Abstract:
Systems, methods and computer program products for controlling a micro electrical power grid to select the volume of electricity produced by each power generation source in order to meet demand and achieve a particular optimization goal. In one embodiment, a system operable to control a micro electrical power grid may include a plurality of intelligent power broker (IPB) units and at least one data analytics unit in communication with the IPB units. Each IPB may be associated with an electrical power source or an electrical load and may be operable to collect data relating to its associated electrical power source or electrical load. The data analytics unit may receive the data from the IPB units and may be operable to analyze the data and direct the IPBs to remove one or more of the electrical power loads from the micro electrical grid and/or increase an electrical power output from the electrical sources during time period T.

Description:
RELATED APPLICATION INFORMATION 
       [0001]    This application claims priority from U.S. Provisional Application Ser. No. 61/812,110, entitled “ADVANCED SENSOR FOR ENERGY BROKERAGE” filed on Apr. 15, 2013, which is incorporated by reference herein in its entirety. 
     
    
     FIELD OF THE INVENTION 
       [0002]    The present invention relates generally to managing electrical grids, and more particularly to managing smart electrical grids having distributed localized electrical generation. 
       BACKGROUND OF THE INVENTION 
       [0003]    Modern electric power grids may combine a diverse array of power generation units fueled by coal, nuclear energy, natural gas, solar power, wind, oil, biodiesel, and/or hydro power, just to name a few. However, some of these power sources may not provide a sustained, reliable energy source for power generation. For instance, renewable power sources, such as solar power and wind, may be susceptible to environmental factors that might hinder their availability when electricity is badly needed. Hence, these power sources are often called intermittent because there might be periods of gaps in their availability. On the other hand, more stable energy sources, such as coal, oil, or natural gas, often produce undesired pollutants as a by-product of their operation. This has resulted in environmental regulations that limit these emissions and hence restrict their use. 
         [0004]    The emergence of Smart Grid initiatives throughout the world is forcing rapid change and decentralized operation of the electric grid. A Smart Grid uses sensors, digital communications, and computer software to help collect real-time information from the grid and automate its operation. Smart Grid systems can automatically fix problems in the energy grid by isolating faulting areas, bringing extra power generation online, opening new power lines, or shedding low priority loads from the system. The widespread interest in renewable power sources brings the problem of managing distributed power generation to the forefront. The use of intermittent power sources requires new operational models that take into consideration not only future demand but also future availability of power sources and the cost of the energy they produce. A Micro Grid environment provides a scaled down Smart Grid, capable of running without transmission lines (which are used to bring electricity from far away power generators) by virtue of localized energy generators. Typically, these nearby generators run on natural gas, diesel, or renewable energy. Micro Grids environments are ideal for studying the problems posed by new management challenges arising from distributed generation, autonomous operation, energy storage, and energy sales. 
       SUMMARY OF THE INVENTION 
       [0005]    The present inventors have recognized that modern Smart Grids, and in particular Micro Grid environments, need to be designed with new capabilities to maximize electricity production, while minimizing pollution, costs, and service interruptions. First, the Smart Grid must assess the availability of all power sources and the duration of the energy reserves for producing electricity. Next, the Smart Grid must estimate current and future energy demands for its customer base. Finally, the Smart Grid needs to choose a volume of electricity to be produced by each power generation unit in order to meet demand while reaching a specific goal in terms of reducing cost, pollution, and service interruptions. 
         [0006]    Accordingly, a system, method and computer program product for controlling a micro electrical power grid (e.g., a Micro Grid) provides a framework to help select the volume of electricity to be produced by each power generation source in order to meet demand and reach a particular optimization goal, including for example, minimizing the total amount of energy produced from fossil fuels, minimizing the total cost of electricity produced by all the power sources in operation, and maximizing the total amount of energy produced from renewable sources. 
         [0007]    In one aspect, a system operable to control a micro electrical power grid comprising one or more electrical power sources and one or more electrical loads, may include a plurality of intelligent power broker (IPB) units and at least one data analytics unit in communication with said IPB units. The data analytics unit and the IPBs may communicate, for example, via data network. Each IPB may be associated with an electrical power source or an electrical load and may be operable to collect data relating to its associated electrical power source or electrical load. The data collected by an IPB associated with an electrical power source may include a current electrical power output level (e.g., how much electrical power the source is currently providing) and a reserve electrical power generation level (e.g., how much additional electrical power, if any, the source could provide) of its associated electrical power source. The data collected by an IPB associated with an electrical load may include an electrical power demand level of its associated electrical load. The data analytics unit may receive the data from the IPB units and may be operable to analyze the data to estimate a combined electrical power output level of the electrical power sources, a combined reserve electrical power generation capability level of the electrical power sources and a combined electrical power demand level of the electrical loads during a time period T. The data analytics unit may also be operable to direct the IPBs to accomplish at least one of removing one or more of the electrical power loads from the micro electrical grid and increasing an electrical power output from the electrical sources during time period T. 
         [0008]    In another aspect a method of managing a micro electrical power grid comprising one or more electrical power sources and one or more electrical loads may include receiving data from a plurality of intelligent power broker (IPB) units, wherein each IPB is associated with an electrical power source or an electrical load. The data received from an IPB associated with an electrical power source may include a current electrical power output level and a reserve electrical power generation level. The data received from an IPB associated with an electrical load may include an electrical power demand level. The method may also include initializing a vector V with values of current micro electrical grid conditions, wherein V includes values of current electrical power output levels of the electrical power sources, reserve electrical power generation levels of the electrical power sources and electrical power demand levels of the electrical loads during a time period T. The method may further include first determining whether a current combined electrical power output level meets a combined electrical power demand level over time period T. When it is determined that the combined electrical power output level meets the combined electrical power demand level, the method may repeat the steps of receiving, initializing and first determining for a subsequent time period prior to expiration of time period T. When it is determined that the combined electrical power output level does not meet the combined electrical power demand level, the method may include second determining whether a combined reserve electrical power generation level is sufficient to meet the combined electrical power demand level when added to the current combined electrical power output level. When it is determined that there is sufficient combined reserve electrical power generation, the method may include re-computing vector V to include new electrical power output levels for the electrical power sources and instructing the electrical power sources via their associated IPBs to produce electrical energy at the new electrical power output levels specified in recomputed vector V. When it is determined that there is not sufficient combined reserve electrical power generation capability, the method may include identifying one or more of the electrical power loads for temporary disconnection from the micro electrical grid during time period T to achieve a reduced combined electrical power demand level balanced by the current combined electrical power output level. 
         [0009]    In a further aspect, a computer program product executable to manage a micro electrical power grid comprising one or more electrical power sources and one or more electrical load may include a non-transitory computer usable medium having computer program code embodied therein. The computer program code may include computer readable program code executable by a processer to initialize a vector V with values of current micro electrical grid conditions, wherein V includes values of current electrical power output levels of the electrical power sources, reserve electrical power generation levels of the electrical power sources and electrical power demand levels of the electrical loads during a time period T. The computer program code may also include computer readable program code executable by a processer to first determine whether a current combined electrical power output level meets a combined electrical power demand level over time period T. The computer program code may also include computer readable program code executable by a processer, when it is determined that the combined electrical power output level meets the combined electrical power demand level, to repeat the initializing and first determining for a subsequent time period prior to expiration of time period T. The computer program code may also include computer readable program code executable by a processer, when it is determined that the combined electrical power output level does not meet the combined electrical power demand level, to second determine whether a combined reserve electrical power generation level is sufficient to meet the combined electrical power demand level when added to the current combined electrical power output level. The computer program code may also include computer readable program code executable by a processer, when it is determined that there is sufficient combined reserve electrical power generation, to re-compute vector V to include new electrical power output levels for the electrical power sources and generate instructions for the electrical power sources to produce electrical energy at the new electrical power output levels specified in recomputed vector V. The computer program code may also include computer readable program code executable by a processer, when it is determined that there is not sufficient combined reserve electrical power generation capability, to identify one or more of the electrical power loads for temporary disconnection from the micro electrical grid during time period T to achieve a reduced combined electrical power demand level balanced by the current combined electrical power output level. 
         [0010]    Various refinements exist of the features noted in relation to the various aspects of the present invention. Further features may also be incorporated in the various aspects of the present invention. These refinements and additional features may exist individually or in any combination, and various features of the various aspects may be combined. These and other aspects and advantages of the present invention will be apparent upon review of the following Detailed Description when taken in conjunction with the accompanying figures. 
     
    
     
       DESCRIPTION OF THE DRAWINGS 
         [0011]    For a more complete understanding of the present invention and further advantages thereof, reference is now made to the following Detailed Description, taken in conjunction with the drawings, in which: 
           [0012]      FIG. 1  is a block diagram showing one embodiment of a managed a micro electrical power grid system; 
           [0013]      FIG. 2  is a block diagram of one embodiment of a data analytics unit; 
           [0014]      FIG. 3  is block diagram showing one embodiment of a data processing device that may be included a data analytics unit; 
           [0015]      FIG. 4  is block diagram showing one embodiment of an intelligent power broker (IPB) unit; and 
           [0016]      FIG. 5  is a flow diagram showing the steps of one embodiment of a method of managing a micro electrical power grid. 
       
    
    
     DETAILED DESCRIPTION 
       [0017]      FIG. 1  shows one embodiment of a managed micro electrical power grid  100  system architecture. The micro electrical power grid  100  may include a one or more electrical power sources  102 A- 102 D and one or more electrical loads  104 A- 104 E. As may be typical in a Micro Grid environment, the micro electrical power grid  100  may be configured without transmission lines bringing electricity from distant sources and may instead rely on localized distributed electricity generation. In this regard, the electrical power sources  102 A- 102 D may, for example, include renewable sources such as wind  102 A and solar  102 B, diesel/natural gas generators  102 C, and battery banks  102 D. Other electrical power sources that are not illustrated such as, for example, geothermal, fuel cells, and micro-turbines may also be included in the micro electrical power grid  100  architecture. The electrical loads  104 A- 104 F may, for example, include homes  104 A, vehicles  104 B, office buildings  104 C, data centers  104 D, factories  104 E and other items that consume electricity. 
         [0018]    Individual intelligent power broker (IPB) units  120  are associated with each electrical power source  102 A- 102 D and each electrical load  104 A- 104 E. The micro electrical power grid  100  architecture may also include a data analytics unit  130  in communication via a data network  140  with the intelligent power broker (IPB) units  120  associated with respective electrical power sources  102 A- 102 D or electrical loads  104 A- 104 B. The data analytics unit  130  may utilize measurements and other data received from the IPBs  120  via the data network  140  to manage and deploy energy generation schemes designed to optimize the use of currently available energy sources within the micro electrical power grid  100 . As part of managing and deploying optimal energy generation schemes, the data analytics unit  130  may also utilize weather data, customer data, and energy price data from various other sources (not shown in  FIG. 1 ). 
         [0019]    The IPBs  120  associated with the electrical power sources  102 A- 102 D may have various functional capabilities. In this regard, each IPB  120  associated with an electrical power sources  102 A- 102 D may, for example, be enabled to: (1) monitor the current health of its respective electrical power source  102 A- 102 D; (2) monitor the current power output, reserve power output, and energy reserves for generation of its respective electrical power source  102 A- 102 D; (3) request a change (e.g., an increase or a decrease) in electrical power output from its respective electrical power source  102 A- 102 D based on demand from the electrical loads  104 A- 104 E; and (4) collect operational statistics and provide this to the data analytics unit  130  located within the micro electrical power grid  100 . As such, the IPBs  120  facilitate the provision of real-time, decentralized operational control of the micro electrical power grid  100 . 
         [0020]    The IPBs  120  associated with the electrical loads  104 A- 104 E may be combined with smart meter (SM) units  122 . Rather than monitor electrical generation/output from the electrical power sources  102 A- 102 D, the IPBs  120  associated with the electrical loads  104 A- 104 E monitor consumption of electricity by the electrical loads  104 A- 104 E. The IPBs  120  associated with the electrical loads  104 A- 104 E may also capable of directing their respective SM units  122  to selectively shed (e.g., electrically disconnect from the micro electrical power grid) one or more of the electrical loads  104 A- 104 E deemed to be of low priority when energy is scarce. Like the IPBs  120  associated with the electrical power sources  102 A- 102 D, the IPBs  120  associated with the electrical loads  104 A- 104 E collect statistics that are sent to the data analytics unit  130 . 
         [0021]    Although only one data analytics unit  130  is shown in  FIG. 1 , there may be more than one data analytics unit  130  included in the micro electrical power grid  100 . In this regard, each data analytics unit  130  may focus on studying different aspects of the grid operations or catering to a different audience of power engineers/managers/consumers. For the presently described embodiment including only one data analytics unit  130 , the data analytics unit  130  may have various functional capabilities. In this regard, the data analytics unit  130  may, for example, be enabled to: (1) collect and store statistics from the micro electrical power grid  100  operation at various granularity levels, organized by time, geographical region, or energy used for power generation; (2) provide a framework to generate reports that help project energy demands by combining operational data with existing models for demand estimation used by power engineers; (3) provide a framework to generate reports that help project energy availability by combining operational data, health of power generators, environmental factors, and weather forecast data with existing models for energy availability estimation used by power engineers; and (4) support long-running simulations of the micro electrical power grid  100  that help demand forecasting, uncover vulnerabilities, or prioritize capital investments to improve the grid infrastructure. 
         [0022]      FIG. 2  shows one embodiment of a data analytics unit  130  that may be included in a micro electrical power grid  100  such as shown in  FIG. 1 . The data analytics unit may include one or more data processing devices  210  and a data storage device  220 . The one or more data processing devices  210  may receive various categories of data such as, for example, energy pricing data  230 , electricity generation/output data  232 , electricity demand data  234 , customer data  236  and weather data  238 . Data such as the electricity generation/output data  232  and electricity demand data  234  may be received from IPBs  120  associated with electrical power sources  102 A- 102 D and electrical loads  104 A- 104 E within a micro electrical power grid  100  such as shown in  FIG. 1 . Other data such as energy pricing data  230 , customer data  236  and weather data  238  may be received from sources outside of the micro electrical power grid  100 . 
         [0023]    The data processing device(s)  210  may comprise a cluster of data processing hardware devices executing data analytics software such as, for example, Hadoop developed by the Apache Software Foundation. In this regard, as depicted in  FIG. 3 , each data processing device  210  may, for example, include one or more computer processors  310  and the data analytics software may comprise computer executable program code  320  stored on a non-transitory computer readable medium  330  (e.g., a hard disc drive, an optical disc, a flash drive), and executable by the one or more computer processors  310 . Each data processing device  210  may also include a data communications port  340  (e.g., an Ethernet port, a universal serial bus (USB) port, a wireless communications port, or the like) that enables data to be sent from and received by the computer processor(s)  310 . Data may be communicated via the communications port  340  to/from each data processing device  210  to/from another data processing device within the same data analytics unit  130  and/or to/from another data analytics unit  130  or one or more of the IPBs  120  via the data network  140 . In this regard, as depicted in FIG.  4 , each IPB  120  may include an interface processor  410  enabling the IPB to interface with its respective electrical power source or electrical load as well as interface with a smart meter unit if associated with an electrical load. Each IPB  120  may also include a non-transitory data storage medium  430  for storing data (e.g., in a vector V as discussed herein) and a communications port  440  (e.g., an Ethernet port, a universal serial bus (USB) port, a wireless communications port, or the like) enabling the IPB to transmit and receive data to/from the data network  140 . 
         [0024]    Referring again to  FIG. 2 , the data storage device(s)  220  may comprise a Hive Data Warehouse, which provides fast access and processing capabilities to help distill the raw data into meaningful abstractions on which detailed analysis software can operate. The Hive Data Warehouse may be organized into one or more Data Marts. Each Data Mart provides a specific view of the system aimed at a different community/application domain such as, for example, reliability engineers, energy brokers, field operators, and so on. 
         [0025]    The data analytics unit  130  may also include one or more machine learning tools  240  and one or more decision support tools  250 . In this regard, the machine learning tool(s)  240  and the decision support tool(s)  250  may be implemented in the form of computer executable program code stored on a non-transitory computer readable medium and executable by the one or more computer processors of the data processing device(s)  210 . Using data that has been organized within a Data Mart (e.g., data  232 ,  234  received from the IPBs  120  in the micro electrical power grid  100  and, if provided, other data  230 ,  236  and  238 ), the machine learning tool(s)  240  may be used to carry out tasks designed to estimate energy needed to meet demand, weak points in the system, fault occurrence, backup generation that must be activated, loads that might need to be shed, and the like. The information obtained from the machine learning tool(s)  240  may be passed along to the decision support tool(s)  250 . The decision support tool(s)  250  help system operators make decisions regarding operating parameters of the micro electrical power grid  100  to ensure proper grid operations. In this regard, the decision support tool(s)  250  may include rich visual interfaces, virtual reality displays, and/or a 3D immersion system that help operators focus on important situations that must be solved. The visual interfaces, virtual reality displays, and/or 3D immersion system may be output on one or more displays devices  260  viewable by the system operators. The decision support tool(s)  250  can also work as autonomous entities that make decisions regarding operating parameters of the micro electrical power grid  100 , and simply ask operators to confirm the actions of the decision support tool(s)  250 . 
         [0026]    The machine learning tool(s)  240  and the decision support tool(s)  250  may implement a number of considerations. One key consideration for the micro electrical power grid  100  is to determine how much electricity to produce from the electrical power sources  102 A- 102 D to meet the demands of the electrical loads  104 A- 104 E. Consider a collection of electrical power sources S 1 , S 2 , . . . , S n , (e.g. electrical power sources  102 A- 102 D). Each power source S i  has a maximum generation capacity C i , and at any given moment it can be producing energy at a specific percentage a i ε[0,1]. This value may be referred to as the generation factor of the power source. For example, a value of 0.7 indicates that the power generator is running at 70% capacity. Likewise, a value of 0 means that the generator is off, and a value of 1 means it is working at 100% capacity. Thus, a i C i  indicates how much energy is being produced by source S i . Hence, at any given point in time the total power generated in the grid, P T  is given by: 
         [0000]    
       
         
           
             
               P 
               T 
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 n 
               
                
               
                   
               
                
               
                 
                   a 
                   i 
                 
                  
                 
                   C 
                   i 
                 
               
             
           
         
       
     
         [0000]    The power grid must serve the power demands of a collection of loads L 1 , L 2 , . . . , L k  (e.g., electrical loads  104 A- 104 E), where D i  denotes the demand of load L i . Thus, the total system demand D T  is given by: 
         [0000]    
       
         
           
             
               D 
               T 
             
             = 
             
               
                 ∑ 
                 
                   i 
                   = 
                   1 
                 
                 k 
               
                
               
                   
               
                
               
                 D 
                 i 
               
             
           
         
       
     
         [0000]    The goal is to find a vector V of parameters v=(p 1 , p 2 , . . . , p n ) that indicate how much energy to get from each source such that P T =D T , while optimizing a specific operational goal. One example of an operational goal is minimizing the total amount of energy produced from fossil fuels. In this regard, given a power source S i , the amount of fossil fuel electricity, PF i , produced by S i  is defined as: 
         [0000]    
       
         
           
             
               PF 
               i 
             
             = 
             
               { 
               
                 
                   
                     0 
                   
                   
                     NFF 
                   
                 
                 
                   
                     
                       
                         a 
                         i 
                       
                        
                       
                         C 
                         i 
                       
                     
                   
                   
                     FF 
                   
                 
               
             
           
         
       
     
         [0000]    where, FF denotes that the power source is fossil-fueled and NFF indicates the power plant is not fossil-fueled. Another example of an operational goal may be minimizing the total cost of electricity produced by the power sources. 
         [0027]      FIG. 5  shows one embodiment of a method  500  of managing a micro electrical power grid. The method  500  of  FIG. 5  may be undertaken by a data analytics unit  130  together with IPBs  120  in communication via a data network  140  with the data analytics unit  130  to manage a micro electrical power grid  100  such as depicted in  FIG. 1 . In this regard, one or more of the steps of method  500  may be performed by executing computer executable program code stored on a non-transitory storage medium with one or more computer processors of the data analytics unit. 
         [0028]    The method  500  may be initiated with step  506  in which data is received. The data may be received by one or more data processing devices of a data analytics unit within the micro electrical power grid. Portions of the data may be received from a plurality of intelligent power broker (IPB) units associated with electrical power sources and electrical loads within the micro electrical power grid, and portions of the data may be received from other sources outside of the micro electrical power grid. The received data may, for example, include a current electrical power output level for each electrical power source in the micro electrical grid, an estimated electrical demand level for each electrical load in the micro electrical power grid, an available reserve electrical power generation capability level for each electrical power source in the micro electrical grid, data modeling weather conditions, energy pricing data from energy markets, and customer data. 
         [0029]    In step  512 , a vector V is initialized with the values of current micro electrical power grid conditions. In this regard, vector V may be initialized using values included in or derived from data received in step  506 . 
         [0030]    In step  518  a determination is made as to whether a current combined electrical power output level meets an estimated combined electrical power demand level over a time period T. In this regard, the current combined electrical output level may be obtained by adding together current electrical power output level included in vector V for each electrical power source in the micro electrical grid and the estimated combined power demand level may be obtained by adding together the estimated electrical demand level included in vector V for each electrical load in the micro electrical power grid. If it is determined in step  518  that the current combined electrical power output level meets the estimated combined electrical power demand level over time period T, then the method  500  waits in step  524  for a period of time less than T and thereafter returns to step  506  to begin the method  500  again for time period subsequent to time period T. In this regard, time period T and the subsequent time period may overlap, or the subsequent time period may begin following completion of time period T. If it is determined in step  518  that the current combined electrical power output level does not meet the estimated combined electrical power demand level over time period T, then the method proceeds to step  530 . 
         [0031]    In step  530 , a determination is made as to whether the combined reserve electrical power generation level is sufficient to meet the combined electrical power demand level over time period T when added to the current combined electrical power output level. In this regard, the combined reserve electrical power generation level may be obtained by adding together the reserve electrical power generation level included in vector V for each electrical power source included in the micro electrical grid. If it is determined in step  530  that there is sufficient combined reserve electrical power generation available within the micro electrical power grid then the method  500  proceeds to step  548 . If it is determined in step  530  that there is not sufficient combined reserve electrical power generation available within the micro electrical power grid then the method  500  proceeds to step  536 . 
         [0032]    In step  536 , one or more of the electrical power loads are identified for temporary disconnection from the micro electrical grid in order to balance the combined electrical power output level during time period T with a revised estimated combined electrical power demand level for the remaining electrical power loads during time period T. In this regard, customer data indicating that a particular load is low priority or on an interruptible contract may be used to help identify one or more electrical loads for temporary disconnection during time period T. In step  542 , instructions may be sent via one or more IPBs to temporarily disconnect their associated load(s) during time period T. In this regard, an IPB may coordinate with its associate smart meter in order to disconnect its associate load from the micro electrical power grid. Thereafter, the method  500  may return to step  506  to begin the process again for a subsequent time period. 
         [0033]    In step  548 , vector V is re-computed to include new electrical power output levels for the electrical power sources. In this regard, a new vector V may be re-computed in a variety of manners including for example by incrementing the generation factor of one or more of the electrical power sources having available reserve generation capacity by an amount p. The increment amount p may be small and may be obtained in a variety of manners including, for example, by using a constant value for p, using a randomly generated number in the range [0,1] for p, using a randomly generated number drawn from a probability distribution in range [0,1] for p, or using an average value obtained from past information for p. 
         [0034]    After completing step  548 , in step  554  a determination is made as to whether re-computed vector V represents a solution that balances the combined electrical power demand level with the combined electrical power output level of re-computed vector V. If it is determined in step  554  that re-computed vector V does not balance the combined electrical power demand level with the combined electrical power output level, then the method  500  may return to re-computing step  548 . If it is determined in step  554  that re-computed vector V balances the combined electrical power demand level with the combined electrical power output level, then in step  560  a determination may be made as to whether re-computed vector V optimizes a desired goal such as, for example, maximizing the amount of electricity provided by renewable electrical power sources and reducing the cost of electricity generated in the micro electrical power grid. If it is determined in step  560  that re-computed vector V optimizes the desired goal, then the method may proceed to step  566 . If it is determined in step  560  that re-computed vector V does not optimize the desired, goal then the method  500  may return to step  548 . 
         [0035]    In step  566 , the electrical power sources may be instructed to provide electricity in accordance with the re-computed vector V. In this regard, the data analytics unit may send instructions to the IPBs associated with the electrical power sources via the data network of the micro electrical power grid. 
         [0036]    In step  572 , the new micro electrical power grid configuration represented by re-computed vector V may be stored. In this regard, re-computed vector V may, for example, be stored in a data storage device of the data analytics unit. 
         [0037]    In step  578 , the method  500  waits for a period of time less than the time period T. Thereafter, the method returns to step  506  to commence the method for a period of time subsequent to time period T. 
         [0038]    The method  500  may be run constantly or intermittently, as desired, throughout the micro electrical power grid. In this regard, the method  500  may be run by the data analytics unit and vector V may be disseminated to the various IPBs within the micro electrical power grid. The IPBs may then use vector V to request/generate more electrical power or to make adjustments to their current configuration. The IPBs may also be enabled to modify vector V based on local information not yet received by the data analytics unit. 
         [0039]    Deviations may be made from the specific embodiments disclosed in the specification without departing from the spirit and scope of the invention. For example, at least some of the functionalities performed by many of the processes and modules discussed herein may be performed by other modules, devices, processes, etc. The illustrations and discussion herein has only been provided to assist the reader in understanding the various aspects of the present disclosure. 
         [0040]    A computer program (also known as a program, software, software application, script, or code) used to provide the functionality described herein (such as to provide the data analytics unit and/or IPB functionalities disclosed herein) may be written in any form of programming language, including compiled or interpreted languages, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by an information flow network. 
         [0041]    The block diagrams, processes, protocols and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit). Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read-only memory or a random access memory or both. Generally, the elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. The techniques described herein may be implemented by a computer system configured to provide the functionality described. 
         [0042]    While this disclosure contains many specifics, these should not be construed as limitations on the scope of the disclosure or of what may be claimed, but rather as descriptions of features specific to particular embodiments of the disclosure. Certain features that are described in this specification in the context of separate embodiments and/or arrangements can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination. 
         [0043]    Additionally, the foregoing description of the present invention has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit the invention to the form disclosed herein. Consequently, variations and modifications commensurate with the above teachings, and skill and knowledge of the relevant art, are within the scope of the present invention. The embodiments described hereinabove are further intended to explain best modes known of practicing the invention and to enable others skilled in the art to utilize the invention in such, or other embodiments and with various modifications required by the particular application(s) or use(s) of the present invention. It is intended that the appended claims be construed to include alternative embodiments to the extent permitted by the prior art.