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This paper presents a measurement procedure to be implemented in smart Power Quality Analyzers (PQA), and conceived to monitor service nodes in the current electrical network. It is thought on the need for developing measurement instruments capable of detecting newly PQ events, which commonly appear in the modern Smart Grid (SG), adding at the same time complementary information, regarding the real-time network state, than the conventional meters do. Its novelty resides in the deployment of the events’ trajectories in three types of 2D maps based in Higher-Order Statistics (HOS). The events under study appear isolated or in hybrid versions: sags and transients. Examining the HOS dynamics (trajectories in the HOS planes), the procedure allows the online detection and help detect specific types of events. Additionally, this trajectory-based method reveals some other features like hybrid events, the depth or event degree, the duration and the phase changes. The results are based on real-life signals, which have been surveyed in a building’s node. A complementary deviation index gathers the information regarding the individual distances between each statistic and its associated ideal value. [ABSTRACT FROM AUTHOR]

There is a prolific literature on the mitigation of cloud-induced photovoltaic fluctuations as a key objective for the effective grid integration of this renewable energy capacity. The meteorological context is of high relevance in such a problem, but it is frequently analysed separately from electrical considerations. However, a few studies have combined both sides of the problem, thus providing valuable references for examining their interdependencies. Therefore, this review focuses on this set of studies to explore the use of meteorological information in the analysis of cloud-induced photovoltaic transients in terms of scales, elements, and objectives under consideration. This examination shows the dependence of the results on the chosen context, allowing for the identification of the strengths and weaknesses of each approach. On this basis, some recommendations for future work are drawn, such as the use of spatiotemporal descriptions of cloud passage, description of the meteorological scenario for an adequate interpretation of results, and necessity of holistic approaches to integrate advances from the meteorological and electrical perspectives of the problem. [Display omitted] • Lack of meteorological information in cloud-induced PV transient analysis. • Meteorological context has significant impact on results. • Cloud passages' spatiotemporal descriptions are necessary for realistic estimations. • Integration of meteorological and electrical advances needed for complete solutions. [ABSTRACT FROM AUTHOR]

The interconnected modern smart grid has resulted in several new power quality (PQ) disturbances that affect the frequency stability requirements in industrial power systems. Ranging between 50 ± 0. 1 Hz for interconnected networks, and even within the European synchronous area, the frequency fluctuates mainly because of the primary control. In fact, owing to the development of distributed resources (e.g. renewable energies), not all generation units are accessible, and are controllable in a few network nodes. This variability, even between nearby locations, triggers the need for a specific measurement strategies, based on an absolute reference and also conceived for monitoring PQ disturbances. This work proposes an easy-to-implement GPS-traceable method that allows the measurement of frequency with better accuracy than that required by the current regulation for interconnected networks. This work explains the measurement methodology, characterising the uncertainty by using ISO JCGM 100. For the purpose of testing and validation, three experiments were conducted: computer-designed, instrument-triggered lab disturbances, and a comparison vs. PMU performance, using real-life signals. The results showed that these experiments offered better performance with improved uncertainty. [Display omitted] • Joint characterization of accuracy and frequency stability. • GPS-referenced reliability monitoring of bulk system and power delivery. • Measurement procedure, UTC synchronized and with accuracy up to 100 ppm. • Alternative to PMUs for measuring the frequency in accordance with the (IEC). • Method without DAQ-board using zero-crossing and GPS. [ABSTRACT FROM AUTHOR]

• Synthetic framework and benchmark methods for cloud motion methods validation • Cross-correlation problems with small-scale irradiance sensor networks. • Benchmarking of a novel cloud motion estimation method for small-scale PV Systems. • 32 tests with RMSE improvements of 50–90% in 75% of cases respect benchmark methods. • Generalization of the proposed method to non-gridded irradiance networks Small-scale PV generation is particularly affected by the irradiance variability produced by cloud shadows. Accurate predictions of the clouds passing over the PV field are thus necessary for the optimized management and integration of this renewable power source. Two main elements are required to perform these predictions: the local irradiance field and the cloud motion vector. Irradiance sensor networks are positioning as a promising data source at the spatial and temporal scales of the problem, avoiding the irradiance inference problems and costs of image-based instrumentation. This paper proposes a method to infer the cloud shadow motion vector from small-scale irradiance sensor networks data. The method does not require specific network configuration or layout, and the algorithm is computationally simple: graphical solutions are obtained by aggregating mean absolute errors in a diagram/matrix with each element representing a possible displacement of the cloud shadows. The validation is conducted with a fractal cloud model that allows the generation of irradiance time series according to an arbitrary cloud motion vector. The most correlated pair and the linear cloud edge methods are used for benchmarking purposes. Gridded and non-gridded sensors layouts are tested with number of sensors ranging from 9 to 100, monitored areas from 100 to 10000 m2, and sampling periods from 0.3 to 3.3 s. The results show the superiority of the proposed method with a reduction of 50–90% of the root mean square errors respect to the benchmark methods in 75% of the tests. Additionally, the proposed method maintains similar performance as sampling rate decreases, while the benchmark methods exhibit worsening results. [ABSTRACT FROM AUTHOR]

Highlights • Weather information is a fundamental input for microgrid scheduling. • A wide variety of weather information has been incorporated into prior microgrid studies. • The diversity of procedures to implement weather data in microgrid energy management systems are analyzed. • Discussion and recommendations for future works are conducted. Abstract Meteorological conditions determine the renewable energy generation and, to a lesser extent, the load of microgrids. Weather forecasts are thus necessary to establish optimal plans according to the operational objectives and priorities of each microgid. Weather forecast errors are also responsible for deviations from these plans, thereby being an important source of uncertainty in the scheduling process. Despite this, weather information plays a secondary role in most of microgrid studies. This paper provides a general overview of the use of meteorological data in microgrids, focusing on the implementation of weather forecasts in microgrid energy management systems. Data sources, methodologies, uncertainty approaches and results from a selection of papers with complete information about the forecast context are analysed in detail. Additionally, similarities and differences regarding other energy forecast applications apart from microgrids are discussed. Finally, on the basis of the above, a list of recommendations for future implementations of weather forecasts in microgrid energy management systems is presented. [ABSTRACT FROM AUTHOR]