TECHNIQUE TO MITIGATE STORMS USING ARRAYS OF WIND TURBINES

This invention describes a system whose operation can change the track and intensity of atmospheric storms. The invention uses arrays of wind turbines which are being built for power generation. Using existing atmospheric and storm tracking models, calculations of a storm track may be determined to establish a baseline track calculation. The storm track may then be calculated for a number of permutations where various groups or individual wind turbines are curtailed (e.g., feathered). The optimal storm track may be determined based on damage estimate calculations. Signals may be sent to individual or groups of wind turbines to curtail the turbines to alter the track of the tropical storm.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1is a diagram illustrating an optimized wind and solar network capable of supplying about 70% of US power. The dark areas in the plains represent blocks of about 170 square kilometers, with the total wind turbine power color coded. A typical block colored deep blue would have about 150 3 Megawatt wind turbines. Curtailment of a block of wind turbines would add about 450 megawatts of wind energy to the column above the turbines. Total power generation capability of the plains network shown is about 600 Gigawatts.

FIG. 2is a diagram illustrating three shaded boxes as areas perturbed in the Large Area Perturbation Technique. Each area can be completely curtailed, or not, giving rise to eight different possible modes of curtailment. These are then used with predictive models to determine the downstream effect of the perturbations. The curtailment is chosen for maximum desirable effect on the storm.

FIG. 3is a diagram illustrating the estimated modification of Hurricane Irene track with 300 Gigawatts of curtailment in the western plains. The wind and pressure perturbation in this simple case is assumed amplify with a doubling period of 36 hours, moving downstream from the wind turbine array to push the storm further east away from the US coast.

FIG. 4is a block diagram illustrating the main components of the present invention. The following illustration of the STRICT algorithm is described in connection with a specific case to enhance understanding of how it works. In this case, a network of wind turbines406A-DC is used, designed for an optimal wind and solar energy system in the US 48 states is illustrated inFIG. 1. For the purposes of illustration, four wind turbines406A-D are shown schematically. These represent a network or networks of wind turbines, as described in connection withFIG. 1. The efficacy of the present invention is proportional to the number of installed wind turbines.

The example of Hurricane Irene is applied to illustrate the present invention. Hurricane Irene existed from August 20 to its decay in early September, impacting the US east coast between August 25 and August 29. An important aspect of the STRICT Algorithm is that the storm was predicted by global models as much as three days before it formed.

Modern weather prediction models402are capable of making predictions403of future atmospheric states which, over a period of days, gradually depart more and more from the observed states. A global weather prediction model402may receive inputs from various weather sensor data401including but not limited to satellite imagery data, temperature and wind data, atmospheric pressure data, ocean temperature data, and the like. The use of model402ensembles allow multiple predictions403with perturbed initial states to represent an envelope of possible states. As an example, a hurricane located in the Caribbean could take a number of different tracks. Multiple model runs can show many tracks which generally “cluster” around the most likely track if the model has skill in both its predictions and perturbation growth. The STRICT404algorithm uses an ensemble of model predictions403as an important part of its technical basis.

The STRICT algorithm404uses the global weather prediction model402to establish a baseline for weather prediction. For example, global weather prediction model402may produce storm track prediction403based on normal model parameters. The STRICT algorithm may then compare this baseline model to altered models, where various wind turbines406A-D are disabled or deactivated (curtailed), and the resulting affect on the storm track re-calculated and compared to the original baseline model. Network control system405may send signals to electronic controllers to automatically shut down (feather) or otherwise disable or curtail turbines or groups of turbines. Alternately, or in addition, network control system405may send signals via the Internet to various utility and wind turbine operators, instructing them to curtail a number of turbines as calculated by the STRICT algorithm.

FIG. 5is a flowchart illustrating the steps used in the method of the present invention. As illustrated inFIG. 5, in step510, weather sensor data is acquired, as discussed previously in connection withFIG. 4. In step520, a global weather prediction model is run to generate a predicted storm track530. Again, the global weather models and storm track generating techniques are known in the art, and thus need not be described here in detail. In step540a determination is made whether the projected storm track would endanger the US coastline or other area of interest. If the storm is projected to remain offshore, no further action may be taken, and processing returns to step510.

The system uses many model integrations to arrive at a best estimate of the spatial and temporal sequence of wind turbine curtailment. The curtailments are given in 12 hour windows, based on model integrations that are run every 12 hours. Thus, a premium is on the rapidity of calculation to determine the sequence as soon after model initialization time as possible. As an example, start with an initial time of 00 UT. If all the assimilation and model runs could be completed in six hours, the curtailment window would be from 6 to 18 hours after the initial time.

Using advanced assimilation, it is assumed that an initialization system has arrived at an optimal estimate of an ensemble of initial states. To distinguish the ensemble of initial states from the ensemble of multiple predictions from each of the initial states, we refer to the former as the “Ensemble of Initial States” (EIS). The prediction models530are integrated forward for an extended period, typically 7 to 10 days.

Two complementary techniques are used in the calculation. The first is the Large Area Perturbation Technique, which will be described here. The second is the Detailed 4 D Variational Technique (D4DVT), which is described below. The techniques are complimentary because the LAPT can be used to determine the larger scale characteristics of the storm modification method, while the detailed, turbine by turbine specification can come from the D4DVT. LAPT starts with a small number of geographic areas. For illustration, we use three areas, as shown inFIG. 2. The arrangement of the areas would in general be based on experience and testing, but in this case we have chosen a north south division. The three areas are treated as blocks in this simple case; it is prescribed that each of the areas will uniformly execute the same temporal sequence of curtailments. Of course, any number of areal subdivisions could be used, based on experience, testing and computational resources available.

The LAPT555uses multiple model integrations for each of an Ensemble of Initial States. The first model integration for each member is the “ensemble member control”545. This is a run that does not have any wind turbine curtailment, as illustrated in steps510-530inFIG. 5. These steps establish the baseline storm track without curtailment. Then, an additional set of model runs are conducted for each EIS in steps550-580. In step550, a turbine area is selected for curtailment. In step560, the global weather prediction model is run and a predicted storm track is generated in step570. In step580, the new storm track is compared to the baseline storm track from block545. Once the storm track has been improved or optimized, the process is complete. If not, the process repeats for every combination of turbine curtailment, as illustrated in LAPT block555.

The number of prediction runs for each initial state (each EIS) could be chosen as all perturbations of the initial areas, with two allowable states—fully curtailed or un-curtailed. Other more complex perturbation methods may also be used, such as partial curtailment of turbines, or the like. In the simple case, three different areas with binary states result in eight possible states, or seven, plus the control run. The eight prediction runs are accomplished for each of the EIS, so the total number of prediction runs is EIS*NR. Thus if there are 20 initial states (EIS=20), then there would have to be 20*8=160 model prediction runs.

The second stage of the LAPT is to select a strategy of curtailment based on the multiple ensemble runs. For example, a simple strategy (which may not be optimal after testing) is to take the ensemble of each perturbation to determine which perturbation moves the prediction most favorably in the desired direction for track and intensity change, as illustrated in block580. The model prediction runs should also include a number of future scenarios of turbine curtailment to enhance the choice of the best current curtailment. There may be some human judgment involved with this step because of the trade-off between track and intensity. The various permutations (raw or edited by humans) may then be input to the D4DVT as illustrated in step590.

The Detailed 4 Dimensional Variational Technique (D4DVT) uses the methodology of four dimensional variational assimilation to determine the detailed schedule of curtailment for each wind turbine. The discipline of four dimensional assimilation (henceforth referred to as 4DVAR) has a 30 year history in atmospheric applications and is used by many global weather prediction centers. In this case the 4DVAR technique is adapted to search for optimal wind turbine curtailment rather than optimal atmospheric analysis. By using the perturbation technique described above, the speed and likelihood of convergence of this algorithm are increased.

The D4DVT seeks to minimize future damage from a tropical storm. Thus it depends on definition of a “damage function”, that must relate the total damage from the storm from the first time that can be affected (e.g. 6 hours from the initial time) to the end of the model integration. The damage functions600can be a varying complexity, including wind damage functions and flooding functions based on precipitation. A very simple example could be to use population density and the cube of the surface wind as proportional to the destructive power. The total damage could be approximated by the population of each county affected times the maximum of the wind damage function. A computational detail is that the area around the tropical storm must be masked such that only the winds from the tropical storm enter into the minimization; this is similar to the spatial masks used in conventional 4DVAR, except that it should not be tapered with distance. Software that tracks the center of tropical storms in prediction models would typically be used as the center for an influence radius of a couple hundred kilometers.

From the damage functions600, the system may then select the best curtailment profile in step610from the permutations calculated in the LAPT block555. The corresponding curtailment signals may then be sent in block620to curtail individual or groups of turbines in step630. Curtailment may be achieved by shutting down (feathering) individual or groups of turbines using commands sent through a network to control systems operating the turbines. Alternately, these signals may be send as communications (e.g., e-mail, or the like) to utility companies and turbine operators, who in turn may enter commands into their systems to feather individual or groups of turbines.

The control variable is the curtailment function of the wind turbines. The curtailment function varies from 0 to 1, where 0 indicates the wind turbine is completely feathered, and 1 means it is operating at full power allowed. Simplifications that can be used, such as blocking the wind turbines e.g., by having all of the wind turbines in a model grid element act in unison. Further, although the wind turbine curtailment function can enter the functional through a complex algorithm, it can be simplified by converting the curtailment into a momentum modification of the wind in the layers that encompass the turbine blades. In any case, the conversion of the momentum modification into its effect on the prediction model is crucial to the accuracy of the technique.

Following conventional 4DVAR approaches (Lewis et al, 2006) a function is defined with the damage variables to be minimized, and a prediction model as a strong constraint. The model is integrated forward, and then backward to obtain the gradient of the function with respect to the control variables—the curtailment of each of the turbines. The gradient of the cost function is then used with a search algorithm, such as the steepest descent or conjugate gradient, to determine the minimum damage configuration. This is used to specify the curtailment of the wind turbines during the 6 to 18 hour period after the initial time. The period of model prediction can be chosen to end arbitrarily. Typically, for efficiency, it would end when the damage potential of the storm goes below a threshold. In the Hurricane Irene case it could be after the storm has dissipated far out in the North Atlantic.

The STRICT algorithm described above will not work in every case. It works best if the storm to be modified is downstream, and if the meteorological conditions strong amplify the initial perturbation. In a simple example, using data from Hurricane Irene, assume that the wind turbine network perturbation is 300 Gigawatts. Thus, the boundary layer gains 300 Gigawatts for a 12 hour period, which is 1.3*1016 joules of energy. This perturbation propagates down-stream and upward into the mid-troposphere at approximately the speed of the mid tropospheric wind flow, e.g. 20 m/s. At this speed, it takes 72 hours to reach the area of the hurricane. Assume an amplification doubling time of 36 hours (many perturbations amplify much more rapidly); by the time the perturbation reaches the western Atlantic, its total energy is 5*1016 joules. Scaling the perturbation by a reasonable mid-latitude resonant Rossby radius for August gives a radius of about 800 km, corresponding to the horizontal extent of the perturbation; at the speed of 20 m/s, the distance covered in 12 hours is 864 km. It is assumed that the energy of the system is strongest in the mid troposphere, and is partitioned between kinetic and potential energy. Two highly damaging Hurricanes in the last couple of decades that had very sensitive periods in their approach to the US mainland were Hurricane Andrew in 1992, and Hurricane Sandy in 2012. Both of these storms would be excellent candidates for the STRICT—testing will be conducted to determine how the technique would work in these and other cases.

Distributing energy in the vertical according to height in the atmosphere gives a mid-level momentum change of about 1 m/s at the “steering level” (Abserson, 2001) of about 700 mb, similar to the perturbations found over the western Atlantic described by Barrie and Kirk-Davidoff (2010). As illustrated inFIG. 3, the track of Hurricane Irene can be modified by assuming that it is pushed further eastward at approximately 1 m/s as it traverses the mid-latitude region downstream from the wind network shown inFIG. 1. The storm crossed 25 N at 15 GMT on August 25 and reached 50 N at 15 GMT on August 29. The total displacement for the period of 4 days at 1 m/s would be about 345 km (216 miles). This example is illustrated by the contrasting tracks ofFIG. 3. The light shaded track shows the actual storm center, which affected the US from the Carolinas to Maine. The dark track shows where the storm would have gone based on the discussion above. It can be seen that by moving the storm over 200 miles to the east, the damage to the US east coast would have been significantly mitigated.

Several aspects of this calculation should be mentioned. The “moving” of the storm discussed depends on relatively high differential energy production from the wind turbine array, and a fairly strong amplification. In reality, each 12 hours would be an opportunity that the STRICT algorithm would reveal. During some periods, the winds would be weaker, so the initial perturbation might only be 150 Gigawatts. Similarly, barotropic (getting energy from the pre-existing jet stream) and baroclinic (getting energy from temperature contrasts) instability can result in positive or negative amplification. The STRICT algorithm shows the envelope of likely results of a given wind turbine curtailment strategy, allowing policymakers to determine if the effect on storms is desired. Since there is an opportunity every 12 hours, which would be 10 opportunities for a storm that lasts 5 days (about the mean lifetime), there are many chances for the system to provide helpful modifications.

Another important aspect of the STRICT technique would be the use of linear programming to estimate the least damaging paths. This technique uses the geographic distribution of the built environment and population locations to determine the best path to steer a storm when there is diversity in the potential motion. It complements the technique with a way to minimize damage.

In a country with large wind turbine arrays, the invention described herein may occasionally give policymakers the opportunity to influence weather. There is no question based on the physical formulation described in this disclosure that the system will work.

Note that while described herein in terms of altering storm tracks, in particular tropical storms and hurricanes, the present invention may be used to alter weather in general. For example, global weather prediction models may be used in conjunction with the STRICT algorithm to determine the influence of turbines on drought, rainfall, cloud cover, high winds, and other weather patterns. Turbines may be selectively curtailed in order to enhance weather conditions based on the iterative calculations of the STRICT algorithm.

While the preferred embodiment and various alternative embodiments of the invention have been disclosed and described in detail herein, it may be apparent to those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope thereof.