Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time

Traditionally, inertia in power systems has been determined by considering all the rotating masses directly connected to the grid. During the last decade, the integration of renewable energy sources, mainly photovoltaic installations and wind power plants, has led to a significant dynamic characteristic change in power systems. This change is mainly due to the fact that most renewables have power electronics at the grid interface. The overall impact on stability and reliability analysis of power systems is very significant. The power systems become more dynamic and require a new set of strategies modifying traditional generation control algorithms. Indeed, renewable generation units are decoupled from the grid by electronic converters, decreasing the overall inertia of the grid. ‘Hidden inertia’, ‘synthetic inertia’ or ‘virtual inertia’ are terms currently used to represent artificial inertia created by converter control of the renewable sources. Alternative spinning reserves are then needed in the new power system with high penetration renewables, where the lack of rotating masses directly connected to the grid must be emulated to maintain an acceptable power system reliability. This paper reviews the inertia concept in terms of values and their evolution in the last decades, as well as the damping factor values. A comparison of the rotational grid inertia for traditional and current averaged generation mix scenarios is also carried out. In addition, an extensive discussion on wind and photovoltaic power plants and their contributions to inertia in terms of frequency control strategies is included in the paper.This work was supported by the Spanish Education, Culture and Sports Ministry [FPU16/04282]

Vertical wind profile characterization and identification of patterns based on a shape clustering algorithm

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/042

The Use of Electrical Measurements of Wind Turbine Generators for Drive Train Condition Monitoring

More modern and larger wind turbine (WT) generators are under continuous development. These exhibit more faults than smaller ones, which becomes critical offshore. Under this framework, operation and maintenance (O&M) is the key to improve reliability and availability of WTs, where condition-based maintenance (CBM) is currently seen as the preferred approach by the early detection and diagnosis of critical faults for WTs. The induction generator is one of the biggest contributors to failure rates and downtime of WTs, together with the gearbox and the drive train. In the present chapter, current signature analysis (CSA) will be introduced as a means for fault detection of WTs. CSA is a cost-effective and nonintrusive technique that can monitor both mechanical and electrical faults within the induction generator, as well as bearing- and gearbox-related faults. Different test cases of in-service wind turbine generators will be used to illustrate its usefulness

Electricity consumption analysis for university buildings. Empirical approach for University of Castilla-La Mancha, campus Albacete (Spain)

[EN] New global situation is boosting the necessity of analysing electricity consumption of university buildings, mainly motived by the exorbitant increase in electricity prices. In this regard, knowing such demand aims at three goals: i) to reduce their consumption, ii) to increase energy efficiency and iii) to develop solar PV installations. Very few research has previously analysed aggregated energy data for educational buildings, and none have studied detailed real electricity consumption in terms of hourly data, which results of utmost relevance, especially for the development of solar PV installations in these environments. Our research tackles this issue and provides a complete methodology to analyse electrical energy and hourly data consumption in university buildings, based on electricity indicators and patterns. The research has been applied to the University of Castilla-La Mancha, in Spain. A complete year data base (2021) of real power and electrical energy consumption of the whole campus has been collected and analysed, with an hourly scale. Results revealed that Biomedical Complex corresponds to the highest load demanding building of the campus (2770 MWh, 43% of the total campus). Outcomes also disclosed an annual high consumption base of 250 kWh for this building, together with 6 different seasonality patterns and 2 annual daily patterns.This work was supported in part by the Spanish Public Administration "Ministerio de Universidades" under the grant Margarita Salas-Universitat Politècnica de València, funded by the European Union-Next Generation EU, by the Council of Communities of Castilla-La Mancha (Junta de Comunidades de Castilla-La Mancha, JCCM) through project SBPLY/19/180501/000287 and by the European Regional Development Fund (Fondo Europeo de Desarrollo Regional, FEDER).Bastida-Molina, P.; Torres-Navarro, J.; Honrubia-Escribano, A.; Gómez Lázaro, E. (2022). Electricity consumption analysis for university buildings. Empirical approach for University of Castilla-La Mancha, campus Albacete (Spain). Aedermacp (EA4EPQ). 216-221. https://doi.org/10.24084/repqj20.26621622

Ellagic acid as a tool to limit the diabetes burden: Updated evidence

Oxidative stress contributes not only to the pathogenesis of type 2 diabetes (T2D) but also to diabetic vascular complications. It follows that antioxidants might contribute to limiting the diabetes burden. In this review we focus on ellagic acid (EA), a compound that can be obtained upon intestinal hydrolysis of dietary ellagitannins, a family of polyphenols naturally found in several fruits and seeds. There is increasing research on cardiometabolic effects of ellagitannins, EA, and urolithins (EA metabolites). We updated research conducted on these compounds and (I) glucose metabolism; (II) inflammation, oxidation, and glycation; and (III) diabetic complications. We included studies testing EA in isolation, extracts or preparations enriched in EA, or EA-rich foods (mostly pomegranate juice). Animal research on the topic, entirely conducted in murine models, mostly reported glucose-lowering, antioxidant, anti-inflammatory, and anti-glycation effects, along with prevention of micro-and macrovascular diabetic complications. Clinical research is incipient and mostly involved non-randomized and low-powered studies, which confirmed the antioxidant and anti-inflammatory properties of EA-rich foods, but without conclusive results on glucose control. Overall, EA-related compounds might be potential agents to limit the diabetes burden, but well-designed human randomized controlled trials are needed to fill the existing gap between experimental and clinical research.A.S.-V. is recipient of the Instituto de Salud Carlos III Miguel Servet fellowship (grant CP II17/00029)

Characterization and visualization of voltage dips in wind power installations

The purpose of this paper is to describe and assess a new characterization and classification method of voltage dips. The proposed method allows us to classify multistage and real voltage dips when measurement errors or transients are present in the recorded data. Different real voltage dips are used to assess this method, comparing the results with previous approaches. For visualization purposes, a voltage-space vector representation is introduced in order to clarify the global voltage dip evolution along the time. The classification of all the voltage dips, measured from a one-year field measurement campaign in wind farms located in the Spanish region of Castilla-La Mancha, is also presented and characterized according to the proposed method.This work was supported in part by the “Ministerio de Educación y Ciencia”—ENE2006-15422-C02-01/ALT, in part by ENE2006-15422-C02-02/ALT—, and in part by “Junta de Comunidades de Castilla-La Mancha” (PAI08-0145-9976). Paper no. TPWRD-00237-200

A detailed analysis of electricity consumption at the University of Castilla-La Mancha (Spain)

[EN] The current energy crisis has drastically altered forecast electricity plans and budgets for European university campuses. This situation heightens the need to analyze their electrical consumption, with two main goals: identifying their patterns and promoting the development of renewable installations for these consumers. Previous research has focused only on aggregated demand data, with the studies being based on estimations and forecasts, and focused mainly on single buildings. Moreover, there is a lack of scientific papers that provide a replicable codebase for electricity analysis. Our work presents a novel methodology to overcome these research gaps, proposing the first comprehensive, replicable and scalable codebase to analyze electricity consumption in universities. It is based on three steps. The first comprises automated data collection of real electricity measurements at each electricity supply point. The second develops the complete analysis of electricity consumption. The last step parameterizes this consumption by identifying seasonal and daily profiles. The research was applied to the University of Castilla-La Mancha, campus Albacete (Spain) case study. The results revealed the 4 highest electricity-demanding buildings: Biomedical Complex, Higher Technical School of Industrial and Computer Engineering, Vice-rectorate and Library, and Higher Technical School of Agricultural and Forestry Engineering. The results are thus of great value for other educational buildings.We are grateful to the Infrastructure Management Office of the UCLM and Mr. Jose Jaen Cebrian (Technician of Infrastructures for UCLM-AB) for providing information about the campus equipment related to this research. This work was partially supported by the Ministry of Science and Innovation, the European Union (Next GenrationEU), the AEI through project PID2021-126082OB-C21 and by the Council of Communities of Castilla-La Mancha (Junta de Comunidades de Castilla-La Mancha, JCCM) through project SBPLY/19/180501/000287.Bastida-Molina, P.; Torres-Navarro, J.; Honrubia-Escribano, A.; Gallego-Giner, I.; Gómez-Lázaro, E. (2023). A detailed analysis of electricity consumption at the University of Castilla-La Mancha (Spain). Energy and Buildings (Online). 289. https://doi.org/10.1016/j.enbuild.2023.11304628

Wind Power Variability and Singular Events

Examples of the different events affecting wind power fluctuations are shown.The behaviors and the responses of the Spanish power system and wind power plants experiencing such events were analyzed. Examples presented in this chapter show that some of the wind power integration issues are related to low-voltage ride-through. They are solved through strict grid code enforcement. Other solutions to manage the reserve power generation and the wind power fluctuations are also very important in order to achieve high levels of wind power penetration. In the Spanish case, this could require increasing the availability of dispatchable and fast-start power plants, as well as increasing wind power plant participation on supporting the power system by providing voltage control, inertial emulation, frequency control, oscillation damping, or updated voltage ride-through capabilities.Comment: Chapter of the Book 'Wind Power' (eBook (PDF) ISBN: 978-953-51-6418-0