Comparison between Illumination and Dark Conditions.

Newly installed renewable power capacity has been increasing incredibly in recent years. For example, in 2018, 181 GW were installed worldwide. In this scenario, in which photovoltaic (PV) energy plays a leading role, it is essential for main players involved in PV plants to be able to identify the failure modes in PV modules in order to reduce investment risk, to focus their maintenance efforts on preventing those failures and to improve longevity and performance of PV plants. Among the different systems for defects detection, conventional infrared thermography (IRT) is the fastest and least expensive technique. It can be applied in illumination and in dark conditions, both indoor and outdoor. These two methods can provide complementary results for the same kind of defects, which is analyzed and characterized in this research. Novel investigation in PV systems propose the use of a power inverter with bidirectional power flow capability for PV plants maintenance, which extremely facilitates the electroluminescence (EL) inspections, as well as the outdoor IRT in the fourth quadrant.

Energy-ecological efficiency of dual-fuel series plug-in hybrid electric vehicle considering WTW emissions.

This paper discussed the possibility of replacing the internal combustion engine of the series plug-in hybrid electric vehicle (PHEV) powered by gasoline A and Brazilian gasoline in single-fuel mode by one fuelled with 50% bioethanol and 50% biogas in dual-fuel mode. The simulation of the combustion of the fuels selected, such as bioethanol, biogas and gasoline A, was carried out through GASEQ software to calculate the energy-ecological efficiency of the single-fuel and dual-fuel modes. The well-to-pump (WTP) emissions of the bioethanol and biogas production from sugarcane were evaluated through GREET software. The tank-to-wheel (TTW) emissions were determined to each series PHEV operating modes. Thus, the well-to-wheel (WTW) emissions were calculated through the sum of the WTP, TTW and electricity mix emissions. According to the results, the energy-ecological efficiency for the dual-fuel mode was 10.7% and 24.1% higher than that found for the single-fuel mode powered by gasoline and Brazilian gasoline, respectively. The analysis showed that the losses during the biogas production aggravate linearly the WTP emissions, and consequently, the WTW emissions of the series PHEV. Besides that, the dual-fuel mode presented 15.5% and 12.8 less TTW emissions than the single-fuel mode powered by gasoline A and Brazilian gasoline, respectively. Compared to the emission standards, the dual-fuel mode presented TTW emissions 30.5% higher than the European Union emission standard by 2021. Although the dual-fuel mode does not meet any of the emission standards, this engine mode can be an alternative to at least reduce the tailpipe emissions.