Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.

Time series wind power forecasting based on variant Gaussian process and TLBO

Due to the variability and stochastic nature of wind power system, accurate wind power forecasting has an important role in developing reliable and economic power system operation and control strategies. As wind variability is stochastic, Gaussian Process regression has recently been introduced to capture the randomness of wind energy. However, the disadvantages of Gaussian Process regression include its computation complexity and incapability to adapt to time varying time-series systems. A variant Gaussian Process for time series forecasting is introduced in this study to address these issues. This new method is shown to be capable of reducing computational complexity and increasing prediction accuracy. It is further proved that the forecasting result converges as the number of available data approaches innite. Further, a teaching learning based optimization (TLBO) method is used to train the model and to accelerate
the learning rate. The proposed modelling and optimization method is applied to forecast both the wind power generation of Ireland and that from a single wind farm to show the eectiveness of the proposed method.

Management of public water supply to reduce power cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Management of public water supply to reduce energy cost and improve wind power uptake

This paper presents a study on the implementation of Real-Time Pricing (RTP) based Demand Side Management (DSM) of water pumping at a clean water pumping station in Northern Ireland, with the intention of minimising electricity costs and maximising the usage of electricity from wind generation. A Genetic Algorithm (GA) was used to create pumping schedules based on system constraints and electricity tariff scenarios. Implementation of this method would allow the water network operator to make significant savings on electricity costs while also helping to mitigate the variability of wind generation.

Incorporating wind power forecast uncertainties into stochastic unit commitment using neural network-based prediction intervals

Penetration of renewable energy resources, such as wind and solar power, into power systems significantly increases the uncertainties on system operation, stability, and reliability in smart grids. In this paper, the nonparametric neural network-based prediction intervals (PIs) are implemented for forecast uncertainty quantification. Instead of a single level PI, wind power forecast uncertainties are represented in a list of PIs. These PIs are then decomposed into quantiles of wind power. A new scenario generation method is proposed to handle wind power forecast uncertainties. For each hour, an empirical cumulative distribution function (ECDF) is fitted to these quantile points. The Monte Carlo simulation method is used to generate scenarios from the ECDF. Then the wind power scenarios are incorporated into a stochastic security-constrained unit commitment (SCUC) model. The heuristic genetic algorithm is utilized to solve the stochastic SCUC problem. Five deterministic and four stochastic case studies incorporated with interval forecasts of wind power are implemented. The results of these cases are presented and discussed together. Generation costs, and the scheduled and real-time economic dispatch reserves of different unit commitment strategies are compared. The experimental results show that the stochastic model is more robust than deterministic ones and, thus, decreases the risk in system operations of smart grids.

Probability analysis of machine angle stability with non-gaussian wind power input

The security of power transfer across a given transmission link is typically a steady state assessment. This paper develops tools to assess machine angle stability as affected by a combination of faults and uncertainty of wind power using probability analysis. The paper elaborates on the development of the theoretical assessment tool and demonstrates its efficacy using single machine infinite bus system.

Doubly Fed Induction Generator for wind energy generation using nine-switch power converter

A Three-Phase Nine-Switch Converter (NSC) topology for Doubly Fed Induction Generator in wind energy generation is proposed in this paper. This converter topology was used in various applications such as Hybrid Electric Vehicles and Uninterruptable Power Supplies. In this paper, Nine-Switch Converter is introduced in Doubly Fed Induction Generator in renewable energy application for the first time. It replaces the conventional Back-to-Back Pulse Width Modulated voltage source converter (VSC) which composed of twelve switches in many DFIG applications. Reduction in number of switches is the most beneficial in terms of cost and power switching losses. The operation principle of Nine-Switch Converter using SPWM method is discussed. The resulting NSC performance of rotor side current control, active power and reactive control are compared with Back-to Back voltage source converter performance. DC link voltage regulation using front end converter is also presented. Finally the simulation results of DFIG performances using NSC and Back-to-Back VSC are analyzed and compared.

An analysis on the possibility of using capacitors of a three-level capacitor clamped inverter as power smoothing elements for wind power systems

This paper presents a new direct integration scheme for supercapacitors that are used to mitigate short term power fluctuations in wind power systems. The idea is to replace ordinary capacitors of a 3-level flying capacitor inverter by supercapacitors and operate them under variable voltage conditions. This approach eliminates the need of interfacing dc-dc converters for supercapacitor integration and thus considerably improves the overall efficiency. However, the major problem of this unique system is the change of supercapacitor voltages. An analysis on the effects of these voltage variations are presented. A space vector modulation method, built from the scratch, is proposed to generate undistorted current even in the presence of dynamic changes in supercapacitor voltages. A supercapacitor voltage equalisation algorithm is also proposed. Furthermore, resistive behavior of supercapacitors at high frequencies and the need for a low pass filter are highlighted. Simulation results are presented to verify the efficacy of the proposed system in suppressing short term wind power fluctuations.

A Battery Energy Storage interface for wind power systems with the use of grid side inverter

This paper presents a novel concept of Energy Storage System (ESS) interfacing with the grid side inverter in wind energy conversion systems. The inverter system used here is formed by cascading a 2-level inverter and a three level inverter through a coupling transformer. The constituent inverters are named as the “main inverter” and the “auxiliary inverter” respectively. The main inverter is connected with the rectified output of the wind generator while the auxiliary inverter is attached to a Battery Energy Storage System (BESS). The BESS ensures constant power dispatch to the grid irrespective of change in wind condition. Furthermore, this unique combination of BESS and inverter eliminates the need of additional dc-dc converters. Novel modulation and control techniques are proposed to address the problem of non-integer, dynamically-changing dc-link voltage ratio, which is due to random wind changes. Strategies used to handle auxiliary inverter dc-link voltage imbalances and controllers used to charge batteries at different rates are explained in detail. Simulation results are presented to verify the efficacy of the proposed modulation and control techniques in suppressing random wind power fluctuations.

A dual inverter with integrated energy storage for wind power systems

This paper explores the possibility of using grid side inverter as an interface to connect energy storage systems. A dual inverter system, formed by cascading two 2-level inverters through a coupling transformer, is used as the testing model. The inverters are named as “main inverter” and “auxiliary inverter”. The main inverter is powered by the rectified output of the wind generator while the auxiliary inverter is attached to a Battery Energy Storage System (BESS). If there is a surplus of wind power compared to the demand, then that would be stored in BESS while if there is a deficit in wind power then the demand will be satisfied by supplying power from the BESS. This enables constant power dispatch to the grid irrespective of wind changes. Novel modulation and control techniques are proposed to address the problem of non-integer, dynamically-varying dc-link voltage ratio, which is due to random wind changes. Furthermore, a maximum power tracking controller for this unique system is explained in detail. Simulation results verify the efficacy of proposed modulation and control techniques in suppressing random power fluctuations.

Variable structure control for maximum wind power extraction

EuroPES 2009

Asymmetrical low-voltage ride through of brushless doubly fed induction generators for the wind power generation

Compared with the Doubly fed induction generators (DFIG), the brushless doubly fed induction generator (BDFIG) has a commercial potential for wind power generation due to its lower cost and higher reliability. In the most recent grid codes, wind generators are required to be capable of riding through low voltage faults. As a result of the negative sequence, induction generators response differently in asymmetrical voltage dips compared with the symmetrical dip. This paper gave a full behavior analysis of the BDFIG under different types of the asymmetrical fault and proposed a novel control strategy for the BDFIG to ride through asymmetrical low voltage dips without any extra hardware such as crowbars. The proposed control strategies are experimentally verified by a 250-kW BDFIG. © 2012 IEEE.