State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems

The integration of wind energy into power systems has intensified as a result of the urgency for global energy transition. This requires more accurate forecasting techniques that can capture the variability of the wind resource to achieve better operative performance of power systems. This paper presents an exhaustive review of the state-of-the-art of wind-speed and -power forecasting models for wind turbines located in different segments of power systems, i.e., in large wind farms, distributed generation, microgrids, and micro-wind turbines installed in residences and buildings. This review covers forecasting models based on statistical and physical, artificial intelligence, and hybrid methods, with deterministic or probabilistic approaches. The literature review is carried out through a bibliometric analysis using VOSviewer and Pajek software. A discussion of the results is carried out, taking as the main approach the forecast time horizon of the models to identify their applications. The trends indicate a predominance of hybrid forecast models for the analysis of power systems, especially for those with high penetration of wind power. Finally, it is determined that most of the papers analyzed belong to the very short-term horizon, which indicates that the interest of researchers is in this time horizon

Meteorological conditions during Dunkelflauten in Germany: Characteristics, the role of weather regimes and impacts on demand

Renewable generation from wind and solar power is strongly weather-dependent. To plan future sustainable energy systems that are robust to this variability, a better understanding of why and when periods of low wind and solar power output occur is valuable. We call such periods of low wind and solar power output `Dunkelflauten', the German word for dark wind lulls. In this article, we analyse the meteorological conditions during Dunkelflauten in Germany by applying the concept of weather regimes. Weather regimes are quasi-stationary, recurrent, and persistent large-scale circulation patterns which explain multi-day atmospheric variability (5-15 days). We use a regime definition that allows us to distinguish four different types of blocked regimes, characterised by high pressure situations in the North Atlantic-European region. We find that in Germany, Dunkelflauten mainly occur in winter when the solar power output is anyway low and when the wind power output drops for several consecutive days. A high-pressure system over Germany, associated with the European Blocking regime, is responsible for most of the Dunkelflauten. Dunkelflauten during the Greenland Blocking regime are associated with colder temperatures than usual, causing higher electricity demand and presenting a particular challenge as space heating demand electrifies in future. Furthermore, we show that Dunkelflauten occur predominantly when a weather regime is well-established and persists longer than usual. Our study provides novel insight on the occurrence and meteorological characteristics of Dunkelflauten, which is essential for planning resilient energy systems and supporting grid operators to prepare for potential shortages in supply.Comment: 20pages, 11figures, submitted to "Meteorological Applications" by Royal Meteorological Society (https://rmets.onlinelibrary.wiley.com/journal/14698080

Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems:A Review

Renewable energy sources (RESs) are the replacement of fast depleting, environment polluting, costly, and unsustainable fossil fuels. RESs themselves have various issues such as variable supply towards the load during different periods, and mostly they are available at distant locations from load centers. This paper inspects forecasting techniques, employed to predict the RESs availability during different periods and considers the dispatch mechanisms for the supply, extracted from these resources. Firstly, we analyze the application of stochastic distributions especially the Weibull distribution (WD), for forecasting both wind and PV power potential, with and without incorporating neural networks (NN). Secondly, a review of the optimal economic dispatch (OED) of RES using particle swarm optimization (PSO) is presented. The reviewed techniques will be of great significance for system operators that require to gauge and pre-plan flexibility competence for their power systems to ensure practical and economical operation under high penetration of RESs

Surface global irradiance assessed by three different methods

Three methods will be used to evaluate the surface global irradiance: radiative transfer theory, empirical regression, and artificial neural networks (ANN). Radiative transfer is the fundamental theory that describes the propagation of radiation through a medium; empirical regression predicts surface global irradiance in simple parameterizations; artificial neural network, as an artificial intelligence technique, can also be tried to assess the surface global irradiance. These three approaches are studied in the present work. Data from the station “EL IDEAM” were used in the modeling experiments built upon these approaches to evaluate the daily transparency, also known as clearness index. We found out that the optimal inputs for artificial neural networks are extraterrestrial irradiance, surface relative humidity, and a pollution index based on particulate matter of sizes less than 10µm (PM 10 ). Surface relative humidity was suggested in a regression trial under meteorological conditions of “EL IDEAM”. By means of the programming code DISORT for the solution of the radiative transfer equation, daily irradiance characteristics were analyzed, and a hybrid model was created. Our results showed that artificial neural network produces higher scores than the other methods, though advantages and drawbacks are also discussed and compared.Maestrí