Patent Publication Number: US-10782721-B2

Title: Method and apparatus for balancing power on a per phase basis in multi-phase electrical load facilities using an energy storage system

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of U.S. Provisional Patent Application Ser. No. 61/693,684, filed Aug. 27, 2012, U.S. Provisional Patent Application Ser. No. 61/693,709, filed Aug. 27, 2012 and U.S. Provisional Patent Application Ser. No. 61/801,274, filed Mar. 15, 2013, which are all herein incorporated by reference. 
    
    
     BACKGROUND OF THE INVENTION 
     Field of the Invention 
     Embodiments of the present invention generally relate to a method and an apparatus for controlling fluctuations in power and amount of power used at an electric load location and/or on an electrical grid. 
     Description of the Related Art 
     Energy demand at a commercial site, such as a business, or at home will vary over the time of day. In a typical home or hotel setting, there is a peak in the morning when the occupants get up and when the occupants return home at the end of the day. This typically creates two demand peaks during a normal day. Commercial buildings tend to follow different patterns depending on the nature of the business. For example, usage is typically low when a commercial building is closed, and may be relatively constant or fluctuate between moderate to high levels depending on the type of business when the building is open. For example, a car wash may have more fluctuations in its energy use than an office building in a moderate climate. 
     The cost to a utility for generating or purchasing electrical energy increases dramatically during periods of peak use versus periods of off-peak usage. In order to compensate for the higher peak-hours costs, utility companies often employ time of day-based rate schedules, charging a significantly higher rate for electrical energy (e.g., cost per kilowatt-hour (kW-hr)) consumed during peak usage hours as compared to energy consumed during off-peak hours. For example, homes and businesses may pay for electricity on a per-kilowatt hour basis with one rate applying during off-peak hours, and another, higher, rate applying during peak hours. The higher rates charged during peak usage periods can lead to significantly higher energy costs for the user, especially when the user&#39;s period(s) of high demand coincides with or falls within the interval set by the utility as peak hours. 
     Devices have been developed that help users reduce the cost of electricity purchases from the power grid by storing electricity in energy storage mediums, such as batteries, that can be “drawn down” during peak hours to reduce demand from the grid. The batteries can be charged during non-peak hours, thus reducing the total cost of electricity, and, during favorable conditions, electricity can even be sold back to the grid. This process is often referred to as “energy arbitrage,” which is generally the storing of energy at one time of day and then the discharging of energy at another time, effectively shifting energy consumption from one time period to another. 
     Energy storage mediums, especially battery energy storage, are expensive and, while various techniques are known by which an energy storage system can be used to optimize energy use at a business or home, such techniques are generally inefficient in applying stored energy to effectively control the energy use at an electric load location. Consequently, an impractical quantity of energy storage mediums are required at an electric load location to realize useful energy arbitrage. Generally, such energy storage systems use simple methods for controlling the charging and discharging of power provided to offset peak demands. For example, two approaches commonly used in energy storage systems include: Using a simple timer to control charge times of the energy storage system (typically during off-peak hours) and discharge times (typically during peak demand hours); and using a single demand set-point that the storage system reacts to while monitoring and controlling the energy use of the business or home location. A single demand set-point generally is a single level set-point to which the controlling element in an energy storage system will control during operation. Each of these approaches generally requires an uneconomical amount of energy storage capacity in order to offset demand peaks at the electric load location. Furthermore, use of a single demand set-point typically results in an energy storage system running out of energy storage availability due to over-reaction of the controlling components to the demand set point set by the controlling system. Thus, a need exists for power charge and discharge systems and methods that more effectively utilize the consumable energy storage medium components in an energy storage system. 
     In multi-phase electrical power distribution configurations at electric load sites, or commercial electric load locations, power is often unevenly consumed across the different phases. Additionally, if there is on-site power generation, such as solar, this on-site generation can be connected to a single phase creating an even greater imbalance in the load seen by each phase at the electric load site. While these phase imbalances may not have a large impact on the electrical grid for singular smaller loads, they do have an impact on the distribution grid at larger singular loads or multiple unbalanced smaller loads. This impact can cause power distribution equipment damage or early failure, as well as transmission and generation efficiency losses. Therefore, there is a need for power charge and discharge systems and methods that can help control and/or limit the imbalance in power flowing through one phase versus other phases at an electric load site. Also, there is a need for power charge and discharge systems and methods that can help control and limit the fluctuations in multiphase power delivered to the electric load location from the electrical grid. 
     SUMMARY OF THE INVENTION 
     Embodiments of the present invention include control methods employed in multiphase distributed energy storage systems that are located behind utility meters typically located at, but not limited to, medium and large commercial and industrial locations. According to such control methods, these multiphase distributed energy storage systems operate semi-autonomously, but may be in frequent contact with a cloud-based optimization engine that is configured to develop energy control solutions based on various data inputs and to communicate these energy control solutions to one or more of the distributed energy storage systems. One of the goals of an installed multiphase distributed energy storage system is to monitor the location&#39;s electric load and electricity use on each phase, and discharge at times of high demand peaks in order to reduce the peak power provided by the electric grid and/or any imbalance between power draw on the various phases, while maximizing the finite amount of energy stored in the consumable energy storage components in the distributed energy storage system. To perform this task the distributed energy storage system may control the kilowatt demand of the local electric load location by controlling the control set-point over time by discharging energy sources, such as DC batteries, through multiple bidirectional power converters. 
     Embodiments of the invention may further provide a system for managing power delivery, comprising a power monitor that is in electric communication with a first phase and a second phase of a multiphase electric line at an electric load location, and is configured to measure the power transferred through the first phase and the second phase. The system may also include a system controller that is configured to receive information regarding the power transferred through the first phase and the second phase from the power monitor, and use the received information to control a transfer of energy from or to the first phase of the multiphase electric line from a bidirectional power converter that is in communication with an energy source. The system may further include an optimization engine configured to receive one or more external inputs and create a set of operating parameters based on the one or more external inputs, and the system controller may be further configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through the first phase of the multiphase electric line below a threshold value. The system may further include an optimization engine configured to receive one or more external inputs and create one or more operating control curves based on the one or more external inputs, wherein the system controller is configured to receive the one or more operating control curves, compare the one or more operating control curves to information received from the power monitor and the energy source, and control a transfer of energy from or to at least the first phase or the second phase from the energy source based on the comparison. 
     Embodiments of the invention may further provide a system for managing power delivery, comprising a power monitor that is in electric communication with a first phase and a second phase of a multiphase electric line at an electric load location, and is configured to measure the power transferred through the first phase and the second phase, a first bidirectional power converter coupled between the first phase and a source bus, and a system controller that is configured to receive information regarding the power transferred through the first phase and the second phase from the power monitor, and use the received information to control a transfer of energy between the first phase of the multiphase electric line and the source bus by use of the first bidirectional power converter. The system may further comprise a second bidirectional power converter coupled between the second phase and the source bus, and an energy source coupled to the source bus. The system may further comprise an optimization engine configured to receive one or more external inputs and create one or more operating control curves based on the one or more external inputs, wherein the system controller is configured to receive the one or more operating control curves, compare the one or more operating control curves to information received from the power monitor and the energy source, and control a transfer of energy from or to the first or second phase from the energy source based on the comparison. 
     Embodiments of the invention may further provide a method of managing power delivery, comprising monitoring an amount of power transferred through a first phase and a second phase of a multiphase electric line of an electric load location, and transferring power to the first phase from a source bus based on the information received from monitoring the amount of power transferred through the first and the second phases, wherein delivering power to the first phase reduces the difference in the amount of power transferred through the first and second phases. The method may further include monitoring an amount of power transferred through a third phase of the multiphase electric line of the electric load location, and transferring power to the first phase from the third phase based on the information received from monitoring the amount of power transferred through the first, second and third phases. The method may further include transferring energy to the first phase from the first bidirectional power converter coupled to the source bus and transferring energy from the second phase to the source bus using a second bidirectional converter. The method may further include the steps of transferring energy to the first bidirectional power converter from the second phase, transferring energy from the first bidirectional power converter to an energy source, and transferring energy from the energy source to the first phase. 
     Embodiments of the invention may further provide a method of managing power delivery, comprising monitoring an amount of power transferred through a first phase, a second phase and a third phase of a multiphase electric line of an electric load location, and transferring power to the first phase from at least one power source selected from a group consisting of an energy source, the second phase and the third phase based on the information received from monitoring the amount of power transferred through the first, the second and the third phases, wherein delivering power to the first phase reduces the difference in the amount of power transferred through the first, second and third phases. 
     Embodiments of the invention may further provide a system for controlling the transfer of energy between an electric load location and an electric grid comprises a power monitor, an optimization engine, and a system controller. The power monitor is configured to monitor electric power usage at a point of common coupling with an electric meter and an electric load at a common location, wherein the electric meter is configured to measure power transferred to the electric load location through the electric line. The optimization engine is configured to receive one or more external inputs and create a set of operating parameters based on the one or more external inputs. The system controller is configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through the electric line below a threshold value. 
     Embodiments of the invention may further provide a system for controlling energy transferred between an electric grid and an electric load location comprises an optimization engine and a distributed energy source. The optimization engine is configured to receive one or more external inputs and create one or more operating control curves based on the one or more external inputs. The distributed energy source comprises a system controller and a power monitor that is configured to monitor an electric line that is coupled to an electric meter, wherein the system controller is configured to receive the one or more operating control curves, compare the one or more operating control curves to information received from the power monitor, and control a transfer of energy from or to the electric line from an energy source based on the computation. 
     Embodiments of the invention may further provide a method of controlling energy transferred between an electric grid and an electric load location comprises monitoring a first rate of energy transfer from an electric line to the electric load location, wherein the electric line is coupled to an electric meter adapted to determine power transferred between the electric load location and the electric grid, receiving a first set of operating parameters that are created based on one or more received external inputs and, based on the first set of operating parameters, varying the energy transfer from an energy source to the electric line to cause the first rate of energy transfer to remain below a threshold value, wherein the threshold value varies with time. 
     Embodiments of the invention may further provide a computer readable medium configured to store instructions executable by a processor of a host device, the instructions when executed by the processor causing the processor to generate control parameters based on a simulation that is performed using forecast information, monitor a first rate of energy transfer from an electric line to an electric load location and control a second rate of energy transfer between the electric line and an energy source based on the control parameters, wherein controlling the second rate of energy transfer alters the first rate of energy transfer. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       So that the manner in which the above recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments. 
         FIG. 1  illustrates a plurality of distributed energy storage systems that are each positioned at an electric load location that is coupled to an electrical grid, according to one embodiment of the invention. 
         FIG. 2A  schematically illustrates one embodiment of a distributed energy storage system that is disposed at an electric load location, according to one embodiment of the invention. 
         FIG. 2B  schematically illustrates two electric load locations that are each coupled to the electric grid through an electric grid and include a distributed energy storage system, according to one embodiment of the invention. 
         FIG. 2C  schematically illustrates a single electric load location that is coupled to the electric grid through two electric meters, according to one embodiment of the invention. 
         FIG. 3  illustrates a process flow diagram for controlling the fluctuation of power at an electric load location and/or power level being drawn by one or more electric load(s) at the electric load location by use of a distributed energy storage system, according to one embodiment of the invention. 
         FIG. 4  illustrates the operation method and information flow provided to and/or used by a set-point controller, according to one or more embodiments of the invention. 
         FIG. 5  illustrates the operation method and information flow provided to and/or used by a runtime controller, according to one or more embodiments of the invention. 
         FIG. 6  illustrates an overview of a communication process between an optimization engine, a simulator farm, and a distributed energy storage system, according to an embodiment of the invention. 
         FIG. 7  illustrates an overview of a system simulator process, according to an embodiment of the invention. 
         FIG. 8A  is a graph that illustrates the performance over a business day of a prior art energy storage system using a single-demand set-point. 
         FIG. 8B  is a graph that illustrates the performance over a business day of an energy storage system configured according to one or more embodiments of the invention. 
         FIG. 8C  is a graph that illustrates the performance over a business day of an energy storage system configured according to one or more embodiments of the invention. 
         FIG. 9  illustrates a control system that includes an optimization engine and is configured to generate operating parameters for an energy storage system controller, according to one embodiment of the invention. 
         FIG. 10  is a block diagram of a general process sequence used by a control system to create and deliver forecast and control information to one or more distributed energy storage systems, in accordance with an embodiment of the invention. 
         FIG. 11  is a block diagram of a process sequence used by a coefficient engine to create updated coefficients for one or more of the distributed energy storage systems associated with a control system, in accordance with an embodiment of the present invention. 
         FIG. 12  is a block diagram of a process sequence used by a forecast engine to generate forward-looking forecasted load profiles for a particular electric load location serviced by a distributed energy storage system associated with the control system. 
         FIG. 13  is a block diagram of a process sequence used by a simulation engine to determine optimal set-points and battery curves, according to an embodiment of the invention. 
         FIG. 14  is a block diagram of a process sequence used by a solution engine to generate a solution of optimal set-points and battery curves for a distributed energy storage system, according to an embodiment of the invention. 
         FIG. 15A  illustrates a flat battery curve, according to an embodiment of the invention. 
         FIG. 15B  illustrates a stepped battery curve, according to an embodiment of the invention. 
         FIG. 15C  illustrates a continuously varying battery curve, according to an embodiment of the invention. 
         FIG. 16  is a block diagram of a process sequence used by an optimization engine to generate and distribute an optimal battery curve for a distributed energy storage system, according to an embodiment of the invention. 
         FIG. 17  schematically illustrates a phase power correction system found in a distributed energy storage system at an electric load location, according to one embodiment of the invention. 
         FIG. 18  schematically illustrates a phase power correction system found in a distributed energy storage system that has an on-site generator at an electric load location, according to one embodiment of the invention. 
         FIG. 19  illustrates an example of three unbalanced power draws per unit time for three phases and a final balanced power draw per unit time for all phases created using an embodiment of the invention described herein. 
     
    
    
     To facilitate understanding, identical reference numerals have been used, where possible, to designate identical elements that are common to the figures. It is contemplated that elements disclosed in one embodiment may be beneficially utilized on other embodiments without specific recitation. The drawings referred to here should not be understood as being drawn to scale unless specifically noted. Also, the drawings are often simplified and details or components omitted for clarity of presentation and explanation. The drawings and discussion serve to explain principles discussed below, where like designations denote like elements. 
     DETAILED DESCRIPTION 
     Embodiments of the present invention include control methods employed in multiphase distributed energy storage systems that are located behind utility meters typically located at, but not limited to, medium and large commercial and industrial locations. According to such control methods, these multiphase distributed energy storage systems operate semi-autonomously, but may be in frequent contact with a cloud-based optimization engine that is configured to develop energy control solutions based on various data inputs and to communicate these energy control solutions to one or more of the distributed energy storage systems. However, due to characteristics of the electric load location and/or the use of on-site power generation, imbalances in the power draw per phase can be created within the electric load location. These phase imbalances can cause power distribution equipment damage or early failure, as well as transmission and generation efficiency losses. Therefore, embodiments of the invention may include systems and methods that are used to control and/or limit the imbalance in power flowing through one phase versus other phases at the electric load location. 
     Also, an installed multiphase distributed energy storage system can be used to monitor the location&#39;s electricity use on each phase, and discharge at times of high demand peaks in order to reduce the peak power provided by the electric grid and/or any imbalance between power draw on the various phases, while maximizing the finite amount of energy stored in the consumable energy storage components in the distributed energy storage system. To perform this task the distributed energy storage system may control the kilowatt demand of the local electric load location by controlling the control set-point over time by discharging energy sources, such as DC batteries, through multiple bidirectional power converters. These systems generally recharge at times of low demand and/or low electricity rates. From the utility&#39;s perspective, demand spike reduction is more valuable than the act of consuming additional energy during periods of lower demand. 
       FIG. 1  illustrates a plurality of distributed energy storage systems  103  that are each positioned at an electric load location  104  that is coupled to an electrical grid  102 , according to one embodiment of the invention. The electrical grid  102  will generally be connected to one or more electric load locations  104  and one or more power plants  102 A that are adapted to deliver electric power to the electric grid  102 . In general, an electric utility will help provide and/or deliver power to each of the electric load locations  104  in a region of the electric grid  102 . In some cases, the tariff structure, such as electric rates and billing schedules, for different electric utilities may vary from region to region within the electric grid  102 . The distributed energy storage systems  103 , also referred to as advanced energy systems (AESs), are coupled to the electric grid  102 . Consequently, the distributed energy storage systems  103  may be in communication with other distributed energy storage systems  103  distributed along the electric grid  102  and may be in communication with an operations center  109 . The operations center  109  may include software and hardware components that are configured to store, retrieve operation information from, and transmit operation information to each distributed energy storage system  103  to control the power fluctuations and power delivery at each respective electric load location  104 . In some cases, the operation information may include environmental data, control set point information, device commands and other useful information. Distributed energy storage systems  103  in the different regions of the grid are generally able to account for differences in power pricing (including energy tariffs and real-time energy pricing), differences in weather, differences in the health of the electric grid, and other external and internal electric power usage differences to provide an optimized and/or customized control of the power at each electric load location  104 . 
     Embodiments of the invention use a control method employed in the distributed energy storage systems  103  located behind a utility&#39;s electric meter  201  ( FIGS. 2A-2C ) typically located at, but not limited to, medium and large commercial and industrial locations.  FIG. 2A  schematically illustrates one embodiment of a distributed energy storage system  103  that is disposed at an electric load location  104 . The distributed energy storage system  103  may include a power monitor  230 , one or more power controllers  222 , which are coupled to an energy source  224 , and a system controller  210 . The electric load location  104  typically contains an electric meter  201  that is coupled to the electric grid  102  and is used by the utility to track electricity usage at the electric load location  104 . The electric meter  201  is configured to provide power to one or more electric loads that are connected to a breaker panel  240  (e.g., three electric loads  241 A- 241 C are shown in  FIG. 2A ). 
     In one example, the electric meter  201  is configured to distribute power to the electric loads  241 A- 241 C along one or more phases that are each coupled to the breaker panel  240  along a conducting element  235 . In general, an electric load can be any device that uses electrical energy at an electric load location  104 , and may include, for example, heating, ventilation, air conditioning (HVAC) equipment, lighting, and other electronics units that receive power from the electric grid  102 . Each electric load  241 A- 241 C may separately draw power through each conducting element  235 . The amount of power passing through the conducting element  235  is monitored by a sensor  234  disposed in the power monitor  230 . The power monitor  230  will typically include one or more sensors  234  (e.g., voltage sensor and/or current sensor) that are configured to monitor and deliver a signal to a power monitor controller  232  that is configured to process and deliver data relating to the time varying current (A), voltage (V) and/or power (W) delivered on the one or more phases to the system controller  210 , and in some cases time varying current, voltage and/or power data to the operations center  109 . 
     To control fluctuation in power and/or power level being drawn by each of the electric loads  241 A- 241 C in an electric load location  104 , the distributed energy storage system  103  typically includes one or more power controllers  222  that are configured to control the delivery of power to the electric grid  102  or absorption of power received from the electric grid  102  by use of a connected energy source  224 . In one embodiment, the power controllers  222  include one or more bidirectional power converters  225  (shown in  FIGS. 2B and 2C ) that are capable of quickly converting stored DC energy found in the energy source  224  to the grid AC electricity and grid AC electricity back to DC energy that is stored in the energy source  224 . An example of a bidirectional power converter that may be adapted for use with the distributed energy storage systems disclosed herein is further described in the commonly assigned U.S. patent application Ser. No. 12/731,070, which was filed Mar. 24, 2010, which is herein incorporated by reference. 
     The distributed energy storage systems  103  can operate autonomously, but generally may be in frequent contact with a cloud-based optimization engine that may be located in the operations center  109 . The optimization engine  1031 , which is discussed further below, can take in various data inputs and develop optimal energy control solutions which are passed back down to one or more of the distributed energy storage systems  103 . In most cases, the primary goal of the installed distributed energy storage system  103  is to keep kilowatt demand of the electric load location  104  from exceeding certain set-point(s), which may be altered at different times of the day. Simply, this occurs by discharging the energy stored in the energy source  224 , such as energy storage devices that may include DC batteries, through the bidirectional power converter  225  during peak demand events. In some embodiments, the distributed energy storage systems  103  also manage the battery state-of-charge by recharging energy source  224  during periods of lower demand. The state-of-charge represents the amount of energy stored in the storage medium of energy source  224  (e.g., batteries), which can be converted to electrical energy at any time of day, for example during a peak demand event. The distributed energy storage system  103  is generally intelligent enough to ensure that there is adequate energy stored in the energy source  224  to be able to offset at least a portion of high-demand events. Also, the controlling elements (e.g., system controller  210 ) in the energy storage system  103  can be used to prevent the unwanted exhaustion of the stored energy in the energy source  224  during non-high demand times, due to an unscheduled or unforeseen demand during these non-critical and low energy cost times. Thus, employing a control system that is based on predictive data can reduce total energy cost to the customer and/or to help make the wider electric grid cleaner. Therefore, energy storage systems that can predict the occurrence of peak demand events allow the energy storage system to better manage the state-of-charge of the energy storage medium in the energy source  224 , and maximize the amount of time that the energy storage components are available to be used to reduce demand spikes during a day, and especially demand spikes during times of high cost and high-demand on the grid. 
     The system controller  210  typically includes a central processing unit (CPU) (not shown), memory (not shown), and support circuits (or I/O) (not shown). The CPU may be one of any form of computer processor that is used for controlling various system functions and support hardware and monitoring the processes being controlled by and within the distributed energy storage systems  103 . The memory is coupled to the CPU, and may be one or more of a readily available memory, such as random access memory (RAM), read only memory (ROM), floppy disk, hard disk, or any other form of digital storage, local or remote. Software instructions (or computer instructions) and data may be coded and stored within the memory for instructing the CPU. The software instructions may include a program that determines which tasks are to be performed at any instant in time. The support circuits are also connected to the CPU for supporting the processor in a conventional manner. The support circuits may include cache, power supplies, clock circuits, input/output circuitry, subsystems, and the like. The system controller  210  is configured to receive information from and deliver control commands to the source power controller  223  found in the one or more power controllers  222  via a wired or wireless communication link  223 A. The system controller  210  is also configured to receive information from the power monitor  230  via a wired or wireless link  209  and from the operations center  109  via a wired or wireless link  109 A. 
     In one embodiment, the system controller  210  also includes a plurality of software based controlling elements that are adapted to synchronize and control the transfer of power between the conducting element  235  that is coupled to the electric grid  102  based on computer instructions retained in the memory of the system controller  210 . The software based controlling elements found in the system controller  210  include a solution manager  202 , a set point controller  204 , and a run time controller  206 . In some embodiments, the software based controlling elements may also include an optional offset controller  208  and/or an local power controller  207 . 
       FIG. 2B  schematically illustrates two electric load locations  104  that are each coupled to the electric grid  102  through an electric meter  201  and include a distributed energy storage system  103 . In this example, each of the distributed energy storage systems  103  are in communication with each other through a link  109 B. In one embodiment, the link  109 B is created using wired or wireless communication components found in the power monitor controller  232 , so that the control between electric load locations  104  can be coordinated. For clarity, the electric loads  241 A- 241 C are not show in the distributed energy storage systems  103  of  FIG. 2B . 
     In one embodiment, as illustrated in  FIG. 2B , the distributed energy storage systems  103  each include one or more power controllers  222  that are coupled to an energy source  224  and include a source power controller  223  and a bidirectional power converter  225 . The energy source  224  may include one or more batteries  227  that may be coupled in series, so as to provide a desirable output voltage (Volts) to the bidirectional power converter  225  and provide a desirable storage capacity (Amp-hrs). The bidirectional power converter  225  may include one or more software and/or hardware components (e.g., bridge rectifiers, power transistors, transformers, resistors, capacitors, diodes) that are capable of controlling the delivery and absorption of power from the electric grid  102 . In some embodiments, the bidirectional power converter  225  may include components that are able to separately deliver power to or receive power from a conducting element  235  that is separately connected to a phase that is coupled to the electric grid  102 . In such embodiments, the energy source  224  may include a plurality of separate battery arrays (not shown) that are each coupled to a separate power controlling circuit in the bidirectional power converter  225  to control the efficient transfer of power at a desirable rate between the conducting element  235  and energy source  224 . Such battery arrays may include two or more batteries  227  each. 
       FIG. 2C  schematically illustrates a single electric load location  104  that is coupled to the electric grid  102  through two electric meters  201 , according to one embodiment of the invention. Each of the electric meters  201  are configured to separately or cooperatively provide power billing information to the utility, due to the electric power drawn by the loads  241 A- 241 F. As illustrated in  FIG. 2C , a single distributed energy storage system  103  can be configured to control the fluctuation in power at the electric load location  104  and/or power level being drawn by a plurality of electric loads (e.g., electric loads  241 A- 241 F) at the electric load location  104 . In this configuration, the distributed energy storage systems  103  may include two or more power monitors  230 , one or more power controllers  222  and a system controller  210 . The bidirectional power converter  225  may also include components that are able to separately control the transfer of power between a conducting element  235 , electric grid  102 , and the energy source  224 . 
       FIG. 3  illustrates a process flow diagram for controlling the fluctuation of power at an electric load location  104  and/or power level being drawn by one or more electric load(s) at the electric load location  104  by use of a distributed energy storage system  103 , according to one embodiment of the invention. In general, the software and hardware components in the system controller  210  (shown in  FIGS. 2A-2C ) are configured to provide control-based commands to control the one or more power controllers  222  based on the measured power data collected by power monitors  230 . The overall control system of the distributed energy storage system  103  is made up of several software and hardware components that may be distributed across the local site (e.g., electric load location  104 ) and/or the Internet cloud.  FIGS. 2A-2C  discussed above illustrate some examples of different configurations of these components. The electric load location  104  contains hardware responsible for implementing the control system, including charging the batteries  227  from and discharging the batteries into the electric grid  102 . Monitoring capability may be included in the bi-directional converter in the source power controller  223  that passes the measured charge and discharge information back to the system controller  210  in the local distributed energy storage system  103 . Additionally, there may be a separate sensor (e.g., a monitoring device, also referred to herein as the power monitor  230 ) that monitors the overall building load (that is, the net of any charging or discharging) and also passes this information back to the system controller  210  in the distributed energy storage system  103 . 
     The system controller  210  of the distributed energy storage system  103  may include up to six primarily software-based controllers that are stored within memory and executed by one or more processors associated with the system controller  210 . As noted above, these six primarily software-based controllers may include the solution manger  202 , the set-point controller  204  (described below in conjunction with  FIG. 4 ), the run time controller  206  (described below in conjunction with  FIG. 5 ), the local power controller  207 , the offset controller  208 , a droop controller  330 , and an optimization engine  1031  (described below in conjunction with  FIGS. 7 and 9 ). The various controllers in the control system, as noted above, will typically include a central processing unit (CPU) (not shown), memory (not shown), and support circuits (or I/O) (not shown). Software instructions and data can be coded and stored within the memory of one or more of the controllers and is used for instructing the CPU of the one or more of the controllers to control the various components in the distributed energy storage system  103 . 
     The solution manager  202  exists on the local premise and is primarily responsible for communicating with the optimization engine  1031  and passing command information received therefrom to the set-point controller  204  and offset controller  208 . In one embodiment, the solution manager  202  receives demand threshold control instructions (e.g., demand set-point curves  301 ) and battery state-of-charge curves  302  from the optimization engine  1031  and then determines at what time(s) of day the set-point controller  204  changes the demand set-point. For example, if during a period of low demand charges (e.g., energy cost is low), the solution manager  202  receives new information that a high demand charge period (e.g., energy cost is high) is approaching, the solution manager  202  passes along the new control set-point to which the set-point controller  204  should attempt to hold the premise load at that time. There are several events the solution manager  202  watches for in order to make changes to the set-point, including a soft failure event, a soft failure set point increment event, a manual set-point change event, a new solution received set-point update event, a set-point controller startup event, and a PID coefficient change event. 
     A soft failure event indicates that operating conditions of the distributed energy storage system  103  have gone outside the forecasted operating solution, or the demand has exceeded its predicted operating value(s) due to an unexpected variation in demand. This type of event is typically informational and allows the optimization engine  1031  to decide if intervention is required. A soft failure set point increment event, which can be either incremental or proportional, indicates whether a set point change is needed as a result of a soft failure event. Set point modifications are either incremental or proportional to the set point and utility and site loads. If the solution manager  202  decides that the correction to the soft failure should be incremental, then the set point will be adjusted upward based on the incremental value found in the forecasted battery curve layer for the current state-of-charge (SOC). If the solution manager  202  decides that the correction to the soft failure should be proportional, then the set point will be adjusted up or down based on the proportional difference between the utility and resulting load. Much like the incremental value, the proportional value used to correct for the soft failure event is found in the battery curve layer of the controlling software found in the solution manager  202  and is based on the current state-of-charge of the energy source  224 . A manual set-point change event is recorded when a user manually changes the set point on the distributed energy storage system  103 . A new solution received set-point update event indicates the set point is updated based on a newly received solution from the optimization engine  1031 . This will generally occur when the distributed energy storage system  103  transitions from one billing cycle to another or when intervention by the user causes a regeneration of a new solution. A set-point controller startup event indicates that the set-point controller  204  was restarted, generally due to manual intervention of a user or when the distributed energy storage system  103  is restarted. A PID coefficient change event indicates that the PID coefficients, used in the run time controller  206 , have been updated, for example to accommodate dynamic battery state-of-health (SOH) strategies. 
     The set-point controller  204  is used to manage the control set-point for each instant in time using information specified and received from the solution manager  202 . As noted above, the control set-points are selected by the solution manager  202  to ensure that the system maintains enough energy reserve in the energy source  224  to manage the load at the electric load location  104  over a particular time period, such as a day or part of a day.  FIG. 4  illustrates operation of set-point controller  204 , according to one or more embodiments of the invention. 
     As shown in  FIG. 4 , in some embodiments, the set-point controller  204  receives the optimized runtime parameters  401  from the solution manager  202  and demand set-point curves  301  and battery state-of-charge profiles  302  from optimization engine  1031 . Simultaneously, set-point controller  204  monitors the actual operating characteristics of energy storage system  103 , including for example information  402  from power monitor  230  and battery telemetry  403  (e.g., battery SOC as a function of time) from power controller  222 , and sends updated runtime parameters  410  (e.g., set-points, PID parameters) to the runtime controller  206 . In some embodiments, set-point controller  204  also monitors transport delay information and other inverter telemetry  404  (e.g., power delivery information) from bidirectional power converters  225  or any other inverter/chargers associated with energy storage system  103 . 
     The updated control parameters  410  are based on the received optimized operating parameters and/or the current operating state of the measured power being drawn by the attached electric loads at the energy storage system  103 . The set-point controller  204  does this by receiving current real-time demand information from the observed site load monitor (e.g., power monitors  230 ), receiving real time battery state-of-charge information directly from hardware monitoring components (e.g., charge/discharge monitor  323 ), which may be found in the source power controller  223  in the power controllers  222 , receiving battery telemetry  403  (e.g., real time charge and discharge information from the charge/discharge monitor  323 ), and then issuing commands (updated runtime parameters  410 ) to the run-time controller  206 . The run-time controller  206  controls the charge or discharge of energy to or from the energy source  224  and to or from the electric grid  102 , via the bi-directional power converter  225 . 
     If the actual operating characteristics of the distributed energy storage system  103  fall outside of the expected operating parameters, the set-point controller  204  will adjust the updated runtime parameters  410  to correct for any variance from the actual or expected performance of the overall system. In some embodiments, set-point controller  204  is also configured to pass any variance information  411  back to the optimization engine  1031  so that the optimization engine  1031  can continue to run the most accurate simulations and issue newly optimized operating parameters. 
     The runtime controller  206  uses the updated runtime parameters  410  received from the set-point controller  204  to implement an optimized charging or discharging solution for the energy storage system  103 .  FIG. 5  illustrates the operation of the runtime controller  206  according to one or more embodiments of the invention. As shown, the runtime controller  206  generally receives the updated runtime parameters  410  from the set-point controller  204  and uses a feedback controller (e.g., local power controller  207 ), such as a PID controller, to implement the optimized charging or discharging solution determined by the set-point controller  204  utilizing the power electronics hardware in the bidirectional power converter  225 . The runtime controller  206  acts on the current demand set-point data, which may be included in updated runtime parameters  410 , and sends the actual charge and discharge instructions  501  to the bidirectional power converter  225  using standard control mechanisms, such as proportional integral and derivative (PID) types of control loops. The data received by runtime controller  206  may include the current demand set-point curves  301  generated by the optimization engine  1031  and/or solution manager  202 . The runtime controller  206  then compares the received inputs and supplies a control signal (charge and discharge instructions  501 ) to the bidirectional power converter  225 , so that a desired amount of energy is received or discharged at that instant in time. 
     Based on charge and discharge instructions  501 , fluctuations in the power used by the electric load location  104  can be controlled or damped by the charging and discharging of the energy source  224 , such as a battery array in the energy source  224 , that is coupled to the power-receiving portion of the electric load location  104 . The energy source  224  may be configured to deliver and/or receive an amount of energy at any instant in time to and/or from the A/C power system of the electric load location  104 . The energy source  224  may include a plurality of batteries (e.g., lithium ion batteries, lead acid batteries, etc.) that are electrically coupled to a portion of the A/C power system of the electric load location  104 . The plurality of batteries may be connected in a series and/or a parallel configuration to the A/C power system. The charging and discharging of the energy source  224  can be controlled by use of the power electronics hardware in the bidirectional power converter  225 , which may include A/C switches, diodes, capacitors and/or inductors. In some embodiments, the runtime controller  206  is configured to constantly send the actual charge and discharge instructions  501  to the power controllers  222  while monitoring charging and discharging behavior of the energy source(s)  224  in a closed control loop using a charge/discharge monitor, such as the charge/discharge monitor  323  in  FIG. 3 . 
     The energy source  323  includes one or more sensors that are adapted to monitor the state-of-charge of one or more energy source components, such as batteries, found within each energy source  224 . In one example, the energy source  323  is able monitor the state-of-charge of each battery array and/or each battery within a battery array to determine its relative health and state-of-charge (e.g., amount of energy stored in the batteries). In some embodiments, the charge/discharge monitor  323  is configured to deliver charging and discharging behavior information (e.g., battery telemetry data) to the runtime controller  206  and set-point controller  204 , so that the current demand set-point data used by the runtime controller  206  can be updated and the set-point at each instant in time is the better managed using commands sent from the set-point controller  204 . 
     The local power controller  207  is used to execute the commands that are received from the runtime controller  206 , and thus execute the actual charge and discharge processes controlled by the components in the bidirectional power converter  225 . The local power controller  207  may include various standard control mechanisms, such as PID control loops, and may be run using a processor found within the system controller  210  ( FIG. 2A ) or power controllers  222  ( FIG. 3 ). 
     The optional offset controller  208  is used to execute grid services commands that are received by the electric load location  104 , and is used as a higher-level system override of the control provided by the system controller  210 . The offset controller  208  may be configured to modify the charge or discharge command to the bidirectional power converter  225  and the telemetry information, such as current load, from the resultant load monitor (e.g., sensor power monitors  230 ). The offset controller  208  thus adjusts the power absorbed or delivered to the electrical grid  102  by the energy source  224  based on instructions received by the system controller  210  to help resolve electrical issues that may be occurring on the grid  102  at that time (e.g., voltage support, frequency regulation, etc.). For example, if the solution manager  202  calls for a grid services event that calls for an additional 2 kW of power, the offset controller  208  would modify the discharge command from the runtime controller  206  to increase by 2 kW of power, but would also add that 2 kW of power back to the telemetry of the power monitors  230  so that the set-point controller  204  and the optimization engine  1031  would not see a deviation from the predicted load and thereby not disturbing the optimized instruction set that is currently being executed. 
     The droop controller  330  takes frequency measurements from a sensor  310  and a device  331  that are coupled to the electric grid  102  via a wired or wireless communication link  311 . This frequency is compared to the nominal grid frequency for the operating region, for example in the United States it would be compared to 60,000 Hz. When commanded to be in frequency regulation mode from the distributed system gateway  325  ( FIG. 3 ), if the frequency of the grid is below the nominal frequency the droop controller  330  will adjust the offset controller  208  (as shown in  FIG. 3 ) to so that commands to the energy source  224  are altered to increase the discharge rate. Alternatively, when the grid frequency is measured to be greater than 60,000 Hz the commands to the energy source  224  can be altered to store energy at a greater rate than required for local demand reduction. The response characteristics, such as the speed and magnitude of the response, are parameters that are controlled by the distributed system gateway  325 . The distributed system gateway  325  can be a cloud-based interface and controller that allows for specific grid services commands to be sent to the distributed energy storage system  103  outside of the control of optimization engine  1031 . Thus, the droop controller  330 , much like the optional offset controller  208 , can be used as a higher-level system override of the control provided by the system controller  210 . 
     In operation, the control method used to maintain the required demand set-point(s) can employ a proportional-integral-derivative (PID) controller that heavily relies on the information received from an optimization engine  1031 . In some embodiments, the control method uses a standard PID control loop, where the process value is the observed electric load at the local premise, the set-point is the demand in kilowatts that the storage system attempts to prevent the local load from exceeding, and the manipulated value is the amount of energy discharged by the bidirectional power converters  225 . In simple terms, if the local load is sensed or observed to exceed the demand set-point, then the bidirectional power converter  225  receives a command to discharge until the observed electric load begins to drop below the demand set-point. This control loop may occur at a frequency from 100 milliseconds or less to multiple seconds depending on load and volatility, and on the granularity of the control output of the bidirectional power converter  225 . For example, said frequency may vary as a function of the ability of the bidirectional power converter  225  to control output, e.g., within 10&#39;s of watts, hundreds of watts, etc. Given this control output granularity and speed, the control system  210  can react within a suitable response time to sensed changes in the electric load sensed by power monitor  230 . 
     In some configurations, the distributed energy storage system  103  is computing resource constrained. Therefore, the generation of the control optimization instructions occurs at an external location (e.g., operations center  109 ) and then a distilled set of instructions is passed down to one or more of energy storage systems  103  from the external location. The predictive nature and/or processes found in the control optimization instructions is particularly beneficial, since these control optimization instructions enable a minimized or otherwise reduced amount of energy storage to be used at the premise. Since the energy storage devices, such as the energy source  224 , can be expensive and have a finite lifetime that is affected by amount of use, the system controller  210  can be used to further improve the lifetime of the energy source  224  and thus reduce the operating costs of distributed energy storage system  103 . For example, if the optimization engine  1031  forecasts that the maximum peak load for the day will occur at 4 PM, the instruction set includes this forecast, so that the distributed energy storage system  103  does not overreact to spikes in demand prior to 4 PM, and therefore enough energy capacity will remain in the energy source  224  for the major 4 PM event. If an unpredicted event occurs prior to 4 PM, the distributed energy storage system  103  will still react correctly and hold the set-point. This unexpected event information is sent back to the optimization engine  1031 , where an updated set of instructions is created for the storage system, which may include a higher set-point later in the day so the energy source  224 &#39;s state-of-charge is not depleted. 
     According to embodiments of the invention, distributed energy storage system  103  maximizes economic return by optimally managing energy use at the electric load location  104 . Rather than simply reacting to gross changes in electricity demand, distributed energy storage system  103  is configured to use a partially predicted command set that may be statistically derived from a large quantity of various data inputs. In practice, some embodiments of distributed energy storage system  103  may lack the processing power, data storage capacity, and/or interfaces to remote data stores that facilitate calculating such a partially predicted command set locally in a timely manner. Consequently, in such embodiments, the partially predicted command set may be calculated at a remote location and the information distilled to a lightweight, i.e., easily transmitted, set of instructions that the distributed storage system  103  can utilize in real-time. For example, in some embodiments, optimization engine  1031  is configured to calculate the command set at such a remote location (e.g., operations center  109  in  FIGS. 2A-2C ) and then deliver the command set to the energy storage system  103 . 
     Additionally, in some embodiments, the created command set may include a predictive element, as opposed to simply a fixed set point, so that the distributed energy storage system  103  can operate autonomously for some time (e.g., hours or days) in the event that the distributed energy storage system  103  is disconnected from the central controller and is unable to communicate with a central command center (e.g., operations center  109 ). For example, disconnection can occur if the local communications network is disconnected for maintenance or if damage occurs to the communication wiring. In such a situation, the predictive element of the command set facilitates resilient operation of distributed energy storage system  103  with respect to various future conditions, such as demand spikes or changes in rate schedule. Thus, in some embodiments, distributed energy storage system  103  is configured to operate using a robust, but small (in terms of data size) command set that is received from a central command center (e.g., from optimization engine  1031  at operations center  109 ) and that allows autonomous operation (via a predictive element) until communications are reconnected with the central command center. In other words, embodiments of the invention can be used to create a concise set of operating parameters in an external data center that the distributed energy storage system  103  can understand and implement while the distributed energy storage system  103  is either connected or disconnected from the communications network, which couples the external data center and the distributed energy storage system  103 . These operating parameters include a statistically generated and tiered expected state-of-charge of the battery or batteries in the distributed energy storage system  103  (e.g., the battery state-of-charge profiles  302  in  FIGS. 4 and 5 ) as well as a set of set points at which to maintain the electric power demand at the load site to enable the forecasted economic return (e.g., the time-based demand set-point curves  301  in  FIGS. 4 and 5 ). Examples of the battery state-of-charge profiles  302  and their uses are further discussed below in conjunction with  FIGS. 10-14 and 15A-15C . 
     Thus, in some embodiments, a process is provided in which operating parameters are computed through simulations and other statistical techniques in a remote data center. These data are then distilled down into a lightweight set of storage system operating parameters (e.g., in the several kilobytes size range), which are acted upon by the controlling elements in the energy storage system  103 , and then any changes in operating characteristics are communicated back to the central data center (e.g., operations center  109 ) for continual operating parameter optimization. 
     The optimization engine  1031  may exist in the cloud  104 A, as shown in  FIG. 3 , or alternatively at an electric load location  104 , and builds the optimal operating instructions for one or more of the distributed energy storage systems  103  by running simulations using multiple categories of data, which may include: electric rate plan and tariff information (e.g., utility rates, billing period, temporal energy and demand prices, and averaging periods), utility data (e.g., requests for grid services like voltage support and frequency regulation), current and future weather data, (e.g., time of day, sunrise and sunset times, temperature, cloud cover, and other weather forecasts), geographic location, local solar production, local incident light, customer type, building specifications (e.g., information relating to the electric load location, such as building material, square footage, age, HVAC type and type of equipment used at the electric load location, etc.), grid operator data (e.g., locational marginal price, area control error, automatic generation control, etc.), and time (e.g., time of day) data, high-resolution energy usage data (e.g., collected high sample rate energy usage data (e.g., &lt;1 minute)) provided by the target energy storage system, and/or load prediction taken from historical patterns for the specific site and similar sites, which may be received from one or more external data sources. Optimization engine  1031 , which may be configured as a software-based analytics engine, then uses this data to simulate and create various operating parameter results by iteratively processing this data to find an optimal solution. The operating parameter results so determined may include optimized runtime parameters  401 , demand set-point curves  301 , and battery state-of-charge profiles  302  of  FIGS. 4 and 5 . For example, in some embodiments, optimization engine  1031  uses an energy storage hardware simulator farm as illustrated in  FIG. 6  to help determine and create various operating parameters. 
       FIG. 6  illustrates an overview of a communication process between an optimization engine  1031 , a simulator farm  601 , and a distributed energy storage system  103 , according to an embodiment of the invention. The simulation farm  601  may be located in a data center external to the distributed energy storage systems  103  and, as shown, may be configured with a plurality of system simulators  602  to run multiple simulations using the various categories of data listed above as input variables. Each simulation can then produce operating parameters and storage system results (e.g., optimized runtime parameters  401 , demand set-point curves  301  and battery state-of-charge profiles  302 ) for a particular distributed energy storage system  103 , including energy use predictions that are used by the semi-autonomous control system of each distributed energy storage system  103 . Simulator farm  601  may be hosted in the same cloud-based system or computer as the optimization engine  1031 , or may be associated with a dedicated cloud or computer or other computing device. 
     After completing a simulation to compute the optimal power control strategy for a distributed energy storage system  103 , the optimization engine  1031  then packages and passes down an array of time-based demand set-point curves  301 , battery state-of-charge profiles  302 , and optimized runtime parameters  401  to the set-point controller  204  of the distributed energy storage system  103 , which then acts upon them. Demand set-point curves  301  provide a series of time-based power level demand set-points, so the distributed energy storage system  103  knows what kilowatt set-point to hold the local load(s) (e.g., electric load(s)  241 A- 241 C in  FIGS. 2A-2C ) to during different times of day. The battery state-of-charge profile  302  provides a set of zones that the set-point controller  204  will act on if the current state-of-charge moves out of the safe zone at different times over a desired time period (e.g., a day). The system controller  210  uses this information so that the power control strategy can be adjusted if the battery state-of-charge moves outside a predetermined safe state-of-charge level, due to unpredicted changes in the load at the electric load location  104 . 
     The strategy computed by the optimization engine  1031  may also include grid services instructions that control the offset controller  208  via the solution manager  202 . Grid services may include frequency regulation, voltage support, and demand response commands, which typically do not disrupt the control loop that is optimizing the power provided to local premise load(s), and are used to allow the distributed energy storage system  103  to help support and resolve issues that arise on the greater electrical grid, which is outside of the commercial electric load location  104 . 
     As described above in conjunction with  FIGS. 2A-2C , the set-point controller  204  unpackages the operating parameters and uses this data to determine a runtime plan, e.g., updated runtime parameters  410 , as a guideline for operation of distributed energy storage system  103 . In some embodiments, set-point controller  204  may also be configured to report back to the optimization engine  1031  any variances in expected behavior using runtime variance  610  data, based on the actual behavior of distributed energy storage system  103 , e.g., system telemetry  620 . The optimization engine  1031  can then use runtime variance  610  to run another set of simulations using one or more system simulators  602  in order to compute a new optimal solution that accounts for these variations at each energy storage system  103 . 
       FIG. 7  illustrates an overview of a system simulator process, according to an embodiment of the invention. An individual system simulator  602  may be configured to behave exactly like a particular distributed energy storage system  103 , but is entirely software-based. A system simulator  602  takes in the operating parameters produced by the optimization engine  1031  as well as the utility&#39;s electric usage billing period and passes this information into a feedback controller that simulates the control loop of a particular distributed energy storage system  103 . In one embodiment, the system simulator  602  generates data in sub-second or greater increments. However, the utility that delivers the electrical power typically measures for demand peaks in longer multi-minute time blocks. Therefore, in some embodiments, the simulation time period optimizations are matched to the site&#39;s utility billing time periods. If the operating parameters are optimized for a time significantly shorter than the utility billing time period associated with a particular distributed energy storage system  103  being simulated, more computational resources than necessary may be used to reach a desired solution. Alternatively, if the operating parameters are optimized for a time significantly longer than the utility billing time period associated with a particular distributed energy storage system  103  being simulated, a demand spike may be missed. 
     As noted above, optimization engine  1031  can be used to create a concise set of operating parameters (including time-based demand set-point curves  301 , battery state-of-charge profiles  302 , and optimized runtime parameters  401 ) that the distributed energy storage system  103  can understand and implement. Furthermore, these operating parameters can be used by the distributed energy storage system  103  even if subsequently disconnected from the communications network coupling the optimization engine  1031  and the distributed energy storage system  103 . The system controller  210  of the distributed energy storage system  103  uses the time-based demand set-point curves  301  and battery state-of-charge profiles  302 , collectively known as feedback based curves, to control the storage and release of energy by the distributed energy storage system  103 . These battery state-of-charge profiles, or battery curves, can be used for a single battery, a group of connected batteries in a single storage device, or a group of batteries contained in several networked energy devices, such as in the energy source  224 . A battery state-of-charge profile  302  represents a targeted state-of-charge of the battery or battery array as a function of time and will vary in the amount of power correction from 0% to 100%. A time-based demand set-point curve  301  represents one or more peak demands that the site should not exceed over the course of a specified time period, e.g., one day. There may be multiple such demand peaks during a particular day, and each may be defined by power as a function of time of day. The battery state-of-charge profile, or battery curve, can be used by the system controller to determine an amount of energy to transfer to the electric line (e.g., conducting element  235 ) based on a measured amount of charge in the energy source  224 . 
     The time-based demand set-point curves  301  and battery state-of-charge profiles  302  can be transmitted either as a matrix or table of time-indexed values, or as a time-varying equation. The points defining time-based demand set-point curves  301  and battery state-of-charge profiles  302  are synchronized in time and are time-delineated in relatively short time increments (such as one minute intervals). The matrix or equation that allows the system controller  210  to build the battery state-of-charge profiles  302  may be in a format of battery state-of-charge percentage with respect to time. Such a matrix or equation allows the system controller  210  to build the demand set-point curve, which comprises electric power (in kilowatts, for example) with respect to time. 
     During operation, the system controller  210  attempts to maintain the given set-points as time progresses by monitoring power delivered to and from the electric load location  104  through a power delivery line (e.g., one or more conducting elements  235 ) coupled to electric meter  201 , and storing and releasing energy to the delivery line from a storage device, such as the energy source  224 . The battery state-of-charge profiles  302  received by the system controller  210  provides a guide for what state-of-charge is expected to be for the distributed energy storage system  103  in order to maintain the demand set-points. If the actual state-of-charge deviates significantly from the expected state-of-charge, the system controller  210  will adjust the demand set-point and report the variance back to the optimization engine  1031 . 
     According to some embodiments, a set of operating parameters provided to distributed energy storage system  103  by optimization engine  1031  may include more than a single demand set-point curve  301  and a single battery state-of-charge profile  302  (or battery curves). In some embodiments, these operating parameters may includes a matrix of synchronized battery and demand set-point data, including “default,” “optimized,” and “current” curve sets. The simplified default curve set is calculated at system installation and initial setup of system controller  210 . This set includes a simple demand set-point curve  301  and a simple battery state-of-charge profile  302  that can be used by the system controller  210 . The optimized battery curve set is calculated by the optimization engine  1031  at the beginning of the billing period (e.g., once per month) or when changes occur in energy and power utility rates. Furthermore, each curve in the optimized curve set may include multiple continuous or discrete value set-point curves for a 24-hour period as well as a higher resolution state-of-charge curves that can be used by the system controller  210  to control the distributed energy system  103 . The current curve set uses operational feedback from the energy source  224  as well as real-time external data (such as local weather conditions) to continually optimize and refine the operation of the distributed energy storage system  103 . The current curve set can be generated by components in the operation center  109 , and/or, in some cases, locally by the system controller  210 . 
     If communication is lost to optimization engine  1031 , the distributed energy storage system  103  uses the optimized curve set for as long as feasible, and, if an unpredicted event occurs, can revert back to a set of default parameters set by the default curve. If demand at the distributed energy storage system  103  begins to push the state-of-charge beyond the capability of energy source  224  to maintain the current set-point, the distributed energy storage system  103  adjusts the set-point until the desired state-of-charge can be maintained based on pre-computed back-off values. In some embodiments, distributed energy storage system  103  is configured to use the optimized curve set when a short-term communication loss occurs (e.g., hours or days) and the default curve set when a long-term communication loss occurs (e.g., several weeks). 
     Having several sets of curves provides the distributed energy storage system  103  with maximum flexibility in the case of abnormal demand conditions and/or loss of communication with the optimization engine  1031 . If the distributed energy storage system  103  loses communication and has only one un-optimized demand set-point curve  301  and one un-optimized battery state-of-charge profile  302  available, the distributed energy storage system  103  can potentially run out of stored energy and fail to meet the scheduled requirements. This is because un-optimized operational data does not benefit from the ability of the optimization engine  1031  to “learn” and forecast how a particular electric load location  104  uses energy. However, in embodiments in which multiple demand set-point curves  301  (e.g., battery curves) and/or battery state-of-charge profile  302  are available to the distributed energy storage system  103 , the current data set is the best optimized data set and is typically used instead of the default or optimized data sets. Moreover, having multiple demand set-point curves  301  and/or battery state-of-charge profiles  302  available in this way advantageously includes inherent error checking. Specifically, if load conditions fall out of range of the latest set of data, the distributed energy storage system  103  may request a new set of battery curves and demand set-point data from the central data center. 
       FIG. 8A  illustrates a graph  800  that shows the performance over a business day of a conventional (prior art) energy storage system using a single-demand set-point. Graph  800  includes a battery charge level curve, which illustrates the amount of charge contained in the batteries, a site load curve, a net load curve, and a single demand set-point having a fixed value of 40 kW. The battery charge level curve indicates percentage charge remaining in the battery array over time of an energy storage system located at an electrical load location, such as a commercial building. The site load curve indicates a varying electrical load over time of the electrical load location, and the net load curve indicates a varying net electrical load over time of the electrical load location, i.e., the quantity of electrical energy actually delivered to the electrical load location from an electrical grid. Thus, at any point in time in graph  800 , the value indicated by the net load curve is equal to the corresponding site load value minus any energy provided to the electrical load location by the conventional energy storage system. 
     At the beginning of a business day, for example at 7 a.m., the battery array of the energy storage system is fully charged (100%) and, over time the charge drops as energy is provided to the electrical load location by the battery array. Because the single demand set-point is fixed at 40 kW, the energy storage system provides electrical energy to the electrical load location at whatever rate that prevents the net load of the electrical load location from exceeding 40 kW. In this way, additional tariffs associated with receiving energy at a rate of greater than 40 kW are avoided. However, because the battery array of the energy storage system has very limited capacity, the battery array is quickly discharged to essentially 0% charge only a few hours after the site load exceeds the single demand set-point. In the example illustrated in  FIG. 8A , the battery array is fully discharged by 10 a.m., which is less than 2.5 hours after the site load initially exceeds the single demand set-point of 40 kW. Consequently, any demand spikes that occur after that time (10 am) cannot be reduced by the conventional energy storage system. Because several such spikes may occur later in the business day in a commercial building, as shown in  FIG. 8A , this is highly undesirable, since tariffs associated with receiving energy at a rate greater than 80 kW and 100 kW are applied to the electrical load location on the business day represented by  FIG. 8A . 
       FIG. 8B  illustrates a graph  850  that shows the performance over a business day of an energy storage system configured according to one or more embodiments of the invention. Graph  850  includes a battery charge level curve, a site load curve, a net load curve, and a demand set-point curve having various values at different times of the business day. The battery charge level curve, the site load curve, and the net load curve have the same definitions provided with respect to graph  800  in  FIG. 8A . In contrast to graph  800 , graph  850  includes a demand set-point curve that is adapted to vary with time over the course of a single day, as shown in  FIG. 8B . This demand set-point curve (analogous to the above-described demand set-point curves  301 ) may be provided by a software-based analytics engines, such as the optimization engine  1031 , and therefore can be tailored specifically to the charging and discharging behavior of the electrical load location  104 . The demand set-point curve illustrated in graph  850  is a stepped set-point curve that includes multiple discrete values. In one configuration, as illustrated in  FIG. 8B , the stepped set-point curve includes multiple periods of time, of variable length, in which the set-point is maintained at a constant level. 
       FIG. 8C  illustrates a graph  860  that shows the performance over a business day of an energy storage system configured according to one or more embodiments of the invention. Graph  860  includes a battery charge level curve, a site load curve, a net load curve, and a continuously varying demand set-point curve that varies continuously with time over the course of a particular time period, such as a business day. The battery charge level curve, the site load curve, and the net load curve have the same definitions provided with respect to graph  800  in  FIG. 8A . This continuously varying demand set-point curve may be provided by a software-based analytics engines, such as the optimization engine  1031 , and therefore can be tailored specifically to the charging and discharging behavior of the electrical load location  104 . 
     According to some embodiments, an energy storage system associated with graph  850  and/or graph  860  is configured to maintain a current demand set-point indicated by the demand set-point curve by monitoring the load of the electric load location and releasing a suitable quantity of energy to the electric load location at different times of the day. The demand set-point curve, which provides a demand set-point for each particular time of day, varies with time and is selected by the set-point controller  204  and delivered to and used by the system controller  210 . Consequently, the control parameters created by the optimization engine  1031  are used to create the demand set-point curve that takes into account demand spikes that can occur later in the business day. For example, the demand set-points for times earlier in a business day may be selected or adjusted to conserve energy in the energy storage system  224  to allow demand spikes that occur later in the business day to be negated. Thus, the energy storage system discharges to keep demand at the electrical load location  104  from exceeding these various adjusted set-points. In this way, charges associated with demand spikes can be avoided, even when such demand spikes occur later in a business day. 
     Furthermore, in some embodiments, the rate at which energy is delivered to the electric load location by the energy storage system  103  is also managed according to a target battery state-of-charge curve received by the solution manager  202 , such as the above-described battery state-of-charge curve  302 . A target battery state-of-charge curve indicates what state-of-charge at any time of the business day is predicted to allow the demand set-points of the demand set-point curve to be maintained. In such embodiments, when an actual or measured charge of the energy storage system falls below a specified value in the target battery state-of-charge curve, a new demand set-point curve and/or target battery state-of-charge curve may be requested from the optimization engine  1031 . Various embodiments of target battery state-of-charge curves are described herein in conjunction with  FIGS. 15A-15C . 
     Thus, in contrast to conventional energy storage systems that use a fixed demand set-point, embodiments of the invention facilitate maximizing the finite energy storage capacity of an energy storage system and reducing the unnecessary usage of such systems during less beneficial times of the day. For example, by use of the optimized solutions provided by the optimization engine, such as including a relatively high target battery state-of-charge earlier in the day, energy in an energy storage system is reserved for preventing more costly demand peaks that occur later in the day. One will note, by use of the control techniques described herein, and as illustrated in  FIG. 8B , the state-of-charge of the energy source  224  can be controlled such that the battery charge level curve does not reach a 0% state-of-charge even when demand management becomes increasingly challenged due to increasing load. 
     As noted above, operating parameters such as optimized runtime parameters  401 , demand set-point curves  301 , and battery state-of-charge profiles  302 , are computed through simulations and other statistical techniques.  FIG. 9  illustrates a control system  900  that includes an optimization engine  1031  and is configured to generate operating parameters for an energy storage system controller  210 , according to one embodiment of the invention. 
     As shown, control system  900  is coupled to a system controller  210  and includes an optimization engine  1031 , a coefficient engine  1032 , data cleansing engines  1033  and  1037 , a forecast engine  1034 , a simulation engine  1035 , a solution engine  1036 , a utility tariff engine  1039 , a universal tariff data store  1041 , an external data store  1042 , a site telemetry data store  1043  and a risk management engine  1049 . 
     Optimization engine  1031  may be configured as a cloud-based or local computing environment designed to compute forecasted solutions for all distributed energy storage systems  103  associated with control system  900 . Generally, a solution may include a series of set point arrays (e.g., demand set-point curves  301 ) as well as forecasted battery curves (e.g., battery state-of-charge curves  302 ) to enable each distributed energy storage system  103  to optimize battery usage based on real-time site load at each electric load location  104  served. These set point arrays may contain multiple sets of time-based set-points, to accommodate daily changes in the utility tariff periods that may occur. For example, one set of set-points may be selected to maximize demand charge savings for a weekday, while another set of set-points may be selected to maximize demand charge savings for a weekend or holiday. The values of the set-points may vary from one type of load location  104  to another (e.g., hotels, car wash, house, apartments) and thus may be configured and controlled by the optimization engine  1031 . Each battery curve is a set of time-based battery state-of-charge zones. If the local storage medium state-of-charge is monitored to drop into or below a certain zone in the battery curve, then the set-point is adjusted in desired increments to limit the discharging (or increase the charging) of the storage medium. Examples of the controlling components in the optimization engine that are used to compute forecasted solutions are discussed below. 
     The forecast engine  1034  is responsible for generating forward-looking forecasted load profiles (e.g., power usage as a function of time) for a given site. To generate the forecast for each of the distributed energy storage systems  103  associated with the control system  900 , the forecast engine  1034  gathers all historical information as well as the latest weather and site-specific attributes. The forecast engine  1034  may be configured to generate forecast for a distributed energy storage system  103  periodically and/or whenever the distributed energy storage system  103  requests a forecast for a specific time, such as a remaining portion of a business day in which the current charge in the distributed energy storage system  103  outside a desired range. 
     The simulation engine  1035  can be configured as a model of each particular distributed energy storage system  103 . Given an operating configuration of a distributed energy storage system  103 , load data, and specifics of the utility tariff for the electric load location  104  served by the distributed energy storage system  103 , the simulation engine  1035  can optimize battery usage based on the economics of the tariff. The output of the simulation engine  1035  may be an optimized set point array and corresponding battery curve(s). In some embodiments, the simulation engine  1035  accommodates varying utility measured power intervals (UMPI). Thus, in such embodiments, the simulation engine  1035  can be configured to change a moving average based on the UMPI when generating a simulation and evaluating maximum demand. 
     The utility tariff engine  1039  may be configured as a cloud-based or local computing environment, and is responsible for managing the specifics of utility tariffs used by the optimization engine  1031  to generate a set point array. Each utility tariff applicable to a specific distributed energy storage system  103  may be stored in the utility tariff engine  1039 . The utility tariff engine  1039  then uses this information to determine the periods of the day that require optimized battery usage. The utility tariff engine  1039  understands and uses the weekday, weekend, and holidays, as well as the changes in rates within each tariff time period (e.g., minute, hour, day or week), to help form an optimized control solution for one or more of the distributed energy storage systems. The utility tariff engine  1039  may also monitor utility-specific events, such as peak day pricing or changing tariff rates. 
       FIG. 10  is a block diagram of a general process sequence  1000  used by the control system  900  to create and deliver forecast and control information to one or more distributed energy storage systems  103 , in accordance with an embodiment of the present invention. The information created in the processing sequence  1000  may include optimized runtime parameters  401 , demand set-point curves  301 , and battery state-of-charge profiles  302 . This information may be used by the system controller  210  disposed in the one or more distributed energy storage systems  103  to both reduce total energy cost to the customer and to help the make the wider electric grid cleaner and more efficient by reducing overall peak power demand. Additional steps may be added in between the steps depicted in  FIG. 10 , as needed, to optimally control the one or more distributed energy storage systems  103  associated with control system  900 . Similarly, one or more steps described herein may also be eliminated as needed without exceeding the scope of the invention. Portions of the process sequence  1000  are also described in greater detail in conjunction with  FIGS. 11-16 . 
     The process sequence  1000  begins at step  1002 , where the coefficient engine  1032  of the control system  900  is used to determine and analyze coefficients for use in the control system  900 . The coefficients determined in step  1002  are used within the forecast engine  1034  and other parts of the control system  900  to form an optimized control solution for each distributed energy storage system  103  associated with the control system  900 . Thus, each of the one or more distributed energy storage systems  103  associated with the control system  900  uses a different optimized control solution to control the demand at each corresponding electric load location  104 . The coefficients generally include weightings applied to current and historical data used by the controlling software in the forecast engine  1034  to generate a forecast for a time period (e.g., a day, week, month or year) as to how various internal and external site-specific attributes will affect the power demand for each of the distributed energy storage systems  103  associated with the control system  900 . Generally, these coefficients correspond to historical weather data, forecasted weather data, additional weather data such as cloud cover and sunrise/sunset times, and historical demand data collected for the site. These coefficients may also correspond to information associated with a business or businesses located at the electric load location  104 , such as operating hours, business hours, scheduled maintenance, special events at the location, special events taking place nearby that may affect location (e.g., a parade), and other useful data that will help forecast the demand at a particular electrical load location  104 . 
     In step  1004 , the coefficient engine  1032  gathers a list of prior coefficients used by a distributed energy storage system  103  from an operation database (e.g., memory location) associated with the control system  900 , gathers historical premises load information from a load telemetry database associated with the control system  900 , gathers other external historical data from the external data store  1042 , and constructs a list of coefficients that will be useful to help control each of the distributed energy storage systems  103 . During the construction process, the coefficient engine  1032  compares the received data and combines like data to form an optimum data stream for the generation of the coefficient that can be used in the forecast engine  1034  for each distributed energy storage system  103 . In one example, historical and forecasted weather data are compared and combined so that the predicted weather conditions can be factored into how the one or more distributed energy storage systems  103  associated with the control system  900  may react to changes in load at any instant in time at each respective electrical load location  104 . 
     In step  1006 , the coefficient engine  1032  validates the coefficients created in step  1004  to ensure that these coefficients will improve the way the distributed energy storage systems  103  reacts to simulated changes in load at any instant in time at each electrical load location  104 . The validation process may include running historical forecasts using historical coefficient data sets and comparing results of the forecast to actual load characteristics received from each electrical load location  104 . If the results of the forecast qualitatively shows an improvement in how the system reacts with the newly generated coefficients versus the current or previously used coefficients, the coefficient engine  1032  will transfer the validated coefficients to the forecast engine  1034 . 
     In step  1008 , the forecast engine  1034  gathers forecasting data for each distributed energy storage system  103  associated with control system  900 . In some embodiments, this information is obtained from existing third party data providers, such as www.wunderground.com, www.weather.gov, and the like. In some embodiments, the forecasting data constructed in step  1008  is based on stored site characteristics for each distributed energy storage system  103 . In such embodiments, the stored site characteristics may be formatted as a business hours time-based array. 
     In step  1010 , the data-cleansing engine  1037  fills in any missing data using previously collected information, extrapolating the data or other useful techniques and adjusts any time-based intervals to match the desired interval of the forecast. 
     In step  1012 , the utility tariff engine  1039  gathers specifics regarding each of the electric utilities&#39; tariff, or tariffs, applicable for each electric load location  104  and constructs a data set for the solution engine  1036  to optimize operation of each distributed energy storage system  103  associated with the control system  900 . 
     In step  1014 , the forecast engine  1034  uses the latest coefficients from the coefficient engine  1032 , coefficient data sets, and historical load data to generate a forecast of the load for each distributed energy storage system  103  for the appropriate period of time (e.g., month). 
     In step  1016 , the simulation engine  1035  generates simulations of how a particular distributed energy storage system  103  operates given the forecasted load generated in step  1014 . Outputs may include optimized set points (e.g., demand set-point curves  301 ) for each day and the state-of-charge (e.g., battery state-of-charge curves  302 ) of each distributed energy storage system  103  for the forecasted period. 
     In step  1018 , the simulation engine  1035  checks the output of the current day&#39;s simulation to ensure the simulation can maintain the optimized set point strategy described by the demand set-point curves  301  generated in step  1016 . If the check fails, then the optimization simulation is repeated with increased demand set-points in the period of the current day&#39;s failure and then validated against the new current simulation. This iterative process continues until both the optimized simulation and the current simulation can successfully maintain the optimized set point strategy using the same set points. 
     In step  1020 , the simulation engine  1035  combines the simulation outputs of the default, optimized, and current results and packages them for delivery to the solution engine  1036 . 
     In step  1022 , the solution engine  1036  builds the appropriate solution battery curve based on the optimal strategy for each distributed energy storage system  103 . Battery curve strategies are assigned to each distributed energy storage system  103  based on analysis of the load predicted for each distributed energy storage system  103 . In some embodiments, the possible battery curve strategies that can be assigned include a flat battery curve, a stepped battery curve, a continuously varying battery curve, and a forecasted battery curve (battery state-of-charge profile  302 ). In such embodiments, a flat battery curve is generally used on load profiles with high demand volatility, a stepped battery curve is used when load profiles have heavy activity within predictable periods of a day or certain days of the week, and a forecasted battery curve is used when load profiles have large energy events for sustained periods of time. In some embodiments, as load changes over time at a particular electric load location  104 , the corresponding distributed energy storage system  103  can be configured to adjust to the appropriate above-described battery curve strategy. 
     In step  1024 , the solution engine  1035  combines the information output from the battery curve generation and the optimized set points from the simulation results and packages the solution (e.g., final solution for a time period) for each distributed energy storage system  103 . 
     In step  1026 , the risk management engine  1049  analyzes the output of each solution and applies a weighting to the forecasted solution to adjust how aggressive or conservative the solution should be for each particular distributed energy storage system  103 . In some embodiments, the risk management engine  1049  bases such analysis on historical results of how forecasted solutions performed in the past as well as real-time economic drivers to adjust battery usage or ensure demand reduction within individual or groups of distributed energy storage systems  103  in the field. 
     In step  1028 , the optimization engine  1031  receives the output of the risk management engine  1049  and solution engine  1035  and combines the data to construct a desired control solution for delivery to system controller  210  of each of the distributed energy storage systems  103  associated with control system  900 . 
       FIG. 11  is a block diagram of a process sequence  1100  used by the coefficient engine  1032  to create updated coefficients for one or more of the distributed energy storage systems  103  associated with the control system  900 , in accordance with an embodiment of the present invention. The process sequence  1100  may be performed periodically (e.g., once per month or billing cycle), or whenever optimization engine  1031  generates battery curves and/or demand set-point curves for a distributed energy storage system  103 . Additional steps may be added to or eliminated from the process sequence  1100  as needed without exceeding the scope of the invention. 
     The process sequence begins at step  1101 , in which the coefficient engine  1032  collects current coefficient values for one or more distributed energy storage systems  103 . These coefficient values may be stored locally and/or in cloud-based storage. 
     In step  1102 , the coefficient engine  1032  collects historical load data for the one or more distributed energy storage systems  103  of interest. This historical load data may be stored in an energy storage system telemetry database associated with the control system  900 , which may be a storage device local to the coefficient engine  1032  or a cloud-based storage device. 
     In step  1103 , the coefficient engine  1032  collects external data, such as weather forecasting information, schedules of events that may occur that may impact loading on the distributed energy storage systems  103  of interest, etc. This external data may be collected via the Internet and/or by any other technically feasible technique. 
     In step  1104 , the coefficient engine  1032  constructs a data array of historical data, external data, and current coefficient values for the one or more distributed energy storage systems  103  of interest. 
     In step  1105 , the coefficient engine  1032  performs a coefficient optimization algorithm to determine new coefficient values for the one or more distributed energy storage system  103  of interest. In some embodiments, the coefficient engine  1032  performs a non-linear mathematical analysis of the information included in the data array constructed in step  1104  to determine the improved new coefficient values in step  1105 . The optimized coefficients are thus generated by use of the historical data, external data, and current coefficient values that have been received. 
     In step  1106 , using the new coefficient values, the coefficient engine  1032  performs one or more simulations of the one or more distributed energy storage system  103  of interest. 
     In step  1107 , the coefficient engine  1032  validates the new coefficient values determined in step  1106  by determining if forecasted behavior of the one or more distributed energy storage system  103  of interest is more accurate using the new coefficient values. If accuracy of a forecast is shown to have increased using the new coefficient values in the simulation, e.g. actual energy consumption is better predicted, the processing sequence  1100  proceeds to step  1109 . If forecast accuracy is not increased using the new coefficient values, the processing sequence  1100  proceeds to step  1108 . 
     In step  1108 , the coefficient engine  1032  flags the new coefficient values as less accurate than the current coefficients, and stores the new coefficient values for future analysis. 
     In step  1109 , the new coefficients are provided to the one or more distributed energy storage system  103  of interest. In some embodiments, the new coefficient values are also stored in an operations database associated with the control system  900 . 
       FIG. 12  is a block diagram of a process sequence  1200  used by the forecast engine  1034  to generate forward-looking forecasted load profiles for a particular electric load location  104  serviced by a distributed energy storage system  103  associated with the control system  900 . Additional steps may be added to or eliminated from the process sequence  1200  as needed without exceeding the scope of the invention. 
     The process sequence begins at step  1201 , in which the forecast engine  1034  receives a request to perform a forecast. Such a request may occur periodically for each electric load location  104  served by control system  900  or whenever a specific distributed energy storage system  103  may benefit from updated coefficients. 
     In step  1202 , the forecast engine  1034  collects available information pertaining to the specific electric load location  104  of interest, including coefficients, historical load data, telemetry data of the distributed energy storage system  103 , and the like. In some embodiments, weighting of the importance of different historical data is performed based on the duration of the forecast and/or how far into the future the requested forecast period takes place. For example, for a forecast that extends beyond seven to ten days, historical data may be weighted more heavily than weather forecast data that is further out into the future, since the accuracy of weather forecasting decreases rapidly with time. 
     In step  1203 , data collected in step  1202  is cleansed. In other words, the collected data are modified to minimize the effect of gaps in data due to outages, etc. In some embodiments, time intervals associated with different data sets are normalized to a uniform desired time interval, using interpolation, averaging, and the like. For example, algorithms included in the forecast engine  1034  may be configured for data measured at ten-minute intervals, but a specific data source may only be available in fifteen-minute intervals. In some embodiments, after time intervals are normalized, actual gaps in data are corrected by interpolation or other techniques. In some embodiments, the procedure by which such data are corrected is a function of the duration of the data gap. For example, if the duration of a data gap is on the order of minutes or seconds, a moving average may be used to make a suitable correction. If the duration of a gap is on the order of hours or day, historical data and/or forecast data generated by the forecast engine  1034  may be used. 
     In step  1204 , the forecast engine  1034  generates a load forecast for the one or more electric load locations  104  of interest. The load forecast generated by the forecast engine  1034  can be used by the simulation engine  1035  to generate a solution result for a particular distributed energy storage system  103 . 
       FIG. 13  is a block diagram of a process sequence  1300  used by the simulation engine  1035  to determine optimal set-points and battery curves, according to an embodiment of the invention. The simulation engine  1035  determines optimal set-points and battery curves for a particular electric load location  104  serviced by a distributed energy storage system  103  associated with the control system  900 . The simulation engine  1035  may be used periodically (e.g., once each month, day, billing period, etc.) to determine an optimized solution for the next such period. Alternatively, in some embodiments, the simulation engine  1034  may be used as part of an intervention, in which a distributed energy storage system  103  is unable to maintain a specified state-of-charge and requires updated battery curves and/or demand set-point curves for a remaining portion of a day, billing cycle, month, etc. Additional steps may be added to or eliminated from the process sequence  1300  as needed without exceeding the scope of the invention. 
     The process sequence  1300  begins at step  1301 , in which the simulation engine  1035  collects a forecasted load for an electric load location  104  of interest from the forecast engine  1034  or a database associated with the control system  900  storing output from the forecast engine  1034 . 
     In step  1302 , the simulation engine  1035  collects the current operating conditions of the distributed energy storage system  103  associated with the electric load location  104  of interest. It is noted that accuracy of the solution generated by the simulation engine  1035  may depend on how recently the current operation condition data was collected in step  1302 . For example, when such data is even 15 to 30 minutes old, accuracy of a solution can be adversely affected. This is because a significant portion of energy available in a distributed energy storage system  103  can be discharged in that time. Such data related to the distributed energy storage system  103  may include current energy capacity, current state-of-charge, efficiency rating, total energy capacity, charge/discharge limits, etc. 
     In some embodiments, the simulation engine  1035  also collects applicable tariff information in step  1302 , for example from the universal tariff data store  1041  in  FIG. 9 . In such embodiments, additional long-term tariff site-specific inform may be collected to ensure that the distributed energy storage system  103  can operate autonomously for an indefinite period of time. Such information may include holiday information and/or major tariff seasonal changes. 
     In step  1303 , the simulation engine  1035  generates a default solution (using a “default” battery curve and demand set-point curve) for the time period for which the simulation is requested (e.g., a day, a month, a billing period, a remaining portion of a day, etc.). This simulation illustrates how well the distributed energy storage system  103  performs by providing a “default” battery drain curve for the time period of interest. 
     In step  1304 , the simulation engine  1035  identifies the “peak” period of the billing period or other time period of interest. This peak period, or peak day, is the day or time period during which the distributed energy storage system  103  typically has the lowest state-of-charge due to higher power usage at the electric load location at these times. Thus, the peak day is the most difficult day during a billing period for the distributed energy storage system  103  to operate within desired parameters. 
     In step  1305 , the simulation engine  1035  performs an analysis determining an optimized demand set-point curve and battery curve for the peak period or peak day determined in step  1304 . Generally, in step  1035 , tariff information is used to determine an economically optimal or otherwise efficient way in which to provide energy to the electrical load location  104 . 
     In step  1306 , the simulation engine  1035  runs a simulation using a generated optimized solution for the current day, i.e., using the load telemetry of the electric load location  104  of interest for the current day and the optimized battery state-of-charge profile and demand set-point curve determined in step  1305  for the distributed energy storage system  103 . 
     In step  1307 , the simulation engine  1035  checks the validity of the optimized solution by checking whether or not the state-of-charge of the distributed energy storage system  103  is reduced below a desired minimum or other threshold value during the simulation in step  1306 . If the state-of-charge remains greater than the minimum threshold value throughout the simulation, process sequence  1300  proceeds to step  1308 . If the state-of-charge drops below the minimum threshold value during the simulation, the process sequence  1300  proceeds to step  1309 . Thus, in step  1307 , proper operation of the new solution is confirmed prior to being distributed to the distributed energy storage system  103 . 
     In step  1308 , the simulation engine  1035  outputs the solution result to the solution engine  1036  for generating a solution for the distributed energy storage system  103 . The solution result may include default, optimized, and current simulation results. 
     In step  1309 , the simulation engine  1035  increases a set-point value in the time period or periods in which the state-of-charge of the distributed energy storage system  103  falls below the desired minimum or other threshold value. In some embodiments, the set-point value is increased by a relatively small amount, for example on the order of about 1 kW for an industrial location. The process sequence  1300  then proceeds back to step  1305  and continues until an optimized solution is obtained in which the state of charge of the distributed energy storage system  103  is reduced below a desired minimum or other threshold value. 
       FIG. 14  is a block diagram of a process sequence  1400  used by the solution engine  1036  to generate a solution of optimal set-points and battery curves for a distributed energy storage system  103 . Additional steps may be added to or eliminated from the process sequence  1400  as needed without exceeding the scope of the invention. 
     The process sequence  1400  begins at step  1401 , in which the solution engine  1036  collects the current battery curve strategy and battery parameters (e.g., battery power, maximum charge capacity, etc.) for a distributed energy storage system  103 . 
     In step  1402 , the solution engine  1036  determines an appropriate battery curve strategy for the distributed energy storage system  103 . Specifically, the solution engine  1036  selects one of a flat, stepped, continuously varying, or forecasted battery curve, based on previous behavior (i.e., historical load telemetry) for the distributed energy storage system  103  of interest. Other configurations of battery curve may also be selected by the solution engine  1036  in step  1402 . Therefore, in one example, as the state-of-charge of the energy source  224  drops due to an increased load the system controller  210  will provide power to the electric load location  104  based on the control strategy defined by the battery curve. 
       FIG. 15A  illustrates one embodiment of a flat battery curve  1510 . As shown, flat battery curve  1510  includes multiple target state-of-charge curves  1511 . Each of the target state-of-charge curves  1511  indicates a target minimum state-of-charge for a distributed energy storage system  103  over a specific time period, for example for a single 24-hour period. In other words, each target state-of-charge curve  1511  provides a threshold state-of-charge value at any point in time. When state-of-charge is below this threshold value, the distributed energy storage system  103  is considered to be below an expected state-of-charge and helps to define how strongly the distributed energy storage system  103  should currently react to the changing state-of-charge so that the distributed energy storage system  103  can effectively respond to the current demand while also retaining enough energy to eliminate demand spikes at critical times later in the day. Consequently, in response to a current state-of-charge of the distributed energy storage system  103  falling below a target state-of-charge curve  1511 , the system controller  210  adjusts demand set-point (e.g., a demand set-point curve  301 ), so that the distributed energy storage system  103  can still meet expected later demand. 
     As the current state-of-charge of the distributed energy storage system  103  falls below additional target state-of-charge curves  1511 , the system controller  210  continues to adjust demand set-points. In some embodiments, such a set-point change is linear. For example, for each target state-of-charge curve  1511  that the current state-of-charge falls below, demand set-points are changed by the same amount, e.g., +1 kW. In other embodiments, such a set-point change can increase in a non-uniform fashion, such as a proportional response that increases as the current state-of-charge falls below additional target state-of-charge curves  1511 . For example, for the first target state-of-charge curve  1511  that the current state-of-charge falls below, demand set-points are changed by +1 kW, for the second +5 kW, for the third +12 kW, and so on. 
     As shown in  FIG. 15A , the target state-of-charge curves  1511  of the flat battery curve  1510  are substantially flat, and therefore do not vary with time. The flat battery curve  1510  has been shown to be effective on commercial load site profiles with high volatility, i.e., multiple demand peaks separated by periods of relatively low demand. 
       FIG. 15B  illustrates one embodiment of a stepped battery curve  1520 . As shown, the stepped battery curve  1520  has a variable set-point. Specifically, the stepped batter curve  1520  includes multiple discontinuous target state-of-charge curves  1521 , each indicating a target minimum state-of-charge for a distributed energy storage system  103  over a specific time period. Similar to the flat battery curve  1510 , the stepped battery curve  1520  includes multiple curves (e.g., the target state-of-charge curves  1521 ) for any given time. Also, for any particular time period, any two target state-of-charge curves  1521  vary by a constant amount with respect to time, since each of the target state-of-charge curves  1521  is a substantially horizontal line segment. However, separation between adjacent target state-of-charge curves  1521  may be uniform. For example, target state-of-charge curves  1521 A and  1521 B may be separated by the same quantity of charge (e.g., 5% of battery charge) as target state-of-charge curves  1521 B and  1521 C. Alternatively, and as illustrated in  FIG. 15B , separation between adjacent target state-of-charge curves  1521  may be non-uniform, for example proportional to distance from the highest target state-of-charge curve  1521 . Unlike the flat battery curve  1510 , the value of the target state-of-charge curves  1521  vary with time, and can therefore be tailored to address multiple heavy demand periods within predictable periods of a day or certain days of the week. Consequently, each of target state-of-charge curves  1521  may be a discontinuous function, as shown in  FIG. 15B . In some embodiments, a stepped battery curve  1520  is used to optimize the performance of a distributed energy storage system  103 . Thus, the general configuration of a stepped battery curve  1520  can be selected to manage how aggressive a distributed energy storage system  103  responds to changes in predicted load. Therefore, stepped battery curve  1520  can be configured to encourage distributed energy storage system  103  to maximize reduction in short-term spikes in demand or to maximize reduction in overall energy use at an electric load location  104 . As illustrated in  FIG. 15B  the system will react more aggressively to changing load during the 10:00 to 16:00 time period versus the 0:00 to 7:00 and 18:00 to 24:00 time periods. Therefore, the distributed energy storage system will appropriately respond to demand in the less critical times, so that it can retain enough energy to eliminate demand spikes at the critical and/or costly times of the day (i.e., 10:00 to 16:00 at this electric load location). 
       FIG. 15C  illustrates one embodiment of a continuously varying battery curve  1530 . As shown, the continuously varying battery curve  1530  includes multiple target state-of-charge curves  1531 , each indicating a target minimum state-of-charge for a distributed energy storage system  103  over a specific time period. Unlike stepped battery curve  1520 , the target state-of-charge curves  1531  are continuous curves, and each of which may have a variable spacing in continuously varying battery curve  1530 . As illustrated in  FIG. 15C  the system will react more aggressively to changing load during the 10:00 to 16:00 time period versus the 0:00 to 5:00 and 22:00 to 24:00 time periods. Therefore, the distributed energy storage system will appropriately respond to the demand in the less critical times, so that it can retain enough energy to eliminate demand spikes at the critical and/or costly times of the day (i.e., 10:00 to 16:00 at this electric load location). 
     In some embodiments, flat battery curve  1510 , stepped battery curve  1520 , and/or continuously varying battery curve  1530  may be a forecasted battery curve. Forecasted battery curve are formed by use of the forecast information received and analyzed by the solution engine, and provides an improved control in cases where the system can use the forecast information to prevent the system from over-reacting to expected periods of high use. Forecasted battery curves may be used effectively when load profiles have large energy events for sustained periods of time. In some embodiments, a forecasted battery curve may include an adjustable recovery period that can be configured to ensure complete recovery of the distributed energy storage system  103 . An example of an adjustable recovery period  1533  is shown in  FIG. 15C . 
     Returning to  FIG. 14 , in step  1403 , after an appropriate battery curve strategy (flat, stepped, continuously varying, forecasted) for the distributed energy storage system  103  has been determined by the solution engine  1036 , the solution engine  1036  collects information regarding the configuration of the distributed energy storage system  103 . 
     In step  1404 , the solution engine  1036  retrieves a simulation for the period of interest, such as an upcoming billing cycle, the remainder of the current business day, etc. As described above in conjunction with  FIG. 13 , the simulation can be generated by the simulation engine  1035 , and the solution result may include default, optimized, and current simulation results. 
     In step  1405 , the solution engine  1036  constructs aggregated state-of-charge battery curves. Specifically, the default, optimized, and current battery curves from the simulation results are combined. For example, in some embodiments, a median value of the default, optimized, and current battery curves is constructed. In some embodiments one or more of the default, optimized, and current battery curves are weighted differently. In some embodiments, the aggregated state-of-charge battery curve can be constructed using information from the default, optimized, and current battery curves (e.g., maximum and minimum values, etc.) in other ways as well. For example, in one embodiment, the default, optimized, and current battery curves are used to generate a maximum, minimum and median value at each time interval, and then a maximum, a minimum, and a median battery curve are constructed. 
     In step  1406 , the solution engine  1036  constructs a battery curve using the selected strategy. For example, to construct a flat battery curve, the solution engine  1036  may use maximum and minimum state-of-charge values determined in step  1405  and then select intervening target state-of-charge curves, similar to target state-of-charge curves  1511  in  FIG. 15A , using a resolution parameter that defines separation between each target state-of-charge curve. To construct a stepped battery curve, the solution engine  1036  may use the minimum state-of-charge values determined in step  1405  to calculate a maximum state-of-charge battery curve substantially similar to one of target state-of-charge curves  1521  in  FIG. 15B . To construct a forecasted battery curve, the solution engine  1036  may construct a master battery curve substantially similar to one of target state-of-charge curves  1531  in  FIG. 15C . The solution engine  1036  may use a combination of the default, optimized, and current battery curves included in the solution result from the simulation engine  1035 . 
     In step  1407 , the solution engine  1036  outputs a set-point array (e.g., demand set-point curves  301 ) and a flat, step, continuously varying, or forecasted battery curve (e.g., battery state-of-charge curves  302 ). 
       FIG. 16  is a block diagram of a process sequence  1600  used by the optimization engine  1031  to generate and distribute an optimal battery curve for a distributed energy storage system  103 . The optimization engine  1031  may perform process sequence  1600  periodically (e.g., once each month, day, billing period, etc.) to determine an optimized solution for the next such period. Alternatively, in some embodiments, the optimization engine  1031  may be used as part of an intervention, in which a distributed energy storage system  103  is unable to maintain a specified state-of-charge and requires updated battery curves and/or demand set-point curves for a remaining portion of a day, billing cycle, month, etc. In addition, in some embodiments, the optimization engine  1031  may be used to perform a reconciliation, in which the optimization engine  1031  audits the performance of a distributed energy storage system  103  using historical data. Additional steps may be added to or eliminated from the process sequence  1600  as needed without exceeding the scope of the invention. 
     The process sequence  1600  begins at step  1601 , in which the optimization engine  1031  determines whether a scheduled job, an intervention, or a reconciliation is requested. If a scheduled job is being performed, the process sequence proceeds to step  1611 , if an intervention is being performed, the process sequence proceeds to step  1621 , and if a reconciliation is being performed, the process sequence proceeds to step  1631 . 
     In step  1611 , the optimization engine  1031  receives a solution result from the solution engine  1036  and sends the solution result to the appropriate distributed energy storage system  103 . The solution result may be a job that is performed on a monthly schedule, a daily schedule, based on a billing cycle, etc. 
     In step  1621 , in which a software intervention is being performed, the optimization engine  1031  confirms that an intervention should be performed for the distributed energy storage system  103 . For example, if an intervention has already been performed very recently for the same distributed energy storage system  103 , the optimization engine  1031  may wait for a specified delay time before performing another intervention to allow the previous intervention to take effect. 
     In step  1622 , the optimization engine  1031  generates a forecasted solution for the remainder of a current time period (e.g., for the remainder of a business day). In some embodiments, more up-to-date external data, such as weather data, may be incorporated into the forecasted solution. 
     In step  1623 , the optimization engine  1031  sends the forecasted solution, which may include a set-point array (e.g., demand set-point curves  301 ) and/or a flat, stepped, continuously varying, or forecasted battery curve (e.g., battery state-of-charge curves  302 ) to the distributed energy storage system  103 . 
     In step  1631 , in which a reconciliation is being performed, the optimization engine  1031  collects historical load data for the distributed energy storage system  103  for the current billing cycle, for example from the site telemetry data store  1043 . 
     In step  1632 , the optimization engine  1031  calculates maximum demand for each period of the utility tariff. 
     In step  1633 , the optimization engine  1031  updates the distributed energy storage system  103  with the maximum demand values determined in step  1632 . 
     It is noted that the same concept of preparing a matrix of temporally synchronized charge state and demand set-point data in order to manage a distributed energy storage system can be applied to other embodiments. For example, a grid operator may use such data to control other distributed resources, such as a hydroelectric facility trying to match a certain changing demand while also maintaining a certain water level in the upstream reservoir. The same concept could be used for a municipal water district trying to maintain a certain reservoir level and/or water pressure at an autonomously controlled storage facility. The same concept could also be used to control a demand response system at a large electric load where the “battery” state is the demand reduction availability. 
     The process of managing an autonomous storage device can have other applications as well. Specifically, any system where a variable demand is preferred to be constant and a distributed supply resource, such as stored energy in the form of compressed air, electricity, or water for example, is used to create this consistency could benefit from this sort of control system. Furthermore, this process is not restricted by size of the supply or demand. For example, a substation configured with an autonomous storage system could automatically balance varying circuit loads by using this process, just as well as a single commercial site could use this system to balance its load. As an additional example, a hydroelectric facility (whether pumped storage or not, whether micro hydro or larger) could benefit from an autonomous control system to balance varying demands and circuit loads with the appropriately forecasted load data and intelligently generated operating parameters. Municipal water (both waste and drinking) could also benefit from such a control method. Water level, water pressure, and electricity generation are all examples of demands that such a control method could help keep constant with a distributed resource. 
     Multiphase Electrical Load Facility Control 
     Referring to  FIGS. 2A and 17 , in some multi-phase electrical configurations at an electric load location  104 , power is often unevenly consumed across the different phases  235 A- 235 C ( FIG. 17 ). Additionally, if there is on-site generation ( FIG. 18 ), such as solar cells, this on-site generation can be connected to a single phase (e.g., e.g., phase  235 A) creating an even greater imbalance in the load seen between phases. As noted above, unbalanced loads between phases can cause distribution equipment damage or early failure, as well as create transmission and generation efficiency losses. 
     Some embodiments of the invention, use a distributed energy storage system  103  located behind the utility meter  201  at the electric load location  104  to provide a system that can rapidly balance the power flowing through two or more phases. These high speed corrections to power imbalances between phases desirably occur on a second or sub-second timeframe to prevent damage to the distribution equipment. High speed corrections made to any unbalanced loads between phases by the system controller  210  may also allow for an improved control of the power flow to and from the electric load location  104 . Electrical connection configurations at an electric load location  104  can be either wye or delta type connections at any distribution service voltage, which is typically 208-480 Volts line-to-line, and have any number of phases (typically split or three phase). 
     Referring to  FIG. 17 , in some embodiments, the distributed energy storage system  103  includes a phase power correction system  1710 , which may include a plurality of single-phase bidirectional power converters  225  within the power controller  222 , such as bidirectional power converters  225 A- 225 C shown in  FIG. 17 . In one configuration of the phase power correction system  1710 , each bidirectional power converter  225 A,  225 B,  225 C is connected to a single phase (e.g., phases  235 A- 235 C in  FIG. 17 ) on the AC side  1715  of the bidirectional power converter and to an energy source  224  on the energy source side  1716  of the bidirectional power converters  225 . The energy source  224  may be a shared or distributed battery bank on the energy source side  1716  of the bidirectional power converters  225 . In some configurations, an electric load location  104  can have an equal or unequal number of converters connected to each phase  235 A,  235 B,  235 C. The bidirectional power converters  225  are controlled by the system controller  210 , which sends commands mandating when to charge and when to discharge energy from the energy source  224 . The bidirectional power converters  225  communicate with one or more monitoring devices, such as the power monitor  230  ( FIG. 2A ), which provides per phase power information to the system controller  210  and/or control system  900 . The system controller  210  uses this information to decide which phases are out of balance relative to each other and then commands at least one of the bidirectional power converters to charge or discharge to compensate for the phase imbalance. 
     In the case where one or more phases are out of balance (e.g. one of the loads (e.g., load  241 A) is consuming power more power than the other loads (e.g., loads  241 B or  241 C)) the system controller  210  will decide to either charge or discharge the energy source  224  in order to balance the power flowing through the various phases. Additionally, the system controller  210  may instruct one or more of the bidirectional converters  225 A,  225 B,  225 C to pull energy off its respective phase and pass it to the energy source  224 , or to a source bus  1720 , where another bidirectional power converter will use it to inject it into another phase. In one embodiment, the source bus  1720  generally includes a DC source bus that comprises a positive terminal connection line  1722  and a negative terminal connection line  1721  that are each coupled to one or more energy sources  224  and to each of the bidirectional converters  225 , as shown in  FIGS. 17 and 18 . The source bus  1720  thus allows power to flow between interconnected bidirectional power converters without the need to transfer energy to the energy source  224 , and/or allows power to flow to one or more energy sources (only one shown in  FIG. 17 ) that are connected to the source bus  1720 . 
     In one example, in a three phase system, if phase  235 A has a positive imbalance (e.g., there is a greater load on phase  235 A than on phases  235 B or  235 C), this imbalance can be dealt with in many ways. In one embodiment, software running on the system controller  210  will instruct the bidirectional converter(s) on phase  235 B (e.g., bidirectional power converter  225 B) or the bidirectional converter(s) on phase  235 C (e.g., bidirectional power converter  225 C), or both bidirectional converters  225 B and  225 C together, to draw power from their respective phases (thereby increasing their per phase loads) and inject it into the source bus  1720 . The injected power delivered to the source bus is then used by the bidirectional power converter  225 A to then inject into phase  235 A to offset and decrease the apparent load on phase  235 A. A processor running in the system controller  210  use the signals received from the power monitor  230  and signal received delivered from the energy source  224  (e.g., state-of-charge, battery health, etc.) to closed-loop control the delivery of power between phases and/or the energy source  224  using the hardware components found in the phase power correction system  1710 . Depending on the loads  241 B,  241 C on the other two phases  235 B,  235 C, the system controller  210  may decide to draw more power from one phase than the other (e.g., phase  235 B or  235 C) in order to achieve the greatest balance. For example, if phase  235 B has a greater load than phase  235 C then the system  210  controller would instruct more power to be drawn from phase  235 C than phase  235 B in order to achieve an even power draw across the phases. In another embodiment, the system controller  210  instructs the bidirectional power converter  225 A on phase  235 A to draw energy from one or more energy sources  224  attached to the source bus  1720  in order to offset its imbalanced load. In yet another embodiment, the system controller  210  will use power from the one or more energy sources  224  and power from phase  235 B or phase  235 C, or both phases, in order to offset the greater load on phase  235 A. The decision from where to draw power from can depend on several variables, such as battery state-of-charge (e.g., 0-100% charged), battery state of health, and forecasted demand on each phase, that are stored within and used by the system controller  210 , or control system  900 , to control the power transfer within the electric load location  104 . 
     The process discussed above can also be used where, for example, phase  235 A has a negative imbalance (e.g. there is a greater load on phases  235 B or  235 C than on phase  235 A). In this case, power will be pulled from phase  235 A and/or the energy source  224 , in order to offset the loads on phases  235 B and/or  235 C. Ideally, at the utility meter  201  or facility service, all phases at the electric load location  104  would appear to have the same amount of load when the correct balancing is implemented by the distributed energy storage system  103 . 
     In the case where the facility has an onsite generator  1801  (see  FIG. 18 ), such as solar PV panels, wind generators or a diesel generator, attached to one or more of the phases at the electric load location  104 , it can create a load imbalance. In some embodiments, the distributed energy storage systems  103  can also be used to counteract rapid changes in the voltage level, current flow and power created by a power disruption event, such as when the amount of power delivered from an onsite generator  1801  to one of the phases rapidly changes at the electric load location  104 . For example, the bidirectional power converter(s) cause the energy source(s)  224  to charge or discharge into one or more of the phases during a power disruption event that occurs on at least one of the phases, due at least in part to the presence of the onsite generation  1801 . A power disruption event, for example, may include a situation where clouds block sun light from making it to one or more solar cells that are coupled to a phase (e.g., phase  235 A ( FIG. 18 )), thus rapidly changing the amount of power generated by the solar cells, or may include a power surplus event (e.g., an over voltage event), such as when the wind blows unexpectedly more than anticipated causing locally attached wind turbines to generate an oversupply of electricity. Therefore, as similarly described above, the distributed energy storage systems  103  illustrated in  FIG. 18  can be used to even the amount of power flowing through all of the phases. The control system  900 , and/or system controller  210 , can respond power fluctuation on each phase in sub-second increments in order to truly balance the load variability on each phase due to onsite generators and other sources of power fluctuations at the site. 
       FIG. 19  graphically illustrates a plot of power usage versus time on three different phases at an electric load location  104  (e.g., top-left, top-right and bottom-left plots) and a plot of power usage versus time of each of the three phases after the imbalanced has been corrected by the distributed energy storage system  103  (e.g., bottom-right plot). In one example, as illustrated in  FIG. 19 , the power usage versus time can achieve the corrected energy level shown in the bottom-right plot by primarily drawing more power on phase  235 C to balance the power consumption being drawn by phases  235 A and  235 B by use of the phase power correction system  1710  to achieve the per-phase balanced load. 
     In some embodiments, the energy storage configuration solution does not necessarily have to be used at a single electric load location  104 . In some cases, the same per-phase load balancing could happen on a much larger power distribution circuit. In this case, many distributed energy storage systems  103  can, by monitoring many individual smaller site loads, perform phase balancing on a larger scale to balance the phases on a particular distribution circuit or substation. 
     In some embodiments of the present invention, as discussed above, the distributed energy storage system is configured to monitor the electric load location&#39;s electricity use on one or more of the phases, and discharge on one or more of the phases at times of high demand peaks in order to reduce the peak power provided by the electric grid and to simultaneously balance the load provided by each phase. In this configuration, the distributed energy storage system can minimize peak demand, reduce the total energy cost to the customer (by primarily reducing peak demand charges), reduce the likelihood that distribution equipment will become damaged due to a load imbalance between phases, maximize the finite amount of energy stored in the consumable energy storage components in the distributed energy storage system and maximize the usable lifetime of the consumable energy storage components in the distributed energy storage system, and thus reduce the cost-of-ownership of the distributed energy storage system. These systems can also recharge at times of low demand and/or low electricity rates, since from the utility&#39;s perspective, demand spike reduction is more valuable than the act of consuming additional energy during periods of lower demand. Examples, of processes that can be used to reduce peak demand at an electric load location  104  on one or more phases are described above, and thus can be integrated with the per phase power balancing techniques discussed herein to provide a robust integrated distributed energy storage system. 
     Embodiments of the invention may provide a system for controlling the transfer of energy between an electric load location and an electric grid, including a power monitor that is in electric communication with a multiphase electric line that is coupled to an electric meter of an electric load location, wherein the electric meter is configured to measure energy transferred to the electric load location through the electric line. The system may include an optimization engine configured to receive one or more external inputs and create a set of operating parameters based on the one or more external inputs, and a system controller that is configured to receive the created operating parameters and use the operating parameters to control an amount of energy flowing through one or more of the phases of the multiphase electric line below a threshold value. The system controller may be further configured to receive information regarding the power transferred through the first phase and the second phase from the power monitor, and use the received information to control a transfer of energy from or to the first phase of the multiphase electric line from a bidirectional power converter that is in communication with an energy source. 
     While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.