Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised.

Current methods and advances in forecasting of wind power generation

Wind power generation differs from conventional thermal generation due to the stochastic nature of wind. Thus wind power forecasting plays a key role in dealing with the challenges of balancing supply and demand in any electricity system, given the uncertainty associated with the wind farm power output. Accurate wind power forecasting reduces the need for additional balancing energy and reserve power to integrate wind power. Wind power forecasting tools enable better dispatch, scheduling and unit commitment of thermal generators, hydro plant and energy storage plant and more competitive market trading as wind power ramps up and down on the grid. This paper presents an in-depth review of the current methods and advances in wind power forecasting and prediction. Firstly, numerical wind prediction methods from global to local scales, ensemble forecasting, upscaling and downscaling processes are discussed. Next the statistical and machine learning approach methods are detailed. Then the techniques used for benchmarking and uncertainty analysis of forecasts are overviewed, and the performance of various approaches over different forecast time horizons is examined. Finally, current research activities, challenges and potential future developments are appraised. (C) 2011 Elsevier Ltd. All rights reserved.

A dynamic factor model for mid-term forecasting of wind power generation

The main objective of this paper is the development and application of multivariate time series models for forecasting aggregated wind power production in a country or region. Nowadays, in Spain, Denmark or Germany there is an increasing penetration of this kind of renewable energy, somehow to reduce energy dependence on the exterior, but always linked with the increaseand uncertainty affecting the prices of fossil fuels. The disposal of accurate predictions of wind power generation is a crucial task both for the System Operator as well as for all the agents of the Market. However, the vast majority of works rarely onsider forecasting horizons longer than 48 hours, although they are of interest for the system planning and operation. In this paper we use Dynamic Factor Analysis, adapting and modifying it conveniently, to reach our aim: the computation of accurate forecasts for the aggregated wind power production in a country for a forecasting horizon as long as possible, particularly up to 60 days (2 months). We illustrate this methodology and the results obtained for real data in the leading country in wind power production: Denmark

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.

Direct integration of battery energy storage systems in distributed power generation

In this paper, a wind energy conversion system interfaced to the grid using a dual inverter is proposed. One of the two inverters in the dual inverter is connected to the rectified output of the wind generator while the other is directly connected to a battery energy storage system (BESS). This approach eliminates the need for an additional dc-dc converter and thus reduces power losses, cost, and complexity. The main issue with this scheme is uncorrelated dynamic changes in dc-link voltages that results in unevenly distributed space vectors. A detailed analysis on the effects of these variations is presented in this paper. Furthermore, a modified modulation technique is proposed to produce undistorted currents even in the presence of unevenly distributed and dynamically changing space vectors. An analysis on the battery charging/discharging process and maximum power point tracking of the wind turbine generator is also presented. Simulation and experimental results are presented to verify the efficacy of the proposed modulation technique and battery charging/discharging process.

Cost efficiency of Japanese steam power generation companies : a Bayesian comparison of random and fixed frontier models

This study analyses and compares the cost efficiency of Japanese steam power generation companies using the fixed and random Bayesian frontier models. We show that it is essential to account for heterogeneity in modelling the performance of energy companies. Results from the model estimation also indicate that restricting CO2 emissions can lead to a decrease in total cost. The study finally discusses the efficiency variations between the energy companies under analysis, and elaborates on the managerial and policy implications of the results.

Regulation, pollution and heterogeneity in Japanese steam power generation companies

In this paper, the random stochastic frontier model is used to estimate the technical efficiency of Japanese steam power generation companies taking into regulation and pollution. The companies are ranked according to their productivity for the period 1976-2003 and homogenous and heterogeneous variables in the cost function are disentangled. Policy implication is derived.

Some of the design considerations in power generation from offshore wind farms

The global efforts to reduce carbon emissions from power generation have favoured renewable energy resources such as wind and solar in recent years. The generation of power from the renewable energy resources has become attractive because of various incentives provided by government policies supporting green power. Among the various available renewable energy resources, the power generation from wind has seen tremendous growth in the last decade. This article discusses various advantages of the upcoming offshore wind technology and associated considerations related to their construction. The conventional configuration of the offshore wind farm is based on the alternative current internal links. With the recent advances of improved commercialised converters, voltage source converters based high voltage direct current link for offshore wind farms is gaining popularity. The planning and construction phases of offshore wind farms, including related environmental issues, are discussed here.

Hampson type heat-exchanger technology and economic evaluation for LNG re- gasification and power generation At LNG receiving terminals

The system for high utilization of LNG cold energy is proposed by use of process simulator. The proposed design is a closed loop system, and composed by a Hampson type heat exchanger, turbines, pumps and advanced humid air turbine (AHAT) or Gas turbine combined cycle (GTCC). Its heat sources are Boil-off gas and cooling water for AHAT or GTCC. The higher cold exergy recovery to power can be about 38 to 56% as compared to the existing cold power generation of about 20% with a Rankine cycle of a single component. The advantage of the proposed system is to reduce the number of heat exchangers. Furthermore, the environmental impact is minimized because the proposed design is a closed loop system. A life cycle comparative cost is calculated to demonstrate feasibility of the proposed design. The development of the Hampson type exchangers is expected to meet the key functional requirements and will result in much higher LNG cold exergy recovery and the overall system performance i.e. re-gasification. Additionally, the proposed design is expected to provide flexibility to meet different gas pressure suited for the deregulation of energy system in Japan and higher reliability for an integrated boil-off gas system.

Integration and optimization of pressure retarded osmosis with reverse osmosis for power generation and high efficiency desalination

Salinity gradient power is proposed as a source of renewable energy when two solutions of different salinity are mixed. In particular, Pressure Retarded Osmosis (PRO) coupled with a Reverse Osmosis process (RO) has been previously suggested for power generation, using RO brine as the draw solution. However, integration of PRO with RO may have further value for increasing the extent of water recovery in a desalination process. Consequently, this study was designed to model the impact of various system parameters to better understand how to design and operate practical PRO-RO units. The impact of feed salinity and recovery rate for the RO process on the concentration of draw solution, feed pressure, and membrane area of the PRO process was evaluated. The PRO system was designed to operate at maximum power density of . Model results showed that the PRO power density generated intensified with increasing seawater salinity and RO recovery rate. For an RO process operating at 52% recovery rate and 35 g/L feed salinity, a maximum power density of 24 W/m2 was achieved using 4.5 M NaCl draw solution. When seawater salinity increased to 45 g/L and the RO recovery rate was 46%, the PRO power density increased to 28 W/m2 using 5 M NaCl draw solution. The PRO system was able to increase the recovery rate of the RO by up to 18% depending on seawater salinity and RO recovery rate. This result suggested a potential advantage of coupling PRO process with RO system to increase the recovery rate of the desalination process and reduce brine discharge.

Biomass gasification: Environmentally sound technology for decentralized power generation, a case study from India

This study aims at understanding the need for decentralized power generation systems and to explore the potential, feasibility and environmental implications of biomass gasifier-based electricity generation systems for village electrification. Electricity needs of villages are in the range of 5–20 kW depending on the size of the village. Decentralized power generation systems are desirable for low load village situations as the cost of power transmission lines is reduced and transmission and distribution losses are minimised. A biomass gasifier-based electricity generation system is one of the feasible options; the technology is readily available and has already been field tested. To meet the lighting and stationary power needs of 500,000 villages in India the land required is only 16 Mha compared to over 100 Mha of degraded land available for tree planting. In fact all the 95 Mt of woody biomass required for gasification could be obtained through biomass conservation programmes such as biogas and improved cook stoves. Thus dedication of land for energy plantations may not be required. A shift to a biomass gasifier-based power generation system leads to local benefits such as village self reliance, local employment and skill generation and promotion of in situ plant diversity plus global benefits like no net CO2 emission (as sustainable biomass harvests are possible) and a reduction in CO2 emissions (when used to substitute thermal power and diesel in irrigation pump sets).

Variable-speed wind power generation using doubly fed wound rotor induction machine - A comparison with alternative schemes

In this paper, a wind energy conversion system (WECS) using grid-connected wound rotor induction machine controlled from the rotor side is compared with both fixed speed and variable speed systems using cage rotor induction machine. The comparison is done on the basis of (I) major hardware components required, (II) operating region, and (III) energy output due to a defined wind function using the characteristics of a practical wind turbine. Although a fixed speed system is more simple and reliable, it severely limits the energy output of a wind turbine. In case of variable speed systems, comparison shows that using a wound rotor induction machine of similar rating can significantly enhance energy capture. This comes about due to the ability to operate with rated torque even at supersynchronous speeds; power is then generated out of the rotor as well as the stator. Moreover, with rotor side control, the voltage rating of the power devices and dc bus capacitor bank is reduced. The size of the line side inductor also decreasesd. Results are presented to show the substantial advantages of the doubly fed system.

Power generation using wind energy in northwest Karnataka, India

On the backdrop of climate change scenario, there is emphasis on controlling emission of greenhouse gases such as CO2. Major thrust being seen worldwide as well as in India is for generation of electricity from renewable sources like solar and wind. Chitradurga area of Karnataka is identified as a suitable location for the production of electricity from wind turbines because of high wind-energy resource. The power generated and the performance of 18 wind turbines located in this region are studied based on the actual field data collected over the past seven years. Our study shows a good prospect for expansion of power production using wind turbines.

A GIS (geographical information system)-based spatial data mining approach for optimal location and capacity planning of distributed biomass power generation facilities: A case study of Tumkur district, India

This paper primarily intends to develop a GIS (geographical information system)-based data mining approach for optimally selecting the locations and determining installed capacities for setting up distributed biomass power generation systems in the context of decentralized energy planning for rural regions. The optimal locations within a cluster of villages are obtained by matching the installed capacity needed with the demand for power, minimizing the cost of transportation of biomass from dispersed sources to power generation system, and cost of distribution of electricity from the power generation system to demand centers or villages. The methodology was validated by using it for developing an optimal plan for implementing distributed biomass-based power systems for meeting the rural electricity needs of Tumkur district in India consisting of 2700 villages. The approach uses a k-medoid clustering algorithm to divide the total region into clusters of villages and locate biomass power generation systems at the medoids. The optimal value of k is determined iteratively by running the algorithm for the entire search space for different values of k along with demand-supply matching constraints. The optimal value of the k is chosen such that it minimizes the total cost of system installation, costs of transportation of biomass, and transmission and distribution. A smaller region, consisting of 293 villages was selected to study the sensitivity of the results to varying demand and supply parameters. The results of clustering are represented on a GIS map for the region.