Patent Abstract:
A power generation system including an electrolyzer may be optimized taking non-linear operational constraints into account with lower processing requirements than current solutions in real time. A pre-processing module assesses non-linear operational constraints in a low-overhead manner and passes results to an optimization module. The optimization module determines an operating power value constrained in accordance with results from the pre-processing module. A post-processing module uses results from the pre-processing and optimization modules to assemble instructions to control the electrolyzer. As a result, optimization may be performed in real time.

Full Description:
BACKGROUND OF THE INVENTION 
       [0001]    The disclosure relates generally to power system control and operation, and more particularly to control and operation of a power system including an electrolyzer that produces hydrogen gas via electrolysis. 
         [0002]    In power systems, particularly in so-called “smart grid” power systems employing renewable energy sources, it is often desirable to store excess energy for use during times when power demand exceeds power generation capacity. For example, in a system employing wind, solar, and hydroelectric power generation, an excess of energy may be produced on a clear, windy day, but on a cloudy, calm day, or a calm night, power demand may exceed what these sources may produce. Various solutions have been realized, with using hydrogen gas as an energy carrier being particularly attractive due to its relatively high heating value and its storage. Hydrogen may be used by producing it using excess electricity, such as by electrolysis. 
         [0003]    Electrolysis is one of the well-established technologies for hydrogen production. Electricity is used by an electrolyzer to generate hydrogen with oxygen and heat as byproducts. The generated hydrogen is then compressed and stored in, for example, tube trailers which can be used by a fuel cell plant to generate electricity at any time. This approach is particularly of interest in small isolated or grid-tied microgrids. 
         [0004]    Electrolyzer start-up and shut-down of the electrolyzer, however, require some time to avoid damage due to shifts of temperature and pressure. In addition, an electrolyzer typically should produce a minimum hydrogen production to function. Therefore, in order to increase the life cycle, physical integrity and higher performance of the electrolyzer, some operational constraints such as minimum up-time, down-time and charging power are defined. 
         [0005]    In power generation optimization problems generally (e.g., optimal dispatch within a microgrid where an electrolyzer is included), the aforementioned operational constraints should be considered. If power generation optimization is formulated in the form of mixed integer nonlinear programming (MINLP) or mixed integer linear programming (MILP) problems, operational constraints that are typically complex may be considered, but require substantial computing resources and time. In fact, MINLP and MILP analyses present such computational challenges that the use of these techniques is impractical for real-time and fast-response applications. Conventional linear programming (LP) is a practical technique, but is not suitable since LP cannot consider the above-mentioned, complex operational constraints. 
       BRIEF DESCRIPTION OF THE INVENTION 
       [0006]    Embodiments of the invention disclosed herein may take the form of a controller configured to selectively activate an electrolyzer configured to produce hydrogen gas from source material using electricity. The controller may include a pre-processing module configured to determine a prediction length by dividing a predefined prediction horizon by a predefined time interval. The pre-processing module may also be configured to produce a forced array that indicates whether the electrolyzer may be forced to operate at each time interval during the prediction length responsive to the electrolyzer being online. In addition, the pre-processing module may be configured to produce an availability array that indicates whether the electrolyzer is available for operation at each time interval during the prediction length responsive to the electrolyzer being offline. The controller may also include an optimization module having an operating power determinator configured to produce an operating power value for the electrolyzer, and an operating constraints module configured to impose at least one operating constraint on the determination of the operating power value by the operating power determinator. The operating constraints module may use at least one of the forced array or the availability array to modify the operating power value to produce an optimized operating power value for each time interval of the prediction length. The controller may further include a post-processing module configured to determine whether to send to the electrolyzer instructions including at least one of a start command, a stop command, or the optimized operating power value produced by the operating power determinator. 
         [0007]    Another embodiment may include a computer program product for enabling optimization of a power system that includes an electrolyzer configured for selective production of hydrogen. The system may include a controller configured for communication with the electrolyzer, and may also include a storage device configured to store the computer program product. The controller may include a computing device configured to execute the computer program product, which may include instructions in the form of computer executable program code that configures the controller. When so configured, the controller may determine a prediction length by dividing a predefined prediction horizon by a predefined time interval. A forced array may be produced that indicates whether the electrolyzer may be forced to operate at each time interval during the prediction length responsive to the electrolyzer being online. Responsive to the electrolyzer being offline, an availability array may be produced that indicates whether the electrolyzer is available for operation at each time interval during the prediction length. An operating power value at which the electrolyzer may be operated may be determined responsive to operating constraints that may include at least one of the forced array or the availability array to produce an optimized operating power value at which the electrolyzer may be operated. Instructions may be sent to the electrolyzer if it is determined that instructions, including at least one of a start command, a stop command, or the optimized operating power value, should be sent to the electrolyzer. 
         [0008]    Other aspects of the invention provide methods, systems, program products, and methods of using and generating each, which include and/or implement some or all of the actions described herein. The illustrative aspects of the invention are designed to solve one or more of the problems herein described and/or one or more other problems not discussed. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWING 
         [0009]    These and other features of the disclosure will be more readily understood from the following detailed description of the various aspects of the invention taken in conjunction with the accompanying drawings that depict various aspects of the invention. 
           [0010]      FIG. 1  shows a schematic diagram of an electrolyzer system according to embodiments of the invention disclosed herein. 
           [0011]      FIG. 2  shows a schematic graphical diagram of operating power versus operating time according to embodiments of the invention disclosed herein. 
           [0012]      FIG. 3  shows a schematic flow diagram of an electrolyzer optimization method according to embodiments of the invention disclosed herein 
           [0013]      FIG. 4  shows a schematic block diagram of a computing environment for implementing electrolyzer optimization according to embodiments of the invention disclosed herein. 
       
    
    
       [0014]    It is noted that the drawings may not be to scale. The drawings are intended to depict only typical aspects of the invention, and therefore should not be considered as limiting the scope of the invention. In the drawings, like numbering represents like elements between the drawings. 
         [0015]    The detailed description explains embodiments of the invention, together with advantages and features, by way of example with reference to the drawings. 
       DETAILED DESCRIPTION OF THE INVENTION 
       [0016]    As indicated above, aspects of the invention provide a method of incorporating electrolyzer operational constraints, such as a minimum up-time, a minimum down-time and charging power in a real-time linear programming (LP)-based optimal dispatch process suitable for real-time control applications, particularly for use in optimizing power generation in microgrids. Pre-processing and post-processing modules may be added to conventional LP-based optimal dispatch problem analysis in embodiments. Accordingly, embodiments of the invention disclosed herein provide a new model of the electrolyzer in an optimal dispatch formulation that may incorporate the history of electrolyzer usage and to consider minimum up-time, down-time and charging power for electrolyzer. 
         [0017]    With reference to  FIG. 1 , embodiments of the invention disclosed herein relate to optimization of operation of a power system  100  that may include an electrolyzer  110 , a master controller  120 , a power source  130 , and a grid  140  that may include additional power sources, loads, control equipment, and/or other components as may be desired. Electrolyzer  110  may be of any suitable type, such as one that produces hydrogen from a source fluid using electricity. 
         [0018]    Power source  130  may be connected to electrolyzer  110  under the direction of master controller  120 , though in embodiments electrolyzer  110  may be connected to grid  140  as any other load. In addition, electrolyzer  110  may be powered on and/or off, such as with an electrolyzer controller  111 , which may be responsive to master controller  120 . For example, master controller  120  may send instructions and/or commands to electrolyzer controller  111  that may cause the electrolyzer controller  111  to power on and/or power off electrolyzer  110 . In addition, in embodiments, master controller  120  may determine an operating power value at which electrolyzer  110  should operate and include the operating power value in or with the commands and/or instructions to electrolyzer controller  111 . Thus, the schematic connections illustrated in  FIG. 1  between electrolyzer  110 , master controller  120 , power source  130 , and/or grid  140  may represent power pathways and/or communications pathways. 
         [0019]    As seen in  FIG. 2 , electrolyzer  110  has various operational constraints that may apply. A minimum electric power, P minELE , must be applied to electrolyzer  110  to produce hydrogen, and power should not exceed a maximum electric power, P maxELE , to avoid damage to electrolyzer  110 . In addition, electrolyzer  110  has a minimum operating time, Up_Time, through which electrolyzer  110  should be operated to avoid damage, and a minimum down time, Down_Time, during which electrolyzer  110  should be left off to avoid damage. The example graph of  FIG. 2  shows that electrolyzer  110  was operated for a period exceeding minimum operating time, Up_Time, and was left off for a period exceeding minimum down time, Down_Time, before operating again. 
         [0020]    In embodiments, it may be convenient to use the terms prediction horizon, time interval, and prediction length. The prediction horizon is a predefined period of time over which optimization is to be performed. The time interval is a predefined time resolution of optimization, or how often optimization is to be performed during the prediction horizon. For example, a typical time interval may be from 6 to 15 minutes, though other time intervals may be employed. The prediction length is the number of time intervals for which optimization is to be performed and may be obtained by dividing prediction horizon by time interval. Thus, for a 24-hour prediction horizon and a 12-minute time interval, a prediction length is 120 time intervals. A time step t may be used as an index in embodiments and may vary from 1 to the prediction length, where 1 is the present time step. To simplify implementation of embodiments, all parameters may be expressed in terms of time step, time interval, and prediction length. 
         [0021]    With reference again to  FIG. 1 , master controller  120  may include a pre-processing module  122 , an optimization module  124 , and a post-processing module  126 . In embodiments, pre-processing module  122  may be configured to receive predetermined values of prediction horizon and time interval to determine prediction length from a computer readable device  128 , such as a computer readable storage device, an input device, or other suitable computer readable device. A first or on timer  123  of pre-processing module  122  may measure how much time, On_Time, has passed with electrolyzer  110  operating, and a second or off timer  125  may measure how much time, Off_Time, has passed with electrolyzer  110  shut down or off. In addition, pre-processing module  122  may be configured to receive a predefined value of Up_Time, as well as a predefined value of Down_Time. In embodiments, pre-processing module  122  may determine how many time intervals of operation remain, T Forced , to meet the minimum up time, Up_Time. As seen, for example, in Equation (1), T Forced  may be determined by pre-processing module  122  as the ceiling of a result of dividing a difference between Up_Time and On_Time by the time interval and rounding up to the nearest integer, where ceiling rounds up to the nearest integer. 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       T 
                       Forced 
                     
                     = 
                     
                       ceil 
                        
                       
                         ( 
                         
                           
                             Up_Time 
                             - 
                             On_Time 
                           
                           TimeInterval 
                         
                         ) 
                       
                     
                   
                    
                   
                     
 
                   
                    
                   
                     
                       T 
                       Unavilability 
                     
                     = 
                     
                       ceil 
                        
                       
                         ( 
                         
                           
                             Down_Time 
                             - 
                             Off_Time 
                           
                           TimeInterval 
                         
                         ) 
                       
                     
                   
                 
               
               
                 
                   ( 
                   1 
                   ) 
                 
               
             
           
         
       
     
         [0022]    Similarly, pre-processing module  122  may determine how many time intervals of down time remain T Unavailability  to meet the minimum down time, Down_Time. As also seen in Equation (1), T unavailability  may be determined as the ceiling of a result of dividing a difference between Down_Time and Off_Time by the time interval. 
         [0023]    Pre-processing module  122  may generate one or both of two arrays that employ binary values indicative of when electrolyzer  110  should be forced to operate and when electrolyzer  110  may be available for operation. In embodiments, the array(s) may be generated at every time step, though other frequencies may be employed for generating the array(s). The first array, Forced, may be generated when electrolyzer  110  is online or operating, and may have a length equal to the number of intervals in the prediction length. In embodiments, Forced may have values of 1, indicating that electrolyzer  110  should be operated, from time step t of 1 to the number of intervals of operation remaining T Forced  to meet minimum up time Up_Time, and values of zero for the remainder of Forced, as illustrated by Equation (2). 
         [0000]      Forced( t= 1 to  T   Forced )=1 
         [0000]      Forced( t=T   Forced +1 to PredictionLength)=0  (2)
 
         [0024]    When electrolyzer  110  is offline or not operating, the second array, Availability, may be generated. In embodiments, Availability may have values of 0, indicating that electrolyzer is not available and/or should not be operated, from time step t of 1 to the number of intervals remaining T unavailability  to meet minimum down time, Down_Time, and values of 1 for the remainder of Availability. 
         [0000]      Availability( t= 1 to T Unavailability )=0 
         [0000]      Availability( t=T   unavailability +1 to PredictionLength)=0  (3)
 
         [0000]    Embodiments may generate both arrays, though if electrolyzer  110  is operating, Availability may not be useful, and if electrolyzer  110  is not operating, Forced may not be useful. 
         [0025]    Optimization module  124  in embodiments may include an operating power determinator  127  and an enhancement portion or operating constraints module  129 , which in embodiments imposes operating constraints of electrolyzer  110  onto operating power determinator  127 . While operating constraints module  129  is schematically illustrated as a separate module, this is for convenience, and it should be noted that operating constraints module  129  or its function could be a part of operating power determinator  127 . Operating power determinator  127  may be configured to determine an operating power P ELE  for electrolyzer  110  in accordance with conventional optimization variables and/or techniques, while operating constraints module  129  may be configured to limit the operating power value when predefined constraint criteria are met. For example, operating power constraints module  129  may use the array or arrays generated by pre-processing module  122 , as well as the prediction horizon, time interval, prediction length, and/or time step index to constrain optimized operating power P ELE  for a given time interval, as predefined constraint criteria. Thus, embodiments may enhance conventionally-determination of an operating power determinator  127  by adding considerations of minimum up time Up_Time, minimum down time Down_Time, and minimum operating power P minELE  of electrolyzer  110  with operating constraints module  129 . Optimization module  124  may therefore determine an operating power P ELE  that may be considered using embodiments, such as by adding Equation (4) as at least one operating constraint, such as of electrolyzer  110 , as follows: 
         [0000]    
       
         
           
             
               
                 
                   { 
                   
                     
                       
                         
                           
                             
                               P 
                               ELE 
                             
                              
                             
                               ( 
                               t 
                               ) 
                             
                           
                           = 
                           0 
                         
                       
                       
                         
                           
                             if 
                              
                             
                                 
                             
                              
                             
                               Availability 
                                
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                           = 
                           0 
                         
                       
                     
                     
                       
                         
                           ( 
                           
                             { 
                             
                               
                                 
                                   
                                     0 
                                     ≤ 
                                     
                                       
                                         P 
                                         ELE 
                                       
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                                         ( 
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                                     ≤ 
                                     
                                       P 
                                        
                                       
                                           
                                       
                                        
                                       
                                         max 
                                         ELE 
                                       
                                     
                                   
                                 
                                 
                                   
                                     
                                       if 
                                        
                                       
                                           
                                       
                                        
                                       
                                         Forced 
                                          
                                         
                                           ( 
                                           t 
                                           ) 
                                         
                                       
                                     
                                     = 
                                     0 
                                   
                                 
                               
                               
                                 
                                   
                                     
                                       P 
                                        
                                       
                                           
                                       
                                        
                                       
                                         min 
                                         ELE 
                                       
                                     
                                     ≤ 
                                     
                                       
                                         P 
                                         ELE 
                                       
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                                         ( 
                                         t 
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                                     ≤ 
                                     
                                       P 
                                        
                                       
                                           
                                       
                                        
                                       
                                         max 
                                         ELE 
                                       
                                     
                                   
                                 
                                 
                                   
                                     
                                       if 
                                        
                                       
                                           
                                       
                                        
                                       
                                         Forced 
                                          
                                         
                                           ( 
                                           t 
                                           ) 
                                         
                                       
                                     
                                     = 
                                     1 
                                   
                                 
                               
                             
                             ) 
                           
                         
                       
                       
                         
                           
                             if 
                              
                             
                                 
                             
                              
                             
                               Availability 
                                
                               
                                 ( 
                                 t 
                                 ) 
                               
                             
                           
                           = 
                           1 
                         
                       
                     
                   
                 
               
               
                 
                   ( 
                   4 
                   ) 
                 
               
             
           
         
       
     
         [0026]    More specifically, as illustrated by Equation (4), for 0 values of Availability, electrolyzer operating power may be forced to be 0 by operating constraints module  129  to satisfy or enforce a minimum electrolyzer down time. For 1 values of Availability, however, operating power may be determined by operating power determinator  127 , though additional constraints may be imposed by operating constraints module  129 . 
         [0027]    For 0 values of Forced, operating power may range between zero and maximum operating power of electrolyzer  110 . In this case, operating power P ELE  determined by operating power determinator  127  may automatically be less than or equal to maximum operating power. 
         [0028]    For 1 values of Forced, operating constraints module  129  may force operating power determinator  127  to determine an operating power for electrolyzer that is at least minimum operating power of electrolyzer  110  or no more than maximum operating power of electrolyzer  110 . It should be noted that the variables of Equation (4) may be one or more arrays representing operating power settings for each time step, t, for which Forced and/or Availability have values. Thus, every time pre-processing module  122  generates Forced or Availability, optimization module  124  may generate an array of operating power settings P ELE (t) for each time step, t, from a present time step, t=1 to the prediction length. As indicated above, this may be performed at every time step, t, in embodiments. Additionally, power values may be determined for each time step, t, of the prediction length, in embodiments, so that a one dimensional array of determined optimized operating power values may be produced for a portion of the prediction length (from t=1 to (Up_Time)) or for the entire prediction length. 
         [0029]    Embodiments of the invention may also take the form of an electrolyzer optimization method  300 , an example of which is shown in  FIG. 3  as a schematic flow diagram. After receiving values of Up_Time, Down_Time, PredictionHorizon, and TimeInterval (block  302 ), PredictionLength may be determined (block  304 ), such as by dividing PredictionHorizon by TimeInterval. On_Time may be measured (block  306 ), such as with on timer  123 , and Off_Time may be measured (block  308 ), such as with off timer  125 , and a check may be made to determine whether electrolyzer  110  is operating (block  310 ). If so, Forced(t) may be generated (block  312 ) as a one-dimensional array with a length equal to PredictionLength by pre-processing module  122 . Pre-processing module  122  may populate Forced(t) with 1 values from t=1 to (Up_Time-On_Time) (block  314 ), and may populate Forced(t) with 0 values for a remainder of Forced(t) to t=PredictionLength (block  316 ). 
         [0030]    An operating power value as determined by conventional methods by operating power determinator  127  may be constrained or limited (block  318 ), such as by defining or imposing a range of operating power with operating constraints module  129 . In embodiments, power values may be determined for each time step, t, of the prediction length, so that a one dimensional array of determined optimized operating power values may be produced for a portion of the prediction length (from t=1 to (Up_Time)). 
         [0031]    If it is determined that electrolyzer  110  is not running at block  310 , Availability(t) may be generated (block  320 ) by pre-processing module  122  as a one-dimensional array with a length equal to PredictionLength. Pre-processing module  122  may also populate Availability(t) with 0 values from t=1 to (Down_Time-Off_Time) (block  322 ), and may populate Availability(t) with 1 values for a remainder of Availability(t) to t=PredictionLength (block  324 ). An operating power value as determined by conventional methods by operating power determinator  127  may be limited or constrained (block  318 ), such as by defining or imposing a range of operating power with operating constraints module  129 . In embodiments, constraint or limitation of selected operating power (block  318 ) may be performed based on predefined constraint criteria, which may include Forced(t) and/or Availability(t). 
         [0032]    Commands for electrolyzer  110  may be assembled ( 326 ) by post-processing module  126  after determination, constraint, assessment, and/or modification of operating power responsive to a check by post-processing module  126  to see whether electrolyzer  110  is operating (block  328 ). If not, post-processing module  126  may include a start command in the instructions for electrolyzer  110 , as well as a command for electrolyzer  110  to operate at the optimized operating power value as constrained or limited in block  318  (block  330 ). In embodiments in which power values are determined for each time step, t, of the prediction length, post-processing module  126  may include a command to operate at each optimized power value when all of the determined optimized operating power values are at least the minimum operating power of electrolyzer  110  for a portion of the prediction length (from t=1 to (Up_Time)) or for the entire prediction length. 
         [0033]    If post-processing module  126  finds at block  328  that electrolyzer  110  is operating, then post-processing module  126  may determine whether the optimized operating power value is at least the minimum operating power (block  332 ). As above, in embodiments in which power values are determined for each time step, t, of the prediction length, post-processing module  126  may include a command to operate at each optimized power value (block  330 ) when all of the determined optimized operating power values are at least the minimum operating power of electrolyzer  110  for a portion of the prediction length (from t=1 to (Up_Time)). 
         [0034]    If in block  332  it is determined that electrolyzer  110  is not operating, then post-processing module  126  may include a stop command in the instructions for electrolyzer  110  (block  334 ). If the optimized operating power value is at least the minimum operating power, then post-processing module  128  may include in the instructions for electrolyzer  110  a command for electrolyzer to operate at the optimized operating power value (block  336 ). 
         [0035]    Turning to  FIG. 4 , an illustrative environment  400  for an automatic electrolyzer optimization computer program product is schematically illustrated according to an embodiment of the invention. To this extent, environment  400  includes a computer system  410 , such as an electrolyzer controller  111  and/or master controller  120  or other computing device that may be part of an electrolyzer  110 , which may perform a process described herein in order to execute an automatic power system optimization method considering electrolyzer operational constraints according to embodiments. In particular, computer system  410  is shown including power system optimization program  420 , which makes computer system  410  operable to manage a power system including an electrolyzer by performing a process described herein, such as an embodiment of the power system optimization method discussed above. 
         [0036]    Computer system  410  is shown including a processing component or unit (PU)  412  (e.g., one or more processors), an input/output (I/O) component  414  (e.g., one or more I/O interfaces and/or devices), a storage component  416  (e.g., a storage hierarchy), and a communications pathway  417 . In general, processing component  412  executes program code, such as power system optimization program  420 , which is at least partially fixed in storage component  416 , which may include one or more computer readable storage medium or device. While executing program code, processing component  412  may process data, which may result in reading and/or writing transformed data from/to storage component  416  and/or I/O component  414  for further processing. Pathway  417  provides a communications link between each of the components in computer system  410 . I/O component  414  may comprise one or more human I/O devices, which enable a human user to interact with computer system  410  and/or one or more communications devices to enable a system user to communicate with computer system  410  using any type of communications link. In embodiments, a communications arrangement  430 , such as networking hardware/software, enables computing device  410  to communicate with other devices in and outside of a node in which it is installed. To this extent, electrolyzer optimization program  420  may manage a set of interfaces (e.g., graphical user interface(s), application program interface, and/or the like) that enable human and/or system users to interact with electrolyzer optimization program  420 . Further, power system optimization program  420  may manage (e.g., store, retrieve, create, manipulate, organize, present, etc.) data, such as electrolyzer optimization data  418 , using any solution. 
         [0037]    Computer system  410  may comprise one or more general purpose computing articles of manufacture (e.g., computing devices) capable of executing program code, such as electrolyzer optimization program  420 , installed thereon. As used herein, it is understood that “program code” means any collection of instructions, in any language, code or notation, that cause a computing device having an information processing capability to perform a particular action either directly or after any combination of the following: (a) conversion to another language, code or notation; (b) reproduction in a different material form; and/or (c) decompression. Additionally, computer code may include object code, source code, and/or executable code, and may form part of a computer program product when on at least one computer readable medium. It is understood that the term “computer readable medium” may comprise one or more of any type of tangible medium of expression, now known or later developed, from which a copy of the program code may be perceived, reproduced, or otherwise communicated by a computing device. For example, the computer readable medium may comprise: one or more portable storage articles of manufacture, including storage devices; one or more memory/storage components of a computing device; paper; and/or the like. Examples of memory/storage components and/or storage devices include magnetic media (floppy diskettes, hard disc drives, tape, etc.), optical media (compact discs, digital versatile/video discs, magneto-optical discs, etc.), random access memory (RAM), read only memory (ROM), flash ROM, erasable programmable read only memory (EPROM), or any other tangible computer readable storage medium now known and/or later developed and/or discovered on which the computer program code is stored and with which the computer program code can be loaded into and executed by a computer. When the computer executes the computer program code, it becomes an apparatus for practicing the invention, and on a general purpose microprocessor, specific logic circuits are created by configuration of the microprocessor with computer code segments. A technical effect of the executable instructions is to implement an automatic power system optimization method and/or system and/or computer program product that includes effects of an electrolyzer included in the power system. A determined electrolyzer operating power value may take electrolyzer operating constraints into account without the use of non-linear programming, resulting in reduced processing times that enable real time power system optimization that accommodates electrolyzer operating constraints. 
         [0038]    The computer program code may be written in computer instructions executable by the controller, such as in the form of software encoded in any programming language. Examples of suitable computer instruction and/or programming languages include, but are not limited to, assembly language, Verilog, Verilog HDL (Verilog Hardware Description Language), Very High Speed IC Hardware Description Language (VHSIC HDL or VHDL), FORTRAN (Formula Translation), C, C++, C#, Java, ALGOL (Algorithmic Language), BASIC (Beginner All-Purpose Symbolic Instruction Code), APL (A Programming Language), ActiveX, Python, Perl, php, Tcl (Tool Command Language), HTML (HyperText Markup Language), XML (eXtensible Markup Language), and any combination or derivative of one or more of these and/or others now known and/or later developed and/or discovered. To this extent, electrolyzer optimization program  420  may be embodied as any combination of system software and/or application software. 
         [0039]    Further, power system optimization program  420  may be implemented using a set of modules  422 . In this case, a module  422  may enable computer system  410  to perform a set of tasks used by power system optimization program  420 , and may be separately developed and/or implemented apart from other portions of power system optimization program  420 . As used herein, the term “component” means any configuration of hardware, with or without software, which implements the functionality described in conjunction therewith using any solution, while the term “module” means program code that enables a computer system  410  to implement the actions described in conjunction therewith using any solution. When fixed in a storage component  416  of a computer system  410  that includes a processing component  412 , a module is a substantial portion of a component that implements the actions. Regardless, it is understood that two or more components, modules, and/or systems may share some/all of their respective hardware and/or software. Further, it is understood that some of the functionality discussed herein may not be implemented or additional functionality may be included as part of computer system  410 . 
         [0040]    When computer system  410  comprises multiple computing devices, each computing device may have only a portion of power system optimization program  420  fixed thereon (e.g., one or more modules  422 ). However, it is understood that computer system  410  and power system optimization program  420  are only representative of various possible equivalent computer systems that may perform a process described herein. To this extent, in other embodiments, the functionality provided by computer system  410  and power system optimization program  420  may be at least partially implemented by one or more computing devices that include any combination of general and/or specific purpose hardware with or without program code. In each embodiment, the hardware and program code, if included, may be created using standard engineering and programming techniques, respectively. 
         [0041]    Regardless, when computer system  410  includes multiple computing devices, the computing devices may communicate over any type of communications link. Further, while performing a process described herein, computer system  410  may communicate with one or more other computer systems using any type of communications link. In either case, the communications link may comprise any combination of various types of wired and/or wireless links; comprise any combination of one or more types of networks; and/or utilize any combination of various types of transmission techniques and protocols now known and/or later developed and/or discovered. 
         [0042]    As discussed herein, power system optimization program  420  enables computer system  410  to implement an automatic electrolyzer optimization product and/or method, such as that shown schematically in  FIG. 3 . Computer system  410  may obtain electrolyzer optimization data  418  using any solution. For example, computer system  410  may generate and/or be used to generate electrolyzer optimization data  418 , retrieve electrolyzer optimization data  418  from one or more data stores, receive electrolyzer optimization data  418  from another system or device in or outside of an electrolyzer, electrolyzer system, and/or the like. 
         [0043]    In another embodiment, the invention provides a method of providing a copy of program code, such as power system optimization program  420  ( FIG. 4 ), which implements some or all of a process described herein, such as that shown schematically in and described with reference to  FIG. 3 . In this case, a computer system may process a copy of program code that implements some or all of a process described herein to generate and transmit, for reception at a second, distinct location, a set of data signals that has one or more of its characteristics set and/or changed in such a manner as to encode a copy of the program code in the set of data signals. Similarly, an embodiment of the invention provides a method of acquiring a copy of program code that implements some or all of a process described herein, which includes a computer system receiving the set of data signals described herein, and translating the set of data signals into a copy of the computer program fixed in at least one tangible computer readable medium. In either case, the set of data signals may be transmitted/received using any type of communications link. 
         [0044]    In still another embodiment, the invention provides a method of generating a system for implementing an automatic electrolyzer optimization product and/or method. In this case, a computer system, such as computer system  410  ( FIG. 4 ), can be obtained (e.g., created, maintained, made available, etc.), and one or more components for performing a process described herein can be obtained (e.g., created, purchased, used, modified, etc.) and deployed to the computer system. To this extent, the deployment may comprise one or more of: (1) installing program code on a computing device; (2) adding one or more computing and/or I/O devices to the computer system; (3) incorporating and/or modifying the computer system to enable it to perform a process described herein; and/or the like. 
         [0045]    It is understood that aspects of the invention can be implemented as part of a business method that performs a process described herein on a subscription, advertising, and/or fee basis. That is, a service provider could offer to implement an automatic electrolyzer optimization product and/or method as described herein. In this case, the service provider can manage (e.g., create, maintain, support, etc.) a computer system, such as computer system  410  ( FIG. 4 ), that performs a process described herein for one or more customers. In return, the service provider can receive payment from the customer(s) under a subscription and/or fee agreement, receive payment from the sale of advertising to one or more third parties, and/or the like. 
         [0046]    While the invention has been described in detail in connection with only a limited number of embodiments, it should be readily understood that the invention is not limited to such disclosed embodiments. Rather, the invention can be modified to incorporate any number of variations, alterations, substitutions or equivalent arrangements not heretofore described, but which are commensurate with the spirit and scope of the invention. Additionally, while various embodiments of the invention have been described, it is to be understood that aspects of the invention may include only some of the described embodiments. Accordingly, the invention is not to be seen as limited by the foregoing description, but is only limited by the scope of the appended claims.

Technology Classification (CPC): 8