Analysis of Two-Speed Wind Farm Operation From Grid-Side Measurements

This paper analyzes data captured by a phasor measurement unit at a wind farm, employing two-speed induction generators, and investigates aspects of the control system's interaction with the power system. Composite superimposed transient events are proposed as a method to improve the quality of the analysis and reduce errors caused by unknowns, such as wind speed variation. A Mathworks SimPowerSystems model validates the inertia contribution of the wind farm, which is an important parameter in power systems with high wind penetration. Transients caused by turbine speed transitions are identified and explained. The analysis also highlights areas where wind farm control should be improved if useful inertia contribution is to be provided.

A biofuel strategy for Ireland with an emphasis on production of biomethane and minimization of land-take

Increasing energy consumption has exerted great pressure on natural resources; this has led to a move towards sustainable energy resources to improve security of supply and to reduce greenhouse gas emissions. However, the rush to the cure may have been made in haste. Biofuels in particular, have a bad press both in terms of competition with good agricultural land for food, and also in terms of the associated energy balance with the whole life cycle analysis of the biofuel system. The emphasis is now very much on sustainable biofuel production; biofuels from wastes and lignocellulosic material are now seen as good sustainable biofuels that affect significantly better greenhouse gas balances as compared with first generation biofuels. Ireland has a significant resource of organic waste that could be a potential source of energy through anaerobic digestion. Ireland has 8% of the cattle population of the EU with less than 1% of the human population; as a result 91% of agricultural land in Ireland is under grass. Residues such as slurries and slaughter waste together with energy crops such as grass have an excellent potential to produce biogas that may be upgraded to biomethane. This biomethane may be used as a natural gas substitute; bio-compressed natural gas may then be an avenue for a biofuel strategy. It is estimated that a maximum potential of 33% of natural gas may be substituted by 2020 with a practical obtainable level of 7.5% estimated. Together with biodiesel from residues the practical obtainable level of this strategy may effect greater than a 5% substitution by energy of transport. The residues considered in this strategy to produce biofuel (excluding grass) have the potential to save 93,000 ha of agricultural land (23% of Irish arable land) when compared to a rapeseed biodiesel strategy. © 2009 Elsevier Ltd. All rights reserved.

Development of a heterogeneous catalyst for lignocellulosic biomass conversion:Glucose dehydration by metal chlorides in a silica-supported ionic liquid layer

An attempt is made to immobilize the homogeneous metal chloride/EMIMCl catalyst for glucose dehydration to 5-hydroxymethylfurfural. To this end, ionic liquid fragments were grafted to the surface of SBA-15 to generate a heterogenized mimick of the homogeneous reaction medium. Despite a decrease in the surface area, the ordered mesoporous structure of SBA-15 was largely retained. Metal chlorides dispersed in such ionic liquid film are able to convert glucose to HMF with much higher yields as is possible in the aqueous phase. The reactivity order CrCl > AlCl > CuCl > FeCl is similar to the order in the ionic liquid solvent, yet the selectivity are lower. The HMF yield of the most promising CrCl-Im-SBA-15 can be improved by using a HO:DMSO mixture as the reaction medium and a 2-butanol/MIBK extraction layer. Different attempts to decrease metal chloride leaching by using different solvents are described. © 2013 American Institute of Chemical Engineers Environ Prog.

Efficiency improvement study for small wind turbines through flow control

In this study, a constant suction technique for controlling boundary layer separation at low Reynolds numbers was designed and tested. This was later implemented on small wind turbines. Small wind turbines need to operate in low wind speeds, that is, in low Reynolds number regimes – typically in the range 104–105. Airfoils are prone to boundary layer separation in these conditions, leading to a substantial drop in aerodynamic performance of the blades. Under these conditions turbines will have reduced energy output. This paper presents experimental results of applying surface-suction over the suction-surface of airfoils for controlling boundary layer separation. The Reynolds numbers for the experiments are kept in the range 8×104–5×105. The air over the surface of the airfoil is drawn into the airfoil through a slit. It is found that the lift coefficient of the airfoils increases and the drag reduces. Based on the improved airfoil characteristics, an analysis of increase in Coefficient of Power (CP), versus input power for a small wind turbine blade with constant suction is presented.

Partial least squares model for dynamic rating of overhead line in wind intensive areas based on field measurements

This paper presents a statistical model for the thermal behaviour of the line model based on lab tests and field measurements. This model is based on Partial Least Squares (PLS) multi regression and is used for the Dynamic Line Rating (DLR) in a wind intensive area. DLR provides extra capacity to the line, over the traditional seasonal static rating, which makes it possible to defer the need for reinforcement the existing network or building new lines. The proposed PLS model has a number of appealing features; the model is linear, so it is straightforward to use for predicting the line rating for future periods using the available weather forecast. Unlike the available physical models, the proposed model does not require any physical parameters of the line, which avoids the inaccuracies resulting from the errors and/or variations in these parameters. The developed model is compared with physical model, the Cigre model, and has shown very good accuracy in predicting the conductor temperature as well as in determining the line rating for future time periods. 

Predictive Maintenance for Improved Sustainability - An Ion Beam Etch Endpoint Detection System Use Case

In modern semiconductor manufacturing facilities maintenance strategies are increasingly shifting from traditional preventive maintenance (PM) based approaches to more efficient and sustainable predictive maintenance (PdM) approaches. This paper describes the development of such an online PdM module for the endpoint detection system of an ion beam etch tool in semiconductor manufacturing. The developed system uses optical emission spectroscopy (OES) data from the endpoint detection system to estimate the RUL of lenses, a key detector component that degrades over time. Simulation studies for historical data for the use case demonstrate the effectiveness of the proposed PdM solution and the potential for improved sustainability that it affords.

A review of biomass co-firing in North America

Biomass fuels have long been accepted as useful renewable energy sources, especially in mitigating greenhouse gases (GHG), nitrogen oxides, and sulfur oxide emissions. Biomass fuel is carbon neutral and is usually low in both nitrogen and sulfur. For the past decade, various forms of biomass fuels have been co-combusted in existing coal-fired boilers and gas-fired power plants. Biomass is used as a supplemental fuel to substitute for up to 10% of the base fuel in most full commercial operations. There are several successful co-firing projects in many parts of the world, particularly in Europe and North America. However, despite remarkable commercial success in Europe, most of the biomass co-firing in North America is limited to demonstration levels. This review takes a detailed look at several aspects of biomass co-firing with a direct focus on North America. It also explores the benefits, such as the reduction of GHG emissions and its implications. This paper shows the results of our studies of the biomass resources available in North America that can be used in coal-fired boilers, their availability and transportation to the power plant, available co-firing levels and technologies, and various technological and environmental issues associated with biomass co-firing. Finally, the paper proffers solutions to help utility companies explore biomass co-firing as a transitional option towards a completely carbon-free power sector in North America.

The Application of Computational Chemistry and Chemometrics to Developing a Method for Online Monitoring of Polymer Degradation in the Manufacture of Bioresorbable Medical Implants

Bioresorbable polymers such as PLA have an important role to play in the development of temporary implantable medical devices with significant benefits over traditional therapies. However, development of new devices is hindered by high manufacturing costs associated with difficulties in processing the material. A major problem is the lack of insight on material degradation during processing. In this work, a method of quantifying degradation of PLA using IR spectroscopy coupled with computational chemistry and chemometric modeling is examined. It is shown that the method can predict the quantity of degradation products in solid-state samples with reasonably good accuracy, indicating the potential to adapt the method to developing an on-line sensor for monitoring PLA degradation in real-time during processing.

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ‘moving window’ technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.