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.

Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Manifestation of Colossal Thermoelectric Power by Charge Ordered Small and Intermediate Bandwidth Manganites

Thermoelectric materials are revisited for various applications including power generation. The direct conversion of temperature differences into electric voltage and vice versa is known as thermoelectric effect. Possible applications of thermoelectric materials are in eco-friendly refrigeration, electric power generation from waste heat, infrared sensors, temperature controlled-seats and portable picnic coolers. Thermoelectric materials are also extensively researched upon as an alternative to compression based refrigeration. This utilizes the principle of Peltier cooling. The performance characteristic of a thermoelectric material, termed as figure of merit (ZT) is a function of several transport coefficients such as electrical conductivity (σ), thermal conductivity (κ) and Seebeck coefficient of the material (S). ZT is expressed asκσTZTS2=, where T is the temperature in degree absolute. A large value of Seebeck coefficient, high electrical conductivity and low thermal conductivity are necessary to realize a high performance thermoelectric material. The best known thermoelectric materials are phonon-glass electron – crystal (PGEC) system where the phonons are scattered within the unit cell by the rattling structure and electrons are scattered less as in crystals to obtain a high electrical conductivity. A survey of literature reveals that correlated semiconductors and Kondo insulators containing rare earth or transition metal ions are found to be potential thermoelectric materials. The structural magnetic and charge transport properties in manganese oxides having the general formula of RE1−xAExMnO3 (RE = rare earth, AE= Ca, Sr, Ba) are solely determined by the mixed valence (3+/4+) state of Mn ions. In strongly correlated electron systems, magnetism and charge transport properties are strongly correlated. Within the area of strongly correlated electron systems the study of manganese oxides, widely known as manganites exhibit unique magneto electric transport properties, is an active area of research.Strongly correlated systems like perovskite manganites, characterized by their narrow localized band and hoping conduction, were found to be good candidates for thermoelectric applications. Manganites represent a highly correlated electron system and exhibit a variety of phenomena such as charge, orbital and magnetic ordering, colossal magneto resistance and Jahn-Teller effect. The strong inter-dependence between the magnetic order parameters and the transport coefficients in manganites has generated much research interest in the thermoelectric properties of manganites. Here, large thermal motion or rattling of rare earth atoms with localized magnetic moments is believed to be responsible for low thermal conductivity of these compounds. The 4f levels in these compounds, lying near the Fermi energy, create large density of states at the Fermi level and hence they are likely to exhibit a fairly large value of Seebeck coefficient.

Teoria de carteiras e a alocação de parques eólicos offshore

The consumption of energy on the planet is currently based on fossil fuels. They are responsible for adverse effects on the environment. Renewables propose solutions for this scenario, but must face issues related to the capacity of the power supply. Wind energy offshore emerging as a promising alternative. The speed and stability are greater winds over oceans, but the variability of these may cause inconvenience to the generation of electric power fluctuations. To reduce this, a combination of wind farms geographically distributed was proposed. The greater the distance between them, the lower the correlation between the wind velocity, increasing the likelihood that together achieve more stable power system with less fluctuations in power generation. The efficient use of production capacity of the wind park however, depends on their distribution in marine environments. The objective of this research was to analyze the optimal allocation of wind farms offshore on the east coast of the U.S. by Modern Portfolio Theory. The Modern Portfolio Theory was used so that the process of building portfolios of wind energy offshore contemplate the particularity of intermittency of wind, through calculations of return and risk of the production of wind farms. The research was conducted with 25.934 observations of energy produced by wind farms 11 hypothetical offshore, from the installation of 01 simulated ocean turbine with a capacity of 5 MW. The data show hourly time resolution and covers the period between January 1, 1998 until December 31, 2002. Through the Matlab R software, six were calculated minimum variance portfolios, each for a period of time distinct. Given the inequality of the variability of wind over time, set up four strategies rebalancing to evaluate the performance of the related portfolios, which enabled us to identify the most beneficial to the stability of the wind energy production offshore. The results showed that the production of wind energy for 1998, 1999, 2000 and 2001 should be considered by the portfolio weights calculated for the same periods, respectively. Energy data for 2002 should use the weights derived from the portfolio calculated in the previous time period. Finally, the production of wind energy in the period 1998-2002 should also be weighted by 1/11. It follows therefore that the portfolios found failed to show reduced levels of variability when compared to the individual production of wind farms hypothetical offshore

Teoria de carteiras e a alocação de parques eólicos offshore

The consumption of energy on the planet is currently based on fossil fuels. They are responsible for adverse effects on the environment. Renewables propose solutions for this scenario, but must face issues related to the capacity of the power supply. Wind energy offshore emerging as a promising alternative. The speed and stability are greater winds over oceans, but the variability of these may cause inconvenience to the generation of electric power fluctuations. To reduce this, a combination of wind farms geographically distributed was proposed. The greater the distance between them, the lower the correlation between the wind velocity, increasing the likelihood that together achieve more stable power system with less fluctuations in power generation. The efficient use of production capacity of the wind park however, depends on their distribution in marine environments. The objective of this research was to analyze the optimal allocation of wind farms offshore on the east coast of the U.S. by Modern Portfolio Theory. The Modern Portfolio Theory was used so that the process of building portfolios of wind energy offshore contemplate the particularity of intermittency of wind, through calculations of return and risk of the production of wind farms. The research was conducted with 25.934 observations of energy produced by wind farms 11 hypothetical offshore, from the installation of 01 simulated ocean turbine with a capacity of 5 MW. The data show hourly time resolution and covers the period between January 1, 1998 until December 31, 2002. Through the Matlab R software, six were calculated minimum variance portfolios, each for a period of time distinct. Given the inequality of the variability of wind over time, set up four strategies rebalancing to evaluate the performance of the related portfolios, which enabled us to identify the most beneficial to the stability of the wind energy production offshore. The results showed that the production of wind energy for 1998, 1999, 2000 and 2001 should be considered by the portfolio weights calculated for the same periods, respectively. Energy data for 2002 should use the weights derived from the portfolio calculated in the previous time period. Finally, the production of wind energy in the period 1998-2002 should also be weighted by 1/11. It follows therefore that the portfolios found failed to show reduced levels of variability when compared to the individual production of wind farms hypothetical offshore

Identifying wind power ramp causes from multivariate datasets: a methodological proposal and its application to reanalysis data.

Forecasting abrupt variations in wind power generation (the so-called ramps) helps achieve large scale wind power integration. One of the main issues to be confronted when addressing wind power ramp forecasting is the way in which relevant information is identified from large datasets to optimally feed forecasting models. To this end, an innovative methodology oriented to systematically relate multivariate datasets to ramp events is presented. The methodology comprises two stages: the identification of relevant features in the data and the assessment of the dependence between these features and ramp occurrence. As a test case, the proposed methodology was employed to explore the relationships between atmospheric dynamics at the global/synoptic scales and ramp events experienced in two wind farms located in Spain. The achieved results suggested different connection degrees between these atmospheric scales and ramp occurrence. For one of the wind farms, it was found that ramp events could be partly explained from regional circulations and zonal pressure gradients. To perform a comprehensive analysis of ramp underlying causes, the proposed methodology could be applied to datasets related to other stages of the wind-topower conversion chain.

Study of low grade temperature CCHP systems for the realization of zero power buildings

Combined Cooling Heat and Power Generation (CCHP) or trigeneration has been considered worldwide as a suitable alternative to traditional energy systems in terms of significant energy saving and environmental conservation. The development and evaluation of a solar driven micro-CCHP system based on a ORC cogenerator and an Adsorption Chiller (AC) experimental prototypes has been the focus of this PhD research. The specific objectives of the overall project are: • To design, construct and evaluate an innovative Adsorption Chiller in order to improve the performances of the AC technology. • To thermodynamically model the proposed micro-scale solar driven CHP system and to prove that the concept of trigeneration through solar energy combined with an organic Rankine turbine cycle (ORC) and an adsorption chiller (AC) is suitable for residential applications.

WEC-Sim Application for the Assessment of Wave Energy Generation in the Tyrrhenian Sea and the North Sea Through the 2-Body Point Absorber (2BPA) and the Oscillating Surge Wave Energy Converter (OSWEC)

In recent years, developed countries have turned their attention to clean and renewable energy, such as wind energy and wave energy that can be converted to electrical power. Companies and academic groups worldwide are investigating several wave energy ideas today. Accordingly, this thesis studies the numerical simulation of the dynamic response of the wave energy converters (WECs) subjected to the ocean waves. This study considers a two-body point absorber (2BPA) and an oscillating surge wave energy converter (OSWEC). The first aim is to mesh the bodies of the earlier mentioned WECs to calculate their hydrostatic properties using axiMesh.m and Mesh.m functions provided by NEMOH. The second aim is to calculate the first-order hydrodynamic coefficients of the WECs using the NEMOH BEM solver and to study the ability of this method to eliminate irregular frequencies. The third is to generate a *.h5 file for 2BPA and OSWEC devices, in which all the hydrodynamic data are included. The BEMIO, a pre-and post-processing tool developed by WEC-Sim, is used in this study to create *.h5 files. The primary and final goal is to run the wave energy converter Simulator (WEC-Sim) to simulate the dynamic responses of WECs studied in this thesis and estimate their power performance at different sites located in the Mediterranean Sea and the North Sea. The hydrodynamic data obtained by the NEMOH BEM solver for the 2BPA and OSWEC devices studied in this thesis is imported to WEC-Sim using BEMIO. Lastly, the power matrices and annual energy production (AEP) of WECs are estimated for different sites located in the Sea of Sicily, Sea of Sardinia, Adriatic Sea, Tyrrhenian Sea, and the North Sea. To this end, the NEMOH and WEC-Sim are still the most practical tools to estimate the power generation of WECs numerically.

On the energy harvesting potential of piezoaeroelastic systems

This paper investigates the concept of piezoaeroelasticity for energy harvesting. The focus is placed on mathematical modeling and experimental validations of the problem of generating electricity at the flutter boundary of a piezoaeroelastic airfoil. An electrical power output of 10.7 mW is delivered to a 100 k load at the linear flutter speed of 9.30 m/s (which is 5.1% larger than the short-circuit flutter speed). The effect of piezoelectric power generation on the linear flutter speed is also discussed and a useful consequence of having nonlinearities in the system is addressed. (C) 2010 American Institute of Physics. [doi:10.1063/1.3427405]

Crowbar System in Doubly Fed Induction Wind Generators

In the last 15 years, the use of doubly fed induction machines in modern variable-speed wind turbines has increased rapidly. This development has been driven by the cost reduction as well as the low-loss generation of Insulated Gate Bipolar Transistors (IGBT). According to new grid code requirements, wind turbines must remain connected to the grid during grid disturbances. Moreover, they must also contribute to voltage support during and after grid faults. The crowbar system is essential to avoid the disconnection of the doubly fed induction wind generators from the network during faults. The insertion of the crowbar in the rotor circuits for a short period of time enables a more efficient terminal voltage control. As a general rule, the activation and the deactivation of the crowbar system is based only on the DC-link voltage level of the back-to-back converters. In this context, the authors discuss the critical rotor speed to analyze the instability of doubly fed induction generators during grid faults.

Performance of the Equivalent Reservoir Modelling Technique for Multi-Reservoir Hydropower Systems

This paper investigates the validity of a simplified equivalent reservoir representation of a multi-reservoir hydroelectric system for modelling its optimal operation for power maximization. This simplification, proposed by Arvanitidis and Rosing (IEEE Trans Power Appar Syst 89(2):319-325, 1970), imputes a potential energy equivalent reservoir with energy inflows and outflows. The hydroelectric system is also modelled for power maximization considering individual reservoir characteristics without simplifications. Both optimization models employed MINOS package for solution of the non-linear programming problems. A comparison between total optimized power generation over the planning horizon by the two methods shows that the equivalent reservoir is capable of producing satisfactory power estimates with less than 6% underestimation. The generation and total reservoir storage trajectories along the planning horizon obtained by equivalent reservoir method, however, presented significant discrepancies as compared to those found in the detailed modelling. This study is motivated by the fact that Brazilian generation system operations are based on the equivalent reservoir method as part of the power dispatch procedures. The potential energy equivalent reservoir is an alternative which eliminates problems with the dimensionality of state variables in a dynamic programming model.

Enhanced aeroelastic energy harvesting by exploiting combined nonlinearities: theory and experiment

Converting aeroelastic vibrations into electricity for low power generation has received growing attention over the past few years. In addition to potential applications for aerospace structures, the goal is to develop alternative and scalable configurations for wind energy harvesting to use in wireless electronic systems. This paper presents modeling and experiments of aeroelastic energy harvesting using piezoelectric transduction with a focus on exploiting combined nonlinearities. An airfoil with plunge and pitch degrees of freedom (DOF) is investigated. Piezoelectric coupling is introduced to the plunge DOF while nonlinearities are introduced through the pitch DOF. A state-space model is presented and employed for the simulations of the piezoaeroelastic generator. A two-state approximation to Theodorsen aerodynamics is used in order to determine the unsteady aerodynamic loads. Three case studies are presented. First the interaction between piezoelectric power generation and linear aeroelastic behavior of a typical section is investigated for a set of resistive loads. Model predictions are compared to experimental data obtained from the wind tunnel tests at the flutter boundary. In the second case study, free play nonlinearity is added to the pitch DOF and it is shown that nonlinear limit-cycle oscillations can be obtained not only above but also below the linear flutter speed. The experimental results are successfully predicted by the model simulations. Finally, the combination of cubic hardening stiffness and free play nonlinearities is considered in the pitch DOF. The nonlinear piezoaeroelastic response is investigated for different values of the nonlinear-to-linear stiffness ratio. The free play nonlinearity reduces the cut-in speed while the hardening stiffness helps in obtaining persistent oscillations of acceptable amplitude over a wider range of airflow speeds. Such nonlinearities can be introduced to aeroelastic energy harvesters (exploiting piezoelectric or other transduction mechanisms) for performance enhancement.