System and method for measuring operation of distributed energy system

Abstract

The present disclosure provides an operational metering method for a distributed energy system arranged to provide energy to an electric power grid, the distributed energy system including a plurality of energy supply devices configured to output stored energy and an operational metering module configured to control operation of the plurality of energy supply devices, the method being performed by the operational metering module and including receiving raw data from the plurality of energy supply devices including operational data for each energy supply device, processing the raw data to remove a portion of the operational data for each energy supply device, selecting representative data from the processed raw data for each energy supply device representative of a status of each energy supply device, aggregating the representative data for each energy supply device into an aggregated data package, and transmitting the aggregated data to the electric power grid.

Description

[Technical field]

[0001] The present disclosure relates to distributed energy systems. In particular, the present disclosure relates to operational measurement of distributed energy systems. [Background technology]

[0002] The National Grid requires operational metering data from assets that contribute to the electricity supply (here energy supply devices) to maintain the balance of services provided by the grid. Here, "energy supply devices" refers to any device that can provide and maintain the supply of energy / power for the required period of time. For example, energy supply devices include batteries, other forms of domestic and industrial energy storage devices, various types of generators, various types of demand assets, e.g. electrolyzers, heat pumps, industrial machines, etc.

[0003] The power grid uses a balancing mechanism (BM) to balance power supply and demand in real time. When production and consumption are not balanced, the power grid uses the BM to purchase available power from participating assets to correct the imbalance. To do this, the power grid requires operational data such as active power and available power from participating assets, and that data must be received in real time.

[0004] Balancing Mechanism Units (BMUs) are used as a trading unit in the balancing mechanism. One BMU is considered the smallest grouping that can trade with the grid on its own. Energy generated or consumed by equipment belonging to one BMU is credited to that BMU. Traditionally, for energy generation, each BMU consists of a single generating unit, and for energy consumption, it consists of a collection of consumption meters. Industrial-scale generators that can generate enough energy to trade as a single BMU can stream operational data directly from the asset to the grid's data collection services.

[0005] However, small-scale assets such as home heat pumps, home solar panels, and electric vehicle batteries cannot individually supply enough energy to be traded independently. Therefore, small-scale assets distributed in different geographical locations are grouped together to form virtual assets and traded as virtual BMUs. In such a configuration, when distributing operation data of the virtual BMU to the power grid, it is necessary to first aggregate operation data from individual physical assets, which creates new challenges not present in conventional metering distribution, where physical assets and BMUs are one-to-one associated with each other as seen from the power grid.

[0006] In a virtual BMU, first, each physical asset consumes or generates electricity, then operational data is collected from each asset, the collected data is aggregated, and the aggregated data is delivered to the power grid. For the aggregated operational data to be relevant and usable by the power grid, latency must be minimized, while the resource processing costs required for the aggregation process must be kept low enough so that it does not become unprofitable for asset operators or service providers to operate the service.

[0007] It would therefore be desirable to provide improved methods and systems for operational metering of distributed energy systems.

[0008] In view of the above, the present technology provides a computer-implemented method of operational metering for a distributed energy system arranged to provide energy to a power grid, the distributed energy system comprising a plurality of energy supply devices configured to output stored energy, and an operational metering module configured to control operation of the plurality of energy supply devices, the method being executed by the operational metering module and including the steps of receiving raw data from the plurality of energy supply devices, the raw data comprising operational data for each energy supply device, processing the raw data to remove a portion of the operational data for each energy supply device, selecting representative data from the processed raw data for each energy supply device representative of a state of each energy supply device, and aggregating the representative data for each energy supply device into an aggregated data package and transmitting the aggregated data to the power grid.

[0009] According to an embodiment of the present technology, raw operating data from a plurality of energy supply devices of a distributed energy system is processed to remove a portion of the raw data that is deemed irrelevant or not immediately necessary for the operating evaluation, and then representative data from each energy supply device is selected from the processed data to further reduce the amount of data before the significantly reduced operating data is aggregated and transmitted to the power grid. Here, the energy supply devices may include small-scale energy storage and / or power generation devices such as electric vehicle batteries, energy storage devices for renewable energy such as solar power generation and wind power generation, and small-scale generators. By first filtering out data that is not required by the power grid before data aggregation and then selecting only data that is representative of the current state of each energy supply device, the amount of data that needs to be processed for aggregation is significantly reduced, resulting in a reduction in both processing resource requirements and processing time. Furthermore, by improving the processing time required for data aggregation and reducing the amount of aggregated data to be transmitted to the power grid, the latency time between obtaining the operating data and receiving the data on the power grid can also be reduced.

[0010] In some embodiments, the method may further include receiving a service request from a power grid, the service request being generated to request a level of energy supply service determined based on the aggregated data package, and outputting instructions to one or more of the plurality of energy supply devices in response to the service request to instruct the one or more of the plurality of energy supply devices to output stored energy to meet the requested level of energy supply service. Using the aggregated data, the power grid can evaluate the combined operating conditions of the plurality of energy supply devices in the distributed energy system to determine what level of energy supply service can be expected from the distributed energy system, and request energy supply service from the virtual BMU accordingly. In this manner, by reducing the amount of irrelevant operating data, the aggregated data generated and transmitted by the operating metering module helps the distributed energy system ensure that received service requests are consistent with the capacity of the system.

[0011] In some embodiments, the removed portion of the raw data may include operational data that is not used by the power grid in determining a level of energy delivery service, for example, the removed portion may include old operational data such as the length of time an energy delivery device has been out of service, temperature measurements, etc.

[0012] In some embodiments, the method may further include storing the removed portion of the raw data for analysis. Although the removed portion includes data not needed by the power grid, analyzing this data is useful, for example, to understand how each energy delivery device has been operated and to determine whether any devices should be taken out of service, for example, if the device is overheating or its state of charge (SoC) is below a specified minimum level.

[0013] In some embodiments, the method may further include monitoring the data received at and / or processed by the driving measurement module by applying one or more driving rules to the data. The monitoring may be performed at any stage on the raw data, the processed data, the representative data, removed portions of the raw data, and / or the processed data that has not been selected as representative data. The monitoring may be performed simultaneously with the aggregation process or, preferably, may be performed outside of the aggregation processing pipeline, optionally at a slower processing speed.

[0014] In some embodiments, one or more driving rules may be applied to the data to identify anomalies.

[0015] In some embodiments, the method may further include excluding an energy supplying device from the plurality of energy supplying devices if an anomaly is identified for the device, for example, the anomaly may be a device operating at a temperature above a specified maximum temperature.

[0016] In some embodiments, the raw data received from the multiple energy delivery devices may include multiple messages, each message may include multiple readings, and the method may further include processing the raw data to remove a portion of the operational data of each energy delivery device, and then splitting each message in the processed raw data into multiple readings. The multiple readings in the messages from the devices may relate to different device parameters, such as operating temperature, state of charge, active power, or available power, and the multiple readings may include readings of one or more of these parameters at different times. For example, multiple readings may be taken every second, and older readings may be included if the device has not been used for a period of time. Splitting each message into multiple readings allows each reading to be individually filtered, selected, manipulated, or otherwise processed.

[0017] In some embodiments, the multiple readings may include one or more temperature measurements, one or more state of charge measurements, one or more available power measurements, historical data, or any combination thereof.

[0018] In some embodiments, the method may further include generating a data file for each of the plurality of readings, where the data file for each of the plurality of readings may include the reading and metadata, where the metadata may include a timestamp and a unique identifier for the reading. Including metadata may include additional information associated with each reading, such as allowing the readings to be sorted chronologically, each reading to be uniquely identified, etc.

[0019] In some embodiments, the unique identifier for the reading may include a device identifier that identifies the energy delivery device from which the reading was obtained and a reading identifier that identifies the reading from multiple readings in the message.

[0020] In some embodiments, the representative data for each energy delivery device may include a most recent reading of the multiple readings, a highest reading of the multiple readings, or an average value of the multiple readings.

[0021] Another aspect of the present technology provides a computer readable storage medium containing machine readable code which, when executed by a processor, causes the processor to perform the above-mentioned method.

[0022] A further aspect of the present technology provides an operation metering system for operation metering of a distributed energy system arranged to supply energy to an electric power grid, the distributed energy system comprising a plurality of energy providing devices configured to output stored energy, the operation metering system including a communications module configured to communicate with the electric power grid, and a processor unit including at least one processor and a memory storing machine readable code that, when executed by the at least one processor, causes the at least one processor to receive raw data from the plurality of energy supplying devices, the raw data including operation data of each energy supplying device, process the raw data to remove a portion of the operation data of each energy supplying device, select representative data for each energy supplying device from the processed raw data representative of a state of each energy supplying device, aggregate the representative data for each energy supplying device into an aggregate data package, and transmit the aggregate data to the electric power grid.

[0023] In some embodiments, the operation metering system may include multiple processor units arranged in a distributed processing system, and the operation metering system is scalable based on the number of energy supply devices in the distributed energy system. In a situation where multiple energy supply devices are operated by different operators at different locations, not all of the multiple energy supply devices in the distributed energy system are available to supply energy at all times or at the same time. In this way, the processing load may vary depending on the number of energy supply devices available at a given time. Therefore, the distributed approach ensures that resources are used by the operation metering system only when necessary and that the system is scalable when the processing load increases.

[0024] Yet another aspect of the present technology provides a distributed energy system for supplying energy to a power grid, the distributed energy system comprising a plurality of energy supply devices, each configured to output stored energy, and an operational metering system as described above.

[0025] Each embodiment of the present technology will have at least one, but not necessarily all, of the above-mentioned objects and / or aspects, and it will be understood that some aspects of the present technology, which result from attempts to achieve the above-mentioned objects, may not meet this object and / or may meet other objects not specifically described herein.

[0026] Additional and / or alternative features, aspects, and advantages of embodiments of the present technology will become apparent from the following description, the accompanying drawings, and the appended claims. Next, an embodiment will be described with reference to the accompanying drawings. [Brief description of the drawings]

[0027] [Figure 1] 1 illustrates an exemplary driving measurement system. [Diagram 2] 1 illustrates a schematic diagram of an exemplary distributed energy system arranged to supply energy to a power grid; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] Service providers currently offer systems and methods for aggregating data across multiple assets, where data aggregation is performed by a centralized server. However, due to the large amount of data that needs to be processed, the act of sampling the data to calculate an aggregate reading necessarily introduces latency. Therefore, to reduce the overall latency, latency at other stages of the process must be reduced and limited, which often requires the allocation of more processing resources (and thus increased costs). Thus, it is difficult for service providers to perform data aggregation with low latency and low cost, and there is often a trade-off between low latency and high cost, or slow speed but low cost.

[0029] Accordingly, the present technology provides a method and system for operational metering of a distributed energy system including multiple energy delivery devices. The present approach employs priority-based routing of different data types generated by the multiple devices from source to aggregation, allowing critical information to be placed at the front of the processing pipeline at each stage of processing, while processing of less critical information or other non-time critical processing can be performed at a slower pace or at a later stage.

[0030] The method may further include de-duplication of data within the prioritized data to identify data that is deemed not useful to the final aggregated data (e.g., data that is not expected to change the output result or, for example, stale data) before the data is moved to aggregation, thereby making the aggregation process more efficient by reducing the time and resources spent processing data that is deemed not useful.

[0031] Additionally, the technique may further monitor multiple energy delivery devices in real time, such that if an individual device has a problem, the status associated with that device is updated in real time to reflect the problem. The status information for the multiple devices is then updated and presented to an aggregation process that uses the status information to determine which devices should be included (or excluded) as part of the aggregation. The inclusion of status information associated with each device means that operating data from devices that are offline or out of service can be excluded from the aggregation process, thereby further reducing the amount of data that needs to be processed.

[0032] While conventional methods employ a centralized server to perform the aggregation processing of multiple devices, the present method can be implemented in a distributed system including multiple interconnected processing units. Since individual energy supply devices are often operated by different owners in different locations, the number of devices participating in the distributed energy system fluctuates from time to time. By employing a distributed processing system, it is possible to expand or contract the processing system according to the increase or decrease in the processing load caused by the increase or decrease in the number of devices, and it is ensured that processing resources are used only as much as necessary.

[0033] The approach allows electric vehicles and other small-scale energy assets such as batteries and generators to be grouped together to provide energy supply services that were previously only available to large, expensive assets such as industrial batteries or gas peaking plants. In this way, the approach makes grid services available to smaller, distributed assets, thereby increasing the flexibility of the electricity grid.

[0034] 1, the illustrated distributed energy system includes an operational metering system 100 and a number of energy supply devices 101, 102, 103, 104, 105 arranged to supply energy (electricity) to a power grid 190. The energy supply devices 101-105 may include, for example, electric vehicle batteries, domestic or small scale photovoltaic cells, wind turbines, hydroelectric generators, domestic or small scale battery energy storage systems (e.g., lithium ion batteries), for example for solar panels, wind turbines, hydroelectric generators, etc.

[0035] The driving measurement system 100 may be implemented in a single processor unit (e.g., a server) including at least one processor, or in this embodiment, may be implemented in a distributed processing system including multiple processor units 100-1, 100-2, and 100-3 as shown in Fig. 2. By implementing the driving measurement system 100 in a distributed processing system, the driving measurement system 100 can be made scalable according to the processing load. For example, the number of processor units used by the driving measurement system 100 may be increased or decreased in proportion to the number of active energy supply devices in the distributed energy system.

[0036] In this embodiment, the driving measurement system 100 comprises a routing module 110 (e.g., an IoT MQTT broker) configured to receive driving data from a plurality of energy supply devices 101-105 and forward the received data in a series of messages to one or more processor units. In one embodiment, one message is received from each device 101-105 per second. In other embodiments, messages may be received more or less frequently as needed from each device 101-105. There may be cases where no messages are received from a device for a period of time, for example, when a device is offline or out of service (e.g., due to a failure or other reason not available). Each message may include multiple readings taken at one of the devices 101-105 relating to different aspects of the operation of that device, such as, for example, operating temperature, state of charge, available power, operating status (online, offline, service available / unavailable, etc.), and readings taken at multiple points in time. In one embodiment, each message may include 20 readings, although more or fewer readings may be received as needed.

[0037] Messages received from devices 101-105 form a raw data stream that first passes through a prioritization process 120 where data that is not of interest to the power grid 190 (e.g., data not related to the balancing mechanism) is filtered or removed. The removed data portions may simply be discarded if desired, but in this embodiment the removed data portions are sent to storage module 170 for analysis by monitoring module 180.

[0038] The prioritized data (e.g., available power) forms a prioritized raw stream that passes through a conversion process 130, where the prioritized data is converted to telemetry data and time-stamped. In particular, each message is separated into individual readings (e.g., 20 readings), and each reading forms an individual data file that includes metadata such as a timestamp and unique identification information including, for example, a device identifier and a reading identifier.

[0039] A selection or reduction process 140 is then performed on the transformed prioritized data to further reduce the amount of data before the data is aggregated. In particular, of the multiple individual readings from each message, one or more, but not all, readings (representative data) are selected that are representative of the current state of the device associated with that message. In this embodiment, one reading from each message is selected as representative data from that message (or of the device associated with that message). In other words, for each device in this example, the amount of data processed for aggregation is reduced from 20 readings per second to one reading per second. Different selection criteria may be used so that the data is representative of the current state of the device. In one example, of the 20 readings in the message, the most recent reading (e.g., based on a comparison of timestamps) is selected. In another example, of the 20 readings in the message, the highest or lowest reading is selected. In yet another example, of the 20 readings in the message, the reading that represents the average value is selected. Other selection criteria may be used as needed. Readings that are not selected as representative data may simply be discarded. Alternatively, in this embodiment, the non-representative readings are stored in the storage module 170 and analyzed by the monitoring module 180 .

[0040] The representative data (selected readings) proceeds to an aggregation process 150 in the VIP stream where the representative data from multiple devices 101-105 is aggregated. The aggregation process 150 may include summing the representative readings from each device or averaging all the representative readings from all devices. The aggregated data is then transmitted to the power grid 190 via the communications module 160.

[0041] Data removed during prioritization 120 and data not selected as representative during selection 140 is stored in storage module 170 so that the data may be analyzed by monitoring module 180 outside of the aggregation processing pipeline. In doing so, the amount of data that needs to be processed for aggregation is significantly reduced and the aggregation process is streamlined by maximizing the computation time available for time-critical aggregation processes. In one embodiment, latency of 5 seconds or less can be achieved with this embodiment.

[0042] The monitoring of data by monitoring module 180 may be performed in real-time. In addition to the data stored in storage module 170, monitoring module 180 may also monitor data at different stages of the aggregation process as needed to, for example, identify anomalies that may cause data from a particular device or in a particular message to be unsuitable for aggregation, such as if a device is overheating or broken or unavailable. Monitoring module 180 may be configured to apply one or more operational rules to the monitored / analyzed data to determine the status of the device (e.g., online / offline, available / unavailable) and / or to identify one or more anomalies in the data.

[0043] As will be appreciated by those skilled in the art, the present technology may be embodied as a system, method, or computer program product, and thus may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware.

[0044] Furthermore, the present technology may take the form of a computer program product embodied in a computer readable medium having computer readable program code embodied thereon. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof.

[0045] Computer program code for carrying out operations of the present techniques may be written in any combination of one or more programming languages, including object-oriented and conventional procedural programming languages.

[0046] For example, program code for carrying out operations of the present technology may include source code, object code or executable code, or assembly code, in a conventional programming language (interpreted or compiled) such as C, code for configuring or controlling an ASIC (application specific integrated circuit) or FPGA (field programmable gate array), or code in a hardware description language such as Verilog™ or VHDL (very high speed integrated circuit hardware description language).

[0047] The program code may be executed entirely on the user's computer, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer via any type of network. The code components may be embodied as procedures, methods, etc., and may include subcomponents that may take the form of any instruction or sequence of instructions at any level of abstraction, from direct machine instructions in a native instruction set to higher-level compiled or interpreted language constructs.

[0048] It will also be apparent to those skilled in the art that all or part of the logical methods according to preferred embodiments of the present technology may be suitably embodied in logic devices including logic elements for performing the steps of the methods, which may include components such as logic gates in, for example, programmable logic arrays or application specific integrated circuits. Such logic arrangements may further be embodied in enabling elements for temporarily or permanently establishing the logical structures in such arrays or circuits, for example using a virtual hardware descriptor language, which enabling elements may be stored and transmitted using a fixed or transmittable carrier medium.

[0049] The examples and conditional expressions described herein are intended to aid the reader in understanding the principles of the technology, and are not intended to limit its scope to the examples and conditions so specifically described. It will be appreciated that those skilled in the art can devise various arrangements that embody the principles of the technology and are included within its scope as defined by the appended claims, even if not explicitly described or illustrated herein.

[0050] Furthermore, to aid in understanding, the above description may describe a relatively simplified implementation of the technology, as one skilled in the art will appreciate that various implementations of the technology may be more complex.

[0051] In some cases, examples of modifications that may be useful to the technology may also be described. This is done merely to aid in understanding, and again, does not limit the scope of the technology or define the limitations of the technology. These modifications are not an exhaustive list, and one of ordinary skill in the art may make other modifications while remaining within the scope of the technology. Furthermore, if modifications are not described, it should not be interpreted that no modifications are possible and / or that what is described is the only way to implement the elements of the technology.

[0052] Furthermore, all statements herein that describe the principles, aspects, and implementations of the present technology, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof, whether currently known or developed in the future. Thus, for example, block diagrams herein will be understood by those skilled in the art to represent conceptual diagrams of exemplary circuits embodying the principles of the present technology. Similarly, any flow charts, flow diagrams, state transition diagrams, pseudocodes, and the like will be understood to represent various processes that may be executed by a computer-readable medium, whether or not a computer or processor is explicitly shown.

[0053] The functionality of the various elements illustrated in the figures, including the functional blocks labeled "processor," may be provided by the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When provided by a processor, the functionality may be provided by a single dedicated processor, a single shared processor, or multiple individual processors, some of which may be shared. Furthermore, explicit use of the terms "processor" or "controller" should not be construed to refer only to hardware capable of executing software, but may implicitly include, but is not limited to, digital signal processor (DSP) hardware, network processors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), read only memory (ROM) for storing software, random access memory (RAM), and non-volatile storage. It may also include other hardware, conventional and / or custom.

[0054] Software modules, or modules that are simply implied to be software, may be represented herein as any combination of flowchart elements or other elements illustrating the execution of process steps, and / or textual descriptions. Such modules may be executed by explicitly or implicitly shown hardware.

[0055] It will be apparent to those skilled in the art that many modifications and variations can be made to the exemplary embodiments described above without departing from the scope of the present technology.

Claims

1. 1. A computer-implemented method of operational metering for a distributed energy system arranged to supply energy to an electric power grid, the distributed energy system comprising: a plurality of energy supply devices configured to output stored energy; and an operational metering module configured to control operation of the plurality of energy supply devices, the method being performed by the operational metering module, the method comprising: receiving raw data from a plurality of energy delivery devices, the raw data including operational data for each energy delivery device; processing the raw data to remove a portion of the operational data for each energy delivery device; selecting representative data from the processed raw data of each energy delivery device representative of a state of each energy delivery device; aggregating the representative data for each energy supply device into an aggregate data package and transmitting the aggregate data to a power grid.

2. receiving a service request from the power grid, the service request being generated to request energy supply service at a level determined based on the aggregated data package; 2. The method of claim 1, further comprising: in response to the service request, outputting instructions to one or more of the plurality of energy supply devices to instruct the one or more of the plurality of energy supply devices to output stored energy to satisfy the requested level of energy supply service.

3. The method of claim 2 , wherein the removed portion of the raw data includes operational data that is not used by the power grid in determining the level of energy supply service.

4. The method of claim 3 , further comprising storing the stripped portion of the raw data for analysis.

5. The method of claim 1 , further comprising monitoring data received at and / or processed by the driving measurement module by applying one or more driving rules to the data.

6. The method of claim 5 , wherein the one or more driving rules are applied to the data to identify anomalies.

7. The method of claim 5 or 6, further comprising excluding the energy supplying device from the plurality of energy supplying devices if an anomaly is identified in the energy supplying device.

8. 8. The method of claim 1, wherein the raw data received from the plurality of energy supply devices includes a plurality of messages, each message including a plurality of readings, and further comprising processing the raw data to remove a portion of the operational data of each energy supply device, and then splitting each message in the processed raw data into a plurality of readings.

9. The method of claim 8 , wherein the plurality of readings comprises one or more temperature measurements, one or more state of charge measurements, one or more available power measurements, historical data, or any combination thereof.

10. 10. The method of claim 8 or 9, further comprising generating a data file for each of the plurality of readings, the data file for each of the plurality of readings including the reading and metadata, the metadata including a timestamp and a unique identifier for the reading.

11. The method of claim 10 , wherein the unique identifier for the reading includes a device identifier that identifies an energy delivery device from which the reading was taken and a reading identifier that identifies the reading from the plurality of readings.

12. The method of any one of claims 8 to 11, wherein the representative data for each energy supply device comprises a most recent reading of the plurality of readings, a highest reading of the plurality of readings, or an average value of the plurality of readings.

13. A computer readable medium comprising machine readable code which, when executed by a processor, causes the processor to perform the method of any one of claims 1 to 12.

14. 1. An operation measurement system for operation measurement of a distributed energy system arranged to supply energy to a power grid, the distributed energy system comprising a plurality of energy supply devices configured to output stored energy, the operation measurement system comprising: A communication module (160) configured to communicate with a power grid; and a processor unit, the processor unit comprising: at least one processor; and a memory storing machine-readable code, the machine-readable code, when executed by the at least one processor, causing the at least one processor to: receiving raw data from a plurality of energy delivery devices, the raw data including operational data for each energy delivery device; processing the raw data to remove a portion of the operational data for each energy delivery device; selecting representative data from the processed raw data of each energy delivery device, the representative data being representative of a state of each energy delivery device; aggregating the representative data for each energy supply device into an aggregate data package and transmitting the aggregate data to a power grid.

15. The system of claim 14 , comprising a plurality of processor units configured in a distributed processing system, the operational measurement system being scalable based on a number of energy supply devices in the distributed energy system.

16. Execution of the machine-readable code by the at least one processor causes the at least one processor to: receiving a service request from a power grid, the service request being generated to request a level of energy supply service determined based on the aggregate data package; outputting instructions to one or more of the plurality of energy supply devices in response to the service request to instruct the one or more of the plurality of energy supply devices to output stored energy to meet the requested energy supply service level; The system according to claim 14 or 15,

17. 17. The system of any one of claims 14 to 16, further comprising a monitoring module (180) configured to monitor data received by the processor unit and / or data being processed by the processor unit by applying one or more driving rules to the data.

18. 20. The system of claim 17, wherein one or more operating rules are applied to the data to identify anomalies, and execution of the machine readable code by the at least one processor further causes the at least one processor to exclude the energy supply from the plurality of energy supply devices if the anomaly is identified in the energy supply device.

19. 19. The system of any one of claims 14 to 18, further comprising a routing module (110) configured to receive the raw data from the plurality of energy supplying devices and forward the received raw data to the processor unit.

20. 20. The system of any one of claims 14 to 19, further comprising a storage module (170) configured to store the stripped portion of the raw data for analysis.

21. 21. The system of any one of claims 14 to 20, wherein the raw data received from the plurality of energy supply devices includes a plurality of messages, each message including a plurality of readings, and execution of the machine readable code by the at least one processor further causes the at least one processor to split each message in the processed raw data into the plurality of readings.

22. 22. The system of claim 21, wherein the plurality of readings comprises one or more temperature measurements, one or more state of charge measurements, one or more available power measurements, historical data, or any combination thereof.

23. 23. The system of claim 21 or 22, wherein execution of the machine readable code by the at least one processor further includes causing the at least one processor to generate a data file for each of the plurality of readings, the data file for each of the plurality of readings including the reading and metadata, the metadata including a timestamp and a unique identifier for the reading.

24. 24. The system of any one of claims 21 to 23, wherein the representative data for each energy delivery device comprises a most recent reading of the plurality of readings, a highest reading of the plurality of readings, or an average value of the plurality of readings.

25. A plurality of energy supply devices (101, 102, 103, 104, 105), each configured to output stored energy; and A driving measurement system (100) according to any one of claims 14 to 24, A distributed energy system for supplying energy to a power grid (190).

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