The integration of renewables into power systems has led to multiple studies and analysis in terms of grid-power quality, reliability, and/or feasibility. Among different resources to be considered as alternative energy systems, wind and solar emerge as the most mature technologies. With regard to photovoltaic (PV) installations, monitoring problems requires detailed analysis, since solar-radiation fluctuations, soiling on solar panels, or deficiency of PV-panel performance can involve unexpected power-output oscillations and, subsequently, undesirable power-generation oscillations. Under this framework, this paper describes and assesses a wireless low-cost PV-module monitoring system based on open-source solutions. Our proposal allows us to monitor installations at the PV-module level, giving detailed information regarding PV power-plant performance. The proposed monitoring system is based on the IEC-61724 standard requirements, as a flexible and ad hoc solution with relevant connectivity options. Meteorological and electrical data are collected from the developed nodes and available for subsequent analysis. Detailed information of the solution, as well as extensive results collected in Spanish PV power plants connected to the grid, are also included in the paper. Energies 2018, 11, 3051 2 of 20Best et al. [5] affirms that solar and wind resources are on the upper rungs of the energy ladder, their integration being predominant in higher-income countries. Recent contributions, focused on future electricity systems, also assume that renewable assets can be considered a mix between wind-and solar-power generation [6]. In [7], and apart from wind and PV solar deployment, a slight increase has been estimated during the past few years for other renewable energy sources for electricity. In addition, Ellabban et al. [8] establishes a direct connection between technological maturity and economic barriers, mainly when the cost of a technology is above the cost of other competing alternatives. Therefore, wind and PV solar resources are currently the most integrated renewable energy sources into power systems, excluding hydropower-generation units. Nevertheless, there are no large-area power systems in the world where wind and solar power generate more than 50% of electricity [9].The integration of wind and PV power plants into power systems involves certain technical problems, mainly focused on reliability, power quality, and stability [10]. The intermittent nature of such sources may increase grid stress, mainly due to undesirable oscillations on the supply side [11] that may affect voltage regulation of transmission systems [12]. At a low/medium-voltage level, different reactive power control strategies have also been proposed to facilitate renewables integration [13], including active power curtailment [14]. As an additional solution to guarantee a balance between generation and demand, with reduced capacity and costs of operating reserves [15], short-term forecasting of PV production emerges as a relevant topic of inte...

This paper proposes a wireless low-cost solution based on long-range (LoRa) technologyable to communicate with remote PV power plants, covering long distances with minimum powerconsumption and maintenance. This solution includes a low-cost open-source technology atthe sensor layer and a low-power wireless area network (LPWAN) at the communication layer,combining the advantages of long-range coverage and low power demand. Moreover, it offers anextensive monitoring system to exchange data in an Internet-of-Things (IoT) environment. A detaileddescription of the proposed system at the PV module level of integration is also included in the paper,as well as detailed information regarding LPWAN application to the PV power plant monitoringproblem. In order to assess the suitability of the proposed solution, results collected in real PVinstallations connected to the grid are also included and discussed.

The increasing integration of photovoltaic (PV) power plants into power systems demands a high accuracy of yield prediction and measurement. With this aim, different global horizontal irradiance (GHI) estimations based on new-generation geostationary satellites have been recently proposed, providing a growing number of solutions and databases, mostly available online, in addition to the many ground-based irradiance data installations currently available. According to the specific literature, there is a lack of agreement in validation strategies for a bankable, satellite-derived irradiance dataset. Moreover, different irradiance data sources are compared in recent contributions based on a diversity of arbitrary metrics. Under this framework, this paper describes a characterization of metrics based on a principal component analysis (PCA) application to classify such metrics, aiming to provide non-redundant and complementary information. Therefore, different groups of metrics are identified by applying the PCA process, allowing us to compare, in a more extensive way, different irradiance data sources and exploring and identifying their differences. The methodology has been evaluated using satellite-based and ground-measured GHI data collected for one year in seven different Spanish locations, with a one-hour sample time. Data characterization, results, and a discussion about the suitability of the proposed methodology are also included in the paper.

Power system configuration and performance are changing very quickly. Under the new paradigm of prosumers and energy communities, grids are increasingly influenced by microgeneration systems connected in both low and medium voltage. In addition, these facilities provide little or no information to distribution and/or transmission system operators, increasing power system management problems. Actually, information is a great asset to manage this new situation. The arrival of affordable and open Internet of Things (IoT) technologies is a remarkable opportunity to overcome these inconveniences allowing for the exchange of information about these plants. In this paper, we propose a monitoring solution applicable to photovoltaic self-consumption or any other microgeneration installation, covering the installations of the so-called ’prosumers’ and aiming to provide a tool for local self-consumption monitoring. A detailed description of the proposed system at the hardware level is provided, and extended information on the communication characteristics and data packets is also included. Results of different field test campaigns carried out in real PV self-consumption installations connected to the grid are described and analyzed. It can be affirmed that the proposed solution provides outstanding results in reliability and accuracy, being a popular solution for those who cannot afford professional monitoring platforms.

Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies.