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The most common way of describing the quality of an existing or potential wind or solar power generation site is the total amount of energy expected to be generated based on typical weather patterns. This total amount of energy is characterized by the capacity factor (the ratio between the expected generated amounts to the maximum possible amount for hypothetical conditions of constantly blowing wind or sunshine for a 24 hour period). The capacity factor gives a correct estimate for the useful amount of generated energy when only a small fraction of power comes from wind or solar resources and there are no significant curtailments.
The proposed method provides a consistent measure for renewable power quality. The difference between the overall amount of energy (capacity factor based estimates) and the useful energy (this method) can be as high as a factor of two and will influence the choice of wind or solar generators. The new method of characterizing renewable energy resources will be useful to different users.
NREL scientists have developed software that accomplishes two key items:
- Characterizes detailed weather conditions for a given area over a significant span of time. This develops a broad set of potential wind and solar generation sites in the area with their corresponding individual generation profiles. The weather condition characterization includes historic measurements and detailed weather modeling
- Determines the planned set of generation sites that will be built, maximizing the amount of un-curtailed (useful) energy from each site, and thus minimizing the costs associated with building the generation sites
Individual sites are presented based on the criteria for best load support, the sites number in the tens of thousands across the western U.S., excluding Alaska. The results show that about 80% of the load can be matched with wind and solar, while curtailing less than 10% of the generated energy.
This software could be useful for energy developers, infrastructure development, utility risk management, and others that are interested in site development that includes transmission issues as well as generation potential.
There are numerous options for renewable energy systems development. Location, size, type of system, and a number of other criteria need to be considered. The objective of this program is to determine the optimal size (capacity, Kilowatt (kW)) of a renewable energy generator, based on minimizing life cycle cost (LCC).
The reason for developing such a program is driven by three major issues. First, the need to evaluate a very large number (tens of thousands) of project opportunity sites. Next, the need for data transfer efficiency and computational efficiency in on-line and mobile applications. And lastly, to increase the accuracy of screening studies, which are the earliest indicators of project opportunities.
Many existing programs require the user to enter the size (kW) of the renewable energy system in order to start calculations. However, there are many factors that affect what the size should be including: renewable energy resources, utility rates, initial and maintenance costs, and economic parameters.
NREL scientists have developed an algorithm that addresses the previous three major issues. Lifecycle cost is calculated based on: the sum of initial cost, plus the present value of operations and maintenance costs, costs involved with purchasing power from the utility company, and revenue involved with the sale of excess electricity back to the utility.
The algorithm computes the derivative of an equation for LCC as a function of the size of the renewable energy generator (P), then sets the derivative equal to zero (dLCC/dP=0), and solves for the value of (P) that minimizes life cycle cost. Once the size (P) has been determined, other project details may be calculated. This algorithm develops a closed-form solution for the optimal size of a renewable energy generator. Having a closed-form solution avoids problems of scale and speed that can be encountered with optimization algorithms. Incentives that affect initial cost or provide a payment for energy production are also represented.
Because the algorithm was built in terms of analytics (calculus) and not numeric methods, it can be easily implemented in any platform such as spreadsheets or mobile computing applications. The program is very accurate as long as the simplifying assumptions in the derivation apply: constant load (during daylight hours for PV, all hours for wind); a single rate for all power purchased from the utility; and a single rate for all power sold back to the utility. The program calculates energy saved on-site and energy sold back to the utility using a duration curve based on capacity factor. Data for the calculation is available on-line at www.nrel.gov/gis.
Wind turbine blades must undergo strength and fatigue testing in order to be rated and marketed appropriately. Presently, wind turbine blades are fatigue-tested in the flapwise direction and in the edgewise direction independently. This testing involves placing the blades through 1 to 10 million or more load or fatigue cycles, which may take 3 to 12 months or more to complete for each tested direction. There is a need for blade testing techniques that are less expensive to use and require less time to complete while still providing accurate fatigue testing results. NREL scientists have designed a dual-axis resonant testing apparatus that can perform fatigue-testing in the flapwise and edgewise directions concurrently which cuts down the time and expense necessary to carry out these tests.
The invention, by allowing for performing both tests simultaneously, significantly reduces the time required for blade fatigue testing and reduces associated costs such as the amount of energy while increasing test accuracy and freeing up facilities for additional testing. Additionally, with independent flap and edge fatigue tests, it had typically only been possible to properly fatigue the blade at two locations (e.g., one location for each blade test). However, by combining the flap and edge loading as part of a single fatigue test, this dual-axis resonant testing apparatus allows simulation of realistic loading conditions at multiple locations around the perimeter and span of the blade.
A dual-axis resonant test system is provided for concurrently testing wind turbine blades and other elongate test articles in two directions such as the flapwise and edgewise directions of a blade. The test system includes an oscillating or actuator assembly that is mounted to the blade at a distance from the root, which is semi-rigidly mounted to a test stand or fixture. The cantilevered blade then is fatigue tested by operating the oscillating assembly to impart a forcing function in the flapwise direction and a forcing function in the edgewise direction to cause the blade to concurrently oscillate principally in both the flap and edge directions. Concurrently, the flapwise actuator and the edgewise actuator impose an external force in the flap and edge directions respectively. Further, this can be executed with a controller-provided amplitude at or near the resonant frequency of the blade in both directions simultaneously. The controller may process feedback signals from sensors mounted on the blade to determine loads or bending moments on the blade in the flap and edge directions, store this data, and also provide adaptive displacement signals with amplitudes to the flapwise and edgewise actuators to maintain the applied loads within a desired load or test envelope (e.g., the dual-axis testing may involve a load that varies over time or between cycles but that is controlled to fall within an acceptable envelope). The dual-axis resonant test method can be applied to any length of turbine blade, but, more particularly, an apparatus is provided for fatigue testing elongate test articles including wind turbine blades such as larger blades over 40 meters or the like.
A general concern based on the supply and demand trend of the permanent magnet (PM) raw materials suggests the need for elimination of these materials from electric motors (and generators) to control future costs. This invention discloses a new motor topology that eliminates the PM. Other innovations include brushless adjustable field excitation for high starting torque, field weakening, and power factor improvement and novel locks for higher peak speed. This novel machine shows promising potential to meet the DOE's FY-2020 motor targets for vehicle applications.
The motor consists of a stator punching core with multi-phase stator windings. The rotor is made of a unique lamination core which reduces the surface loss on the rotor. Grooves between poles are used for the insertion of locks or their equivalents that use the vacant space of the grooves for latching purpose. The rotor punching core is assembled to the rotor hub with keys and key ways for torque transmission. Two parallel magnetic fluxes are produced by two toroidal coils located in the stationary excitation cores. At each end of the rotor punching core, an end piece that contacts every other pole transfers the flux from the stationary excitation core to the rotor.
Supported by funding from the U.S. Department of Energy, other federal agencies, and industry sponsors, Argonne is providing broad-based scientific and engineering expertise to create analytical software tools that will enable the United States to make substantive enhancements in energy efficiency and serve the growing demand for renewable energy.
To support DOE’s goal of increasing generation from existing hydropower facilities, Argonne is leading the development of an integrated modeling and simulation toolkit dealing with water forecasting, reservoir and power supply modeling, stream flow routing, and hydropower unit performance metrics. The toolkit will enable the optimization of hydropower operations and environmental performance and improve the ability of plant operators to manage the risk of hydrological uncertainty. Argonne is leading a multi-laboratory project team that includes Oak Ridge, Pacific Northwest, and Sandia National Laboratories.
Wind Power Forecasting and System Integration
Key challenges in meeting DOE’s target of 20% wind power by 2030 are the need for enhanced wind forecasting and better integration of wind plants into power system operation. To meet these challenges, Argonne is developing new unit commitment models that account for wind uncertainty, as well as leading an international team developing improved methodologies for short-term wind forecasting.
Buildings use more energy than any other sector of the U.S. economy, consuming over 70% of electricity and over 50% of natural gas. Argonne’s research team is working to deliver important new technologies to the market that will help reduce energy use and make new and existing buildings more energy efficient.
Researchers at the Los Alamos National Laboratory Intelligent Wind Turbine Program are developing a multi-physics modeling approach for the analysis of wind turbines in the presence of realistic wind loading.
Researchers at the Los Alamos National Laboratory (LANL) Intelligent Wind Turbine Program are developing a multi-physics modeling approach for the analysis of wind turbines in the presence of realistic wind loading. This first-of-a-kind simulation tool will decipher connections between wind events and compromising/damaging load configurations, providing a synthetic environment for preliminary evaluation of changes in operating loads and for testing control algorithms. This work is expected to provide key design and operational guidance for defining turbine design criteria and to enhance wind farm energy production.
This capability is being developed to fill the wind industry’s need for a fast and reliable approach to optimize wind turbine operations in real world conditions. Wind turbines operate with too many variables to explore that may, and probably do, affect wind turbine performance in the short and long term. The potential cost is manifested in many ways, including 1) extensive R&D to understand how to increase performance and mitigate turbine damage in the field and 2) maintenance miscalculations that compromise optimal energy production in multiple operational scenarios due to lack of on-location guidance.
This new technology will integrate multi-scale monitoring of both local and global conditions with a validated, predictive simulation capability, advanced diagnostics, and data extraction and interrogation, with the goal of both creating system state awareness for production enhancement and damage mitigation in the field and accelerating the pace of significant discoveries to optimize current design parameters in multiple field scenarios.
Successful implementation of this multi-scale monitoring will result in a system capable of predicting the behavior of damaged components in wind turbines and its consequences on system performance. Developing a validated, predictive capability to support the design and analysis of intelligent wind turbines will require integration on an unprecedented scale (See HIGRAD/WindBlade and Wind Turbine Structural Health Monitoring). Elements to be integrated include simulations, finite element models, adjoint optimization, resulting data products, and uncertainty inherent in field experiments. The decision-making framework will articulate a strategy to manage these simulations, experiments, and results via a combination of prognostics and exploratory and explanatory visual analysis.
HIGRAD's modeling and simulation software capability captures the dynamic, two-way interaction between rotating turbine blades and the three-dimensional wind flows and turbulence that affect wind turbine and wind farm performance. HIGRAD enables the wind energy industry to optimize site location, wind turbine placement, and turbine/blade development.
As the wind energy industry works to provide the infrastructure necessary to generate 20% of the U.S. national energy supply by 2030, it will encounter many technical issues along the way.
For example, in order to meet national wind energy production goals, the industry is increasing the size of the wind turbines. As blade spans increase, both the turbine design process and turbine site optimization will increase in complexity. This is due to the significant complexity and variability of interactions between turbine blades and atmospheric processes in the Earth’s planetary boundary layer. The variability in wind environments is affected by the surrounding vegetation structure, topography, and turbine array itself as well as transient conditions such as diurnal cycles and evolving weather systems.
Los Alamos National Laboratory is developing unique capabilities, like HIGRAD/Windblade, to address these issues. HIGRAD/Windblade is a physics-based modeling software application designed to simulate the interactions between rotating turbine blades and the complex atmospheric conditions to which they are exposed including heterogeneous wind shears, density currents, streamwise vortices, transitions in atmospheric stability, and turbine wakes. The results from these simulations, including transient blade loadings and turbulence generation, are critical for designing wind turbines, predicting wind turbine performance, finding wind farm locations, planning wind turbine arrays, and assessing the environmental effects of wind turbine arrays.
HIGRAD's modeling and simulation software capability captures the blade-scale (meters), dynamic, two-way interaction between rotating turbine blades and the three-dimensional atmospheric structures over turbine-scale (hundreds of meters ) to wind-farm-scale domains (multiple kilometers). HIGRAD also calculates high-gradient wind flows and turbulence that affect wind turbine and wind farm performance. As such, HIGRAD will deliver valuable capabilities to the wind energy industry by enabling the optimization of site location, wind turbine placement, and turbine/blade development.
A unique LANL research team composed of world experts in structural health monitoring, modeling and simulation, and prognostic decision making has established a strong capability in wind energy research. The intelligent wind-turbine project has resulted in a U.S. patent application and copyrighted software, with other intellectual property in the disclosure stage.
As the wind energy industry works to provide the infrastructure necessary for wind turbine reliability, expectations for performance and design criteria continue to escalate as the world community develops a means to augment power production with wind-derived energy. Turbines have become massive and are likely to grow as technology advances, further straining the limits of current design standards. As a result, modern wind turbines, having a design lifespan of 20 years, typically fail 2.6 times per year during the first 10 years and average 3.9 unplanned maintenance incidents per year. The industry struggles to understand the causes of these premature failures and is currently unable to predict, detect the onset, or manage growth of defects.
Wind-turbine reliability issues, e.g., defect prediction/containment, design optimization, and performance enhancement in the field are believed to result from poorly understood turbulent and unsteady wind factors. Specifically, LANL researchers are concerned about complex and dynamic blade loading resulting from the interaction of the blade with unbalanced wind forces under conditions of strong shear. These potentially damaging loads are in turn transmitted to the turbine hub and gearbox, eventually leading to compromised rotor integrity and failure. Such dynamic, turbulent wind interactions need to be measured, modeled, anticipated, and managed in order to bring down the costs of wind turbine power-producing operations. Therefore, it is paramount that a capability be developed that monitors and understands how blades interact with atmospheric wind conditions as the blades are the physical mechanisms by which loads are transmitted to the turbine hub, gearbox, and generator.
Currently, there are no methods for modeling the interaction between landscape wind events and spinning wind turbines. There are no tools for modeling two-way interactions between realistic wind fields, deforming blades, and the resulting stresses imparted on the blades and hub. In addition, there has been minimal research in developing techniques for real-time monitoring and control of turbines under realistic wind-loading conditions.
LANL researchers are using extensive Laboratory resources by integrating experiments, simulations and modeling capabilities with the ultimate goal of providing solutions to these pressing issues. In particular, LANL’s expertise in the areas of predictive modeling and simulation (see HIGRAD/WindBlade), advanced sensing technologies (see Wind Turbine Structural Health Monitoring), and prognostic decision making linked to active performance enhancement and damage control (see Wind Turbine System State Awareness) will be leveraged to create intelligent models for predictive control of wind-farm operations.
Power converters are used to convert alternating current (AC) electric power from a fixed-frequency and fixed-voltage to different frequencies and different voltages for powering loads, such as electric motors. They are also used in reverse to convert variable-frequency, variable-voltage, AC electric power to fixed-frequency, fixed-voltage electric power. Electric generators driven by wind turbines rotate at different speeds, depending on wind conditions, so they produce variable-frequency, variable-voltage, AC electric power. Therefore, power converters are used to convert such wind-generated electric power to fixed-frequency, fixed-voltage, AC power to match public utility and similar AC power systems. However, wind turbine power systems also spend lots of time operating at light loads or fractions of their rated power capacities, whereas standard, state-of-the-art, power converters are designed to operate most efficiently at full-rated power all, or nearly all, of the time. Further, standard power converters do not work at low voltages. Therefore, when wind turbine-driven generators are operating in low wind, light load, conditions, standard power converters are inefficient and may not work at all.
Engineers at the National Renewable Energy Laboratory (NREL) have developed a system that improves existing variable-speed, wind power technology by achieving high efficiency at low wind speeds and capturing more wind energy. This is done by providing improvements in AC power converter technologies to enable more efficient and reliable conversion of variable-frequency, variable-voltage, AC power to fixed-frequency, fixed-voltage, AC power and vice versa. The system also provides more efficient and reliable power converters for wind power generator applications in which generated AC power varies over wide ranges of frequencies, voltages, and load levels, for converting such AC power to fixed-frequency, fixed-voltage, AC power for public utility and similar AC power systems.
Recently, there has been a rapidly growing demand for renewable energy, including wind energy. To meet this demand, wind turbine designers are working to provide blade designs that allow a turbine connected to the wind turbine blades or to the rotor to effectively convert wind into electricity. The blades must also be designed properly to withstand inertial forces, aerodynamic forces, and structural forces so as to provide a relatively long service life and safe operation. Like all rotating machines, wind turbines are generators of fatigue, and every revolution of its components including the turbine blades produces a load or fatigue cycle, with each of these cycles causing a small, finite amount of damage that eventually may lead to fatigue cracks or other failures.
Modeling may be used in some cases to determine service life of a turbine blade during normal operations. However, modeling has its limitations including variations in the as-built/manufacture blade design and the difficulty in accurately modeling operational conditions with varying and sometimes random loading. As a result, wind turbine blades are typically laboratory tested to determine that their fatigue strength or characteristics are adequate for a desired service life. Wind turbine or rotor blade testing is used to verify that laminations in the blade are safe, e.g., the layers used to fabricate a blade do not separate/delaminate and to verify that the blade will not break under repeated stress.
Presently, wind turbine blades are fatigue tested in the flapwise direction (i.e., out of the rotor plane or in the direction transverse to a plane extending through the blade) and in the edgewise direction (i.e., in the plane of rotation or in a direction parallel to a plane extending through the blade). For large blades (greater than forty-meter blade lengths), these two fatigue tests are typically run sequentially, and, to simulate a typical service life of a blade, each test may involve placing a blade through one million to ten million or more load or fatigue cycles, which may take three to twelve months or more to complete for each tested direction. There is a trend for wind generator systems to become increasingly larger. Unfortunately, however, the larger blades associated with larger wind generator systems are subjected to greater static and dynamic loads and the facilities for testing these larger blades are also very large as newer generation turbine generators are being designed with blades 40 meters or more in length. It is very desirable, and often necessary, to advance test a proposed blade design to ensure that it will be capable of withstanding the expected loads with structural failure and to evaluate the fatigue resistance or the blade design, and these advanced tests may significantly delay implementation of a new blade design. The test equipment can also be relatively expensive to purchase and operate, which can drive up the costs of blades and wind energy. Hence, there is a need for blade testing techniques that are less expensive to use and take less time to complete while still providing accurate fatigue testing results.
Engineers at the National Renewable Energy Laboratory (NREL) have developed a system which uses a motor to resonate wind turbine blades by oscillating the system at the root. One advantage of this system is that it removes the need to have specialized hydraulic equipment such as pumps, hoses and actuators. This is done using a motor to create oscillatory motion and resonate a blade in either the flapwise or edgewise direction. The motor and flywheel system rotate a link, which is attached to a frame with a prismatic joint. The frame’s vertical deflection causes the system frame to rotate about a revolute joint mounted to the ground. The blade is mounted to the frame and oscillates with the frame. In contrast to traditional test stands, the frame is mobile because it is self-supporting and requires little anchoring to the ground. A variation of the concept shown below would be to cantilever a weight from the blade stand to reduce the loads at the follower carriage.
The supporting structure includes a tunable spring element for applied excitation forces, the tunable spring enabling dual axis testing by modification to the spring constant in different directions.
These technologies are designs and methods that boost the efficiency of electric generators by decoupling the magnetic polarity of the driving mechanism while increasing the operational frequency of the machine. Both are unique, low cost methods to develop a generator with a higher power density.
Commercial applications include stationary, rotational or linear generator sets. The technologies can increase efficiency output at all application levels. Both technologies can be used anywhere a lower cost, higher power density generator is needed.
Researchers at ORNL have developed a method of modifying existing coating techniques to include a bonded superhydrophobic outer coating layer. Superhydrophobic powder will not readily bond to most substrates directly, since superhydrophobic powder is almost entirely made up of fluorinated particles of silica glass, which is chemically inert to most materials. In a standard electrostatic powder spraying process, dry resin powder is sprayed on to a given substrate. The powder adheres to the substrate by electrostatic forces and becomes permanently bonded to the substrate after the resin powder is heated and/or cured.
The developed method is an improvement over standard methods. The novel method could be used to make large superhydrophobic surface areas on a wide variety of substrate materials. The resulting coating can completely repel water and heavy oils leading to potential applications in a number of areas including, but not limited to superhydrophobic, anti-corrosion, anti-icing, and antibacterial coatings.