Abstract:
System, method, and computer program product for optimally scheduling energy storage devices in wind energy applications. The power production system includes an energy storage device configured to service a first energy storage application at the first wind farm and a supervisory controller configured to determine if an attribute of the energy storage device is less than or equal to a threshold for the first energy storage application. In response to the attribute being less than or equal to a threshold for the first energy storage application, the supervisory controller schedules the energy storage device for a second energy storage application at the first wind farm or at a second wind farm different than the first wind farm. The optimization of the scheduling may be implemented in a computer-implemented method or as a computer program product.

Description:
BACKGROUND 
     This application relates generally to electrical power generation and, more specifically, to the optimization of energy storage device usage in wind energy applications. 
     A utility-scale wind energy system or wind farm includes a group of wind turbines that operate collectively as a power plant to produce electrical energy without the consumption of fossil fuels. The output of wind energy from a wind farm is less consistent than the energy output from fossil fuel-fired power plants. As a result, the power from wind turbines operating at nominal conditions in a wind farm may not meet output requirements for the power plant. For example, the power from a power plant may not track the power forecast due to forecast errors. As another example, the rate of power production for a power plant may be outside of a desired range because of wind gusts. A conventional approach for dealing with these and other similar situations is to use controls to manage the operation of the wind farm, such as utilizing pitch control of the rotor blades to increase or decrease the power produced by the individual wind turbines. 
     Traditional utility-scale wind energy systems are not dispatchable sources of electricity that can be turned on or off at the request of power grid operators. For that reason, a wind farm may include an energy storage device, such as one or more rechargeable batteries, that is linked to the power grid and that may assist with meeting requirements on the power production by the power plant. Energy storage systems can be used to shift power production by a wind farm from off-peak times to peak load times. Energy storage systems can store curtailed production for later release to the power grid. The ability to store energy during times of high wind turbine production and release the stored energy during times of low wind production also allows a wind farm to improve power production forecast accuracy. The accuracy improvements allow wind farms to meet firm capacity commitments to power companies and to avoid expensive penalties. 
     The pattern of charge and discharge cycles for intermittent generators, such as wind turbines, may be irregular dependent upon the application or combination of applications served by the wind farm. Nevertheless, a battery experiencing on average a single daily charge and discharge for twenty years in a wind farm accumulates roughly 7,300 cycles. As a result, candidate batteries of wind farms must be characterized by long cycle lifetimes. Battery life is dependent on both the depth of discharge and the rate of discharge, as well as other external factors such as temperature, charging strategy, etc. 
     Accordingly, the management of energy storage systems must be improved to optimize the use of energy storage systems, such as batteries, in wind energy applications. 
     BRIEF SUMMARY OF THE INVENTION 
     In one embodiment of the invention, a power production system includes a wind farm comprising a plurality of wind turbines, an energy storage device configured to service a first energy storage application at the first wind farm, and a supervisory controller configured to determine if an attribute of the energy storage device is less than or equal to a threshold for the first energy storage application and to schedule the energy storage device for a second energy storage application at the first wind farm or at a second wind farm different than the first wind farm. 
     In another embodiment of the invention, a computer-implemented method is provided for optimizing energy storage device scheduling in wind energy applications. The method includes comparing an attribute of an energy storage device at a first wind farm with a threshold for a first energy storage application served by the energy storage device at the first wind farm. In response to the attribute being less than or equal to the threshold, the energy storage device is scheduled for a second energy storage application at the first wind farm or at a second wind farm different than the first wind farm. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various embodiments of the invention and, together with a general description of the invention given above and the detailed description of the embodiments given below, serve to explain the embodiments of the invention. 
         FIG. 1  is a perspective view of a wind turbine typical of that used in a wind farm. 
         FIG. 2  is a perspective view of a portion of the wind turbine of  FIG. 1  in which the nacelle is partially broken away to expose structures housed inside the nacelle. 
         FIG. 3  is a diagrammatic view of wind farms with batteries that supplement the operation of the wind turbines in the wind farms. 
         FIG. 4  is a graphical view showing the degradation of battery capacity over time for the batteries in the wind farms of  FIG. 3  and the reallocation of the batteries to optimize the scheduling of the batteries across multiple wind farms in accordance with an embodiment of the invention. 
         FIG. 5  is a diagrammatic view similar to  FIG. 3  in which one of the batteries has been moved to a different wind farm based upon capacity degradation in  FIG. 4  and replaced with a different battery based upon optimization in accordance with an embodiment of the invention. 
         FIG. 6  is a flow chart of a method of implementing optimal battery scheduling across multiple wind farms in accordance with an embodiment of the invention. 
         FIG. 7  is a diagrammatic view of a wind farm that includes a battery servicing an application. 
         FIG. 8  is a diagrammatic view of the wind farm of  FIG. 7  in which a battery has been moved to a different application within the wind farm based upon optimization in accordance with an embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION 
     Generally, the embodiments of the invention are directed to optimizing energy storage device usage in wind energy applications through appropriate energy device scheduling among different applications, in either the same or different wind farms. In representative embodiments, the managed energy storage devices are batteries although the invention is not so limited. 
     With reference to  FIGS. 1 and 2 , the following description of wind turbine  10  applies equally to all wind turbines in this specification. Wind turbine  10  includes a tower  12 , a nacelle  14  disposed at the apex of the tower  12 , and a rotor  16  operatively coupled to a generator  20  housed inside the nacelle  14 . In addition to the generator  20 , nacelle  14  houses various components needed to convert wind energy into electrical energy and also various components needed to operate and optimize the performance of the wind turbine  10 . The tower  12  supports the load presented by the nacelle  14 , rotor  16 , and other wind turbine components housed inside the nacelle  14 . The tower  12  of the wind turbine  10  operates to elevate the nacelle  14  and rotor  16  to a height above ground level or sea level, as may be the case, at which air currents having lower turbulence and higher velocity are typically found. 
     The rotor  16  includes a central hub  22  and a plurality of blades  24  attached to the central hub  22  at locations distributed about the circumference of the central hub  22 . In the representative embodiment, the rotor  16  includes three blades  24 . The blades  24 , which project radially outward from the central hub  22 , are configured to interact with the passing air currents to produce lift that causes the central hub  22  to spin about its longitudinal axis. The design, construction, and operation of the blades  24  are familiar to a person having ordinary skill in the art. For example, pitch angle control of the blades  24  may be implemented by a pitch control mechanism (not shown). 
     The rotor  16  is coupled by a drive shaft  32  and a gearbox  34  with the rotor assembly of the generator  20 . The gearbox  34  relies on gear ratios in a drive train to provide speed and torque conversions from the rotation of the rotor  16  to the rotor assembly of the generator  20 . Alternatively, the drive shaft  32  may directly connect the central hub  22  of the rotor  16  with the rotor assembly of the generator  20  so that rotation of the central hub  22  directly drives the rotor assembly to spin relative to a stator assembly of the generator  20 . A mechanical coupling  36  provides an elastic connection between the drive shaft  32  and the gear box  34 . 
     The wind turbine  10 , which is depicted as a horizontal-axis wind turbine, has the ability to convert the kinetic energy of the wind into electrical power. Specifically, the motion of the rotor assembly of generator  20  relative to the stator assembly of generator  20  functionally converts the mechanical energy supplied from the rotor  16  into electrical power so that the kinetic energy of the wind is harnessed by the wind turbine  10  for power generation. Wind exceeding a minimum level will activate the rotor  16  and cause the rotor  16  to rotate in a direction substantially perpendicular to the wind. Under normal circumstances, the electrical power is supplied to the power grid  40  as known to a person having ordinary skill in the art. 
     With reference to  FIG. 3  and in accordance with an embodiment of the invention, the wind farms  42 ,  44  each include one or more wind turbines, such as the representative wind turbine  10 . The wind turbines collectively act as a generating plant ultimately interconnected by transmission lines with the power grid  40 , which may be a three-phase power grid. Where the wind farm has more than one turbine, each wind farm  42 ,  44  gangs the wind turbines together at a common location in order to take advantage of the economies of scale that decrease per unit cost with increasing output. It is understood by a person having ordinary skill in the art that wind farms  42 ,  44  may include an arbitrary number of wind turbines of given capacity in accordance with a targeted power output. 
     The power grid  40  generally consists of a network of power stations, transmission circuits, and substations coupled by a network of transmission lines. The power stations generate electrical power by nuclear, hydroelectric, natural gas, or coal fired means, or with another type of renewable energy like solar and geothermal. Additional wind farms analogous to the wind farms  42 ,  44  depicted may also be coupled with the power grid  40 . Power grids and wind farms typically generate and transmit power using Alternating Current (AC). Because the batteries  50 ,  52  can only store and release electrical energy in the form of Direct Current (DC), power converters  46 ,  48  are required to convert between AC power usable by the wind farms  42 ,  44  and the power grid  40 , and DC power usable by the batteries  50 ,  52 . 
     The converters  46 ,  48  are electrically connected between the power grid  40  and respective batteries  50 ,  52 . The converters  46 ,  48  include active switches, such as power semiconductor devices, in a configuration suitable to transform AC power supplied by wind farms  42 ,  44  into DC power during times when batteries  50 ,  52  are storing excess power supplied from the wind farms  42 ,  44  and to transform DC power into AC power at times when batteries  50 ,  52  are supplying power to the grid  40 . When batteries are charging by storing power received from wind farms  42 ,  44 , converters  46 ,  48  condition the output from the wind farms  42 ,  44  to provide a DC output voltage and current suitable for charging batteries  50 ,  52 . When batteries  50 ,  52  are providing power to the grid  40 , converters  46 ,  48  condition the energy discharged by the respective batteries  50 ,  52  to provide an output voltage and current at a frequency and phase appropriate for transmission to the power grid  40 . The design, construction, and operation of converters  46 ,  48  is understood by a person having ordinary skill in the art. 
     At least one sensor  54  is operatively coupled to battery  50  and at least one sensor  56  is operatively coupled to battery  52 . The sensors  54 ,  56  are each configured with one or more sensors to detect and monitor one or more battery operational parameters, including but not limited to voltage, battery current, and temperature, and to generate signals representative of each sensed battery operational parameter. A data converter  58  receives reading in the form of signals communicated as data from sensors  54 ,  56  and communicates the readings to a supervisory controller  60 . 
     In the representative embodiment, the batteries  50 ,  52 , as well as battery  51  described hereinbelow, may include one or more rechargeable electro-chemical storage batteries including, but not limited to, sodium sulfur batteries, lithium ion batteries, and vanadium redox batteries. In alternative embodiments, the supervisory controller  60  can manage a different type of energy storage device, such as flywheels or banks of capacitors, capable of receiving and stably storing electrical energy, and also capable of discharging the stored electrical energy. In yet another alternative embodiment, the energy storage devices managed by the computed actions of the supervisory controller  60  may be hybrid in the sense that energy storage device may include devices of different types, such as one or more flywheels, one or more banks of capacitors, one or more rechargeable batteries, or combinations of these devices. In the representative embodiment, decisions about battery usage across different wind energy applications are made based upon perceived changes in battery capacity; however, a different attribute of the batteries may be measured and assessed by the supervisory controller  60  in its computations and, if the energy devices that are managed are not limited to batteries, other attributes may be measured and assessed by the supervisory controller  60  in its computations. 
     The supervisory controller  60  can be implemented using one or more processors  66  selected from microprocessors, micro-controllers, digital signal processors, microcomputers, central processing units, field programmable gate arrays, programmable logic devices, state machines, logic circuits, analog circuits, digital circuits, and/or any other devices that manipulate signals (analog and/or digital) based on operational instructions that are stored in a memory  64 . Memory  64  may be a single memory device or a plurality of memory devices including but not limited to read-only memory (ROM), random access memory (RAM), volatile memory, non-volatile memory, static random access memory (SRAM), dynamic random access memory (DRAM), flash memory, cache memory, and/or any other device capable of storing digital information. Mass storage device  68  may be a single mass storage device or a plurality of mass storage devices including but not limited to hard drives, optical drives, tape drives, non-volatile solid state devices and/or any other device capable of storing digital information. An Input/Output (I/O) interface  62  may employ a suitable communication protocol for communicating with at least the data converter  58 . 
     Processor  66  operates under the control of an operating system, and executes or otherwise relies upon computer program code embodied in various computer software applications, components, programs, objects, modules, data structures, etc. to read data from and write instructions to the data converter  58  through I/O interface  62 , whether implemented as part of the operating system or as a specific application. The resident computer program code executing on supervisory controller  60  as a set of instructions includes a battery health status monitoring algorithm (BHSMA)  72  operative to collect and store in memory  64 , and/or in the mass storage device  68 , battery operational parameters received from sensors  54 ,  56  as transmitted by data converter  58  through I/O interface  62 , as well as to analyze the battery operational parameters in order to assess the health of the batteries  50 ,  52  and to manage the use of the batteries  50 ,  52  based upon the health assessment as described by the various embodiments of the invention. The supervisory controller  60  may use the BHSMA  72  to periodically determine an optimal battery scheduling based on the battery operational parameters, the current and predicted capacity of the batteries  50 ,  52 , the forecast operational requirements of a particular energy storage application, and the inherent characteristics of the battery  50 ,  52 . 
     A human machine interface (HMI)  70  is operatively coupled to the processor  66  of the supervisory controller  60  in a known manner. The HMI  70  may include output devices, such as alphanumeric displays, a touch screen, and other visual indicators, and input devices and controls, such as an alphanumeric keyboard, a pointing device, keypads, pushbuttons, control knobs, etc., capable of accepting commands or input from the operator and transmitting the entered input to the processor  66 . 
     With reference to  FIG. 5 , an exemplary battery scheduling for the batteries  50 ,  52  ( FIG. 4 ) is presented. Time t 0  represents the time at which batteries  50 ,  52  are put into service at wind farms  42 ,  44 . At time t 0 , both batteries  50 ,  52  have 100% of their designed battery capacity, which is sufficient to satisfy the application requirements for energy storage at the respective wind farms  42 ,  44 . The BHSMA  72  executing on the supervisory controller  60  regularly collects and stores data relating to the capacity and operating conditions of the batteries  50 ,  52 , which is acquired by sensors  54 ,  56  and communicated by data converter  58  through I/O interface  62  to the supervisory controller  60 . 
     At any instant in time, the batteries  50 ,  52  are characterized by a battery capacity that may be expressed as either a state of charge (SOC) or a depth of discharge (DOD). The SOC reflects the charge level of the batteries  50 ,  52  or, in other words, the available charge capacity remaining in each of the batteries  50 ,  52  numerically expressed as a percentage of a rated charge capacity. An SOC of 100 percent indicates a full charge relative to the rated charge capacity, an SOC of 0 percent indicates that the battery  50  is fully discharged relative to the rated charge capacity, and an SOC (e.g., 35 percent) between fully discharged and fully charged indicates that the battery  50  is only partially charged. In one embodiment, the SOC for batteries  50 ,  52  can be determined by measuring battery voltage with the sensors  54 ,  56  and then converting the battery voltage with a calculation using a known discharge curve of voltage as a function of the SOC for the battery  50 . The values of SOC may be compensated to correct for temperature and/or battery current. Alternatively, the SOC for batteries  50 ,  52  may be determined by measuring the battery currents as a function of time and converting the battery current with a calculation that time integrates the battery currents. The supervisory controller  60  may be used to calculate the SOC of batteries  50 ,  52 . 
     Depth of discharge (DOD) is an alternate quantity for use in characterizing the charge status of battery  50  and is determined from changes in the SOC of the battery  50 . The DOD represents the amount of charge capacity removed from a charged battery  50  and the amount of charge capacity then restored to the charged battery  50  numerically expressed as a fraction of a rated charge capacity for the battery  50 . A DOD of unity ( 1 ) indicates that the battery  50  is fully discharged and then fully charged, and a DOD between 0 and 1 indicates that the battery  50  is only partially charged and partially discharged. 
     Alternatively, the SOC and the DOD may be expressed in terms of ampere-hours instead of the fraction of rated capacity or as a percentage. For example, the removal of 250 ampere-hours from the battery  50  rated at 1000 ampere-hours and the subsequent addition of 250 ampere-hours to the battery  50  results in a DOD of 0.25 as expressed herein. 
     As time advances from the initial time t 0 , the capacities of the batteries  50 ,  52  degrade, as represented by their respective solid lines. Due to differing energy storage application demands at wind farms  42 ,  44 , the capacity of battery  50  degrades at a parabolic rate with respect to time, while that of battery  52  degrades in a linear—and initially higher—rate. The degradation is reflected by the reduction in battery capacity for the batteries  50 ,  52  as time lapses. 
     The supervisory controller  60  will use the BHSMA  72  to compute battery capacity for batteries  50 ,  52  and to compare the measured battery capacity with a minimum energy storage capacity requirement for an application or combination of applications associated with each of the batteries  50 ,  52 . As shown in  FIG. 4 , prior to time t 1 , the batteries  50 ,  52  satisfy the minimum energy storage application capacity requirements, and thus no battery scheduling changes are necessary. 
     At time t 1 , the battery capacity of battery  50  has decreased to a value that no longer satisfies the minimum battery capacity for energy storage required by the energy storage application for battery  50  at wind farm  42 . In this instance, the battery capacity of battery  50  has degraded to 75% of its initial value. The BHSMA  72  recognizes this occurrence and, in response to the determination of inadequate capacity, the BHSMA  72  notifies the operator of wind farm  42  of the occurrence. This triggers a sequence of events that allocates a different battery  51 , which is typically rated with 100% of its designed battery capacity, to the energy storage application at wind farm  42 . The wind farm operator will uninstall battery  50  at wind farm  42  and install battery  51  at wind farm  42  to serve the application formerly served by battery  50 . A converter  47 , similar in construction and function to converters  46 ,  48 , is electrically connected between the power grid  40  and battery  51 . At least one sensor  55 , which is similar to sensors  54 ,  56 , is operatively coupled to battery  51  and, similar to sensors  54 ,  56 , sends readings in the form of signals to the data converter  58  for communication to the supervisory controller  60 . 
     The BHSMA  72  executing on the supervisory controller  60  will then optimize the battery scheduling across multiple wind farms, including but not limited to wind farm  42 , by determining whether or not the removed battery  50  can be reallocated to a different application at a different wind farm. In the representative embodiment, the BHSMA  72  determines an optimum battery scheduling that allocates battery  50  to wind farm  44 , where its capacity is added to that already provided by battery  52 . The reallocation of battery  50  to wind farm  44  and the introduction of battery  51  to wind farm  42  are apparent in  FIG. 5 , which depicts the new arrangement for the batteries  50 ,  51 ,  52  at the wind farms  42 ,  44 . 
     The rationale for the reallocation is readily apparent from an examination of  FIG. 4 . Battery  52  at wind farm  44  also has a threshold battery capacity at which battery  52  no longer satisfies the minimum battery capacity for energy storage required by the energy storage application for battery  52  at wind farm  44 . For purposes of discussion, this threshold value for battery  52  may be presumed to be 15 percent. The trajectories (dotted lines) for the degradation of battery capacities of batteries  50 ,  52 , if undisturbed, would result in neither of the batteries  50 ,  52  providing service for the respective intended applications until the end of life for wind farms  42 ,  44 . Specifically, the battery capacities for both batteries  50 ,  52  would degrade to respective values less than their 75 percent and 15 percent thresholds of their respective energy storage applications before the end of life of their respective wind farms  42 ,  44 . 
     However, in the optimal scheduling recommended by the BHSMA  72 , the projected battery capacities (solid lines), the reallocation of battery  50  to supplement battery  52  at wind farm  44  permits the combination of batteries  50 ,  52  to have a sufficient battery capacity to serve the energy storage application at wind farm  44  until the end of life for wind farm  44 . Because battery  50  is reallocated to support battery  52  at wind farm  44 , their collective battery capacities will reduce the slope of the linear degradation trajectory for battery  52 . 
     The storage capacity of battery  51  will degrade over time in the same manner as battery  50  in use to serve the energy storage application at wind farm  42 . However, despite the degradation, the storage capacity of battery  51  remains adequate to service the energy storage application at wind farm  42  until the end of life for wind farm  42 . As a result, the time-sequenced use of battery  50  and battery  51  at wind farm  42  services the energy storage application at wind farm  42  until the end of life for wind farm  42 . 
     By reallocating battery  50  to serve the energy storage application at wind farm  44  instead of removing it from service or leaving it at wind farm  42 , the recommendations on battery scheduling determined by the BHSMA  72  avoids any additional battery reallocations during the useful life span of wind farms  42 ,  44 . Thus, the battery scheduling shown in  FIG. 5  represents an optimal battery scheduling over the life of wind farms  42 ,  44 . 
     In alternative embodiments, the BHSMA executing on the supervisory controller  60  may manage additional batteries and energy storage applications at a multiplicity of wind farms in addition to wind farms  42 ,  44 . 
       FIG. 6  shows a flowchart  100  illustrating a sequence of operations for the supervisory controller  60  that may be used to schedule battery operations across multiple wind farms. In block  102 , the supervisory controller  60  receives sensor readings from sensors that are monitoring the health of various batteries at different wind farms, such as battery  50  at wind farm  42  and battery  52  at wind farm  44 . The BHSMA  72  will calculate a battery capacity for each battery from the capacity sensor data. 
     In block  104 , the BHSMA  72  periodically determines whether the storage capacity for any of the monitored batteries has degraded to become insufficient for the respective application for energy storage. In one embodiment, the BHSMA  72  maintains a threshold battery capacity for an energy storage application served by each battery in a database or other type of data storage structure accessible to programs running on the supervisory controller  60 . The energy storage application database may be maintained on the mass storage device  68  or in the memory  64  of the supervisory controller  60 . The BHSMA  72  compares the battery capacity for energy storage for each of the monitored batteries with a minimum energy storage capacity maintained as an entry in the energy storage application database. If all batteries are projected to satisfy their intended energy storage application for the life of their respective wind farm, then the BHSMA  72  will continue to monitor the storage capacity of each monitored battery (“No” branch of decision block  104 ). However, if the BHMSA  72  determines that any of the monitored batteries has degraded such that the respective battery capacity fails to satisfy the minimum requirement for the application served to reach the expected end of life of the corresponding wind farm, the BHSMA  72  will mark that particular battery as capacity deficient or approaching capacity deficiency (“Yes” branch of decision block  104 ). The capacity deficient battery will be replaced by a different battery having a battery capacity projected to be adequate to serve the application formerly served by the capacity deficient battery and, preferably, to serve that application until the end of life of the corresponding wind farm. 
     In block  106 , the BHSMA  72  computes an optimal battery scheduling for all of the monitored batteries and across all of the wind farms. The BHSMA  72  will evaluate, among its computations, the consumed battery life and the remaining battery life for each of the batteries in an attempt to find a new location with a different application for the capacity deficient battery. The capacity deficient battery may serve a new application at its new location either alone or in combination with another battery. The latter event may use the capacity deficient battery to extend the life of an existing battery. The BHSMA  72  may recommend immediate installation of the capacity deficient battery to perform a different application either at the same wind farm or at a different wind farm, or the BHSMA  72  may recommend that the installation be delayed. 
     To calculate the battery scheduling, the BHSMA  72  may take into account the projected future capacities of all batteries  50 ,  51 ,  52  managed by the supervisory controller  60 ; the minimum energy storage capacity requirements in the energy storage application database; and how long the battery scheduling will satisfy the minimum energy storage capacity requirements. Projected future capacities of batteries  50 ,  51 ,  52  may be determined based on historical data on energy capacity and operating conditions obtained from the sensors  54 ,  56 ; a battery life curve for each of the batteries  50 ,  51 ,  52 ; and forecasts of energy storage application functional requirements. Other data used in calculating the permutation battery service life may be data collected by the BHSMA  72 ; data entered into the supervisory controller  60  through the HMI  70 ; data otherwise entered into or generated internally by the supervisory controller  60 ; or data residing in lookup tables and/or databases, such as the energy storage application database. 
     In block  108 , the optimal battery scheduling is communicated to the wind farms for action in terms of battery placement. Control then returns to block  102  to continue the battery monitoring. 
     As their battery capacities degrade over time, optimizing and maximizing the useful lifetime of wind farm batteries may provide significant economic benefits. Batteries typically represent a large capital investment and often represent a large fraction of the overall cost of a wind farm. The monetary value of power produced by a wind farm may be enhanced by optimizing battery scheduling because storage capacities are better matched with applications of different functional storage requirements. In order to simultaneously optimize battery lifetime and wind farm power economic value, the BHSMA  72  may be used to allocate batteries to serve in different applications taking into account, among other items, the degradation in battery capacity. To assist with this allocation process, sensors may be deployed that measure battery capacity and supply those readings at regular intervals to the BHSMA  72 . At each appropriate control interval, the supervisory controller  60  reallocates the batteries across multiple wind farms or within a wind farm to optimize scheduling based on predicted future functional requirements and projected battery condition. 
     With reference to  FIGS. 7 and 8  in which like reference numerals refer to like features in  FIG. 6  and in accordance with an embodiment of the invention, wind farm  44  includes both batteries  50 ,  52 , but each of the batteries  50 ,  52  serves a different energy storage application that require different energy storage system characteristics. 
     With initial reference to  FIG. 7 , battery  50  serves a more demanding energy storage application at wind farm  44  than the energy storage application served by battery  52 . As a result, the battery capacity of battery  50  for energy storage degrades at an accelerated rate relative to energy storage application served by battery  52 . Eventually, the energy storage capacity of battery  50  falls below a minimum energy storage capacity requirement for its particular application before the end of life for wind farm  44 . The BHSMA  72  executing on the supervisory controller  60  will sense the degradation of battery  50  and, because battery  50  is inadequate for its intended application, will determine a different optimal battery usage pattern for battery scheduling. 
     In  FIG. 8 , the operator of the wind farm  44  has implemented the optimal battery usage pattern determined by the BHSMA  72 . The BHSMA  72  allocates a new battery  51  to replace battery  50 . Battery  51  may be selected to satisfy the minimum energy storage capacity requirements for the more demanding energy storage application at wind farm  44  for the remainder of the expected life of wind farm  44 . However, because battery  50 —even in its degraded state—has a higher remaining storage capacity than battery  52 , the BHMSA  72  can recommend to the operator of wind farm  44  that battery  52  be replaced by battery  50 . In its new application, the battery capacity of battery  50  for energy storage may satisfy the capacity requirements of the less demanding energy storage application originally served by battery  52  for the remainder of the expected lifespan of wind farm  44 . The optimal battery usage pattern satisfies the minimum energy storage capacity requirements of both energy storage applications at wind farm  44  beyond the expected end of life for wind farm  44 , and may avoid additional movements of the batteries  50 ,  51  prior to the end of life of wind farm  44 . The BHMSA  72  may determine that battery  52  can be moved to a different wind farm to service an application at that wind farm or battery  52  may simply be discarded. 
     As will be appreciated by one skilled in the art, the embodiments of the invention may also be embodied in a computer program product embodied in at least one computer readable storage medium having computer readable program code embodied thereon. The computer readable storage medium may be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof, that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. Exemplary computer readable storage media include, but are not limited to, a hard disk, a floppy disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, a portable compact disc read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof. Computer program code for carrying out operations for the embodiments of the present invention may be written in one or more object oriented and procedural programming languages. 
     The methods described herein can be implemented by computer program instructions supplied to the processor of any type of computer to produce a machine with a processor that executes the instructions to implement the functions/acts specified herein. These computer program instructions may also be stored in a computer readable medium that can direct a computer to function in a particular manner. To that end, the computer program instructions may be loaded onto a computer to cause the performance of a series of operational steps and thereby produce a computer implemented process such that the executed instructions provide processes for implementing the functions/acts specified herein. 
     The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, “composed of”, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.” 
     While the invention has been illustrated by a description of various embodiments and while these embodiments have been described in considerable detail, it is not the intention of the applicant to restrict or in any way limit the scope of the appended claims to such detail. Additional advantages and modifications will readily appear to those skilled in the art. The invention in its broader aspects is therefore not limited to the specific details, representative methods, and illustrative examples shown and described. Accordingly, departures may be made from such details without departing from the spirit or scope of applicant&#39;s general inventive concept.