Real-time energy tracking method and system

Abstract

Real-time energy tracking method and system. A computer-implemented method, computer program, and system for real-time energy tracking are provided. The system includes steps of measuring in real time, by an energy generation sensor, the energy generated by an energy generator and obtaining a plurality of generated energy measurement values over a certain period; measuring in real time, by an energy consumption sensor, the energy consumed by an energy consumer and obtaining a plurality of consumed energy measurement values over a certain period; receiving a plurality of generator data packets, each generator data packet including one of the plurality of generated energy measurement values, and the associated generator metadata including time data corresponding to the time when one of the plurality of generated energy measurement values was measured; receiving a plurality of consumption data packets, each consumption data packet including one of the plurality of consumed energy measurement values, and the associated consumer metadata including time data corresponding to the time when one of the plurality of consumed energy measurement values was measured; matching the plurality of generated energy measurement values with the plurality of consumed energy measurement values according to the time data from each generator metadata and consumer metadata to obtain a plurality of sets of matching measurement values over a certain period; and for each of the sets of matching measurement values over a certain period, determining and outputting a real-time energy shortage or excess by comparing the generated energy measurement value with the consumed energy measurement value that matches it.

Description

Technical Field

[0001] The present invention relates to the tracking of energy in an energy distribution network or grid, and more particularly to the real-time measurement and tracking of energy generation and consumption.

Background Art

[0002] In energy networks and power grids, energy is typically generated at multiple energy generation sites and provided to multiple consumers. Energy generation sites can be associated with various types and sources of energy and can be managed by various operators. Similarly, consumers can be different individuals, companies, etc., and the amount of energy consumed also varies.

[0003] Energy generators and consumers are connected by a network of power lines, distribution lines, substations, and energy storage facilities.

[0004] Energy consumption by consumers is not constant, and therefore, energy demand varies greatly depending on multiple factors such as time, weather, consumer habits, and consumer location.

[0005] Energy networks or grids are increasingly including energy from renewable or "green" energy sources, but still include energy generated from conventional energy sources such as coal and natural gas. During peak demand periods, consumers can expect to be supplied with energy from a variety of energy sources, including both green and conventional energy sources.

[0006] Currently, the energy within a network or grid is not precisely tracked. The total energy generated and consumed can be recorded in individual blocks every hour or every 30 minutes. However, when using such individual blocks, problems arise because the energy generated within a block may not correlate with the energy that is seemingly consumed within the block. For example, an energy consumer may consume 4 kWh of energy in the first 5 minutes of a 30 - minute block, while an energy generator may generate 10 kWh of energy in the last 10 minutes of the 30 - minute block. However, since the blocks are individual and do not provide more detailed information, the block only shows that 4 kWh is consumed while 10 kWh of energy is generated. Thus, from this 30 - minute data block, it appears that the generator satisfied the 4 kWh demand from the energy consumer and could supply an additional 6 kWh of excess energy to other consumers. This is incorrect because in reality, only 10 kWh of energy was generated in the last 10 minutes of the 30 - minute block after the 4 kWh energy consumer demand in the first 5 minutes. Thus, in reality, during the first 5 minutes of the block, the energy consumer was being supplied with energy from a source different from the energy generator.

[0007] Therefore, using blocks every hour or every 30 minutes results in inaccuracies and low reliability, and data regarding actual energy generation and consumption is lost. Rather, these blocks only provide an estimate or prediction of which energy sources contributed to the consumed energy.

[0008] A further drawback associated with the use of such individual blocks is that it is impossible to accurately match a specific energy generator to an energy consumer.

[0009] Furthermore, since many energy generators do not provide information or generate only their own data, some of the energy generated by certain energy generators may not be included in such energy blocks.

[0010] Accordingly, it has been recognized that there is a need for more accurate and reliable methods and systems for energy tracking.

SUMMARY OF THE INVENTION

[0011] This summary is provided to introduce a selection of concepts in a simplified form that are further described in the detailed description below. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used to determine the scope of the claimed subject matter. Variations and alternative features that facilitate implementation of the invention and / or achieve substantially similar technical effects are considered to be within the scope of the invention disclosed herein.

[0012] In a first aspect, the present disclosure is a computer-implemented real-time energy tracking method, comprising: measuring, in real time by an energy generation sensor, the energy generated by an energy generator, and obtaining a plurality of generated energy measurement values over a certain period; measuring, in real time by an energy consumption sensor, the energy consumed by an energy consumer, and obtaining a plurality of consumed energy measurement values over a certain period; receiving a plurality of generator data packets, each of the generator data packets including one of the plurality of generated energy measurement values, and associated generator metadata including time data corresponding to the time at which one of the plurality of generated energy measurement values was measured; receiving a plurality of consumption data packets, each of the consumption data packets including one of the plurality of consumed energy measurement values, and associated consumer metadata including time data corresponding to the time at which one of the plurality of consumed energy measurement values was measured; matching the plurality of generated energy measurement values with the plurality of consumed energy measurement values according to the time data from the generator metadata and each consumer metadata, and obtaining a plurality of sets of matching measurement values over a certain period; and for each set of matching measurement values over a certain period, determining and outputting a real-time energy shortage or excess by comparing the generated energy measurement value with the consumed energy measurement value that matches it.

[0013] By measuring and recording the generated energy measurement values and the consumed energy measurement values at high frequency and in real time, the measurement values are highly reliable and accurately matched, enabling the determination of real-time energy excess or shortage at a specific point in time. In this way, by matching the generated energy and the consumed energy, it is ensured that no contradictions or mismatches occur between the generated energy and the consumed energy over a long period.

[0014] A set of matching measurement values can be regarded as a linked data group. The set of matching measurement values can include only generated energy measurement values, or only consumed energy measurement values, for example, when the energy generator is not active during the instance.

[0015] Preferably, for each of a plurality of sets of matching measurement values, the method includes obtaining a grid carbon intensity related to a power grid connected to the energy consumer, obtaining an energy generator carbon intensity related to the energy generator, and determining a consumer carbon intensity related to the set of matching measurement values, wherein the consumer carbon intensity is a weighted average of the grid carbon intensity weighted according to a real-time energy shortage of the set of matching measurement values and the energy generator carbon intensity weighted according to the corresponding generated energy measurement value of the set of matching measurement values, the step, and the step of outputting the consumer carbon intensity.

[0016] Consumer carbon intensity can also be simply called the carbon intensity of energy consumption, and is a measure of the mass of carbon dioxide per kilowatt-hour of generated energy. This indicator can be usefully output to track how "green" a consumer's energy consumption is, and more generally to track the carbon intensity of energy generation.

[0017] Preferably, for each set of matching measurement values over the certain period, the method further includes accumulating the consumer carbon intensity, averaging the consumer carbon intensity over the certain period to determine an average consumer carbon intensity, and outputting the average consumer carbon intensity.

[0018] Accordingly, the carbon intensity can be averaged over a longer period, such as one day, to indicate the average carbon intensity of the consumer's energy consumption. The average is calculated weighted according to the total energy consumed in each set of matching measurements.

[0019] Preferably, the step of obtaining the grid carbon intensity includes determining an energy consumer associated with the measured energy consumption value, determining an alternative energy supplier associated with the energy consumer, and obtaining an alternative supplier carbon intensity associated with the alternative energy supplier such that the grid carbon intensity is set as the alternative supplier carbon intensity.

[0020] Using the grid carbon intensity can take into account not only the directly monitored energy generators but also those not directly monitored. The grid carbon intensity can be obtained from direct measurement, sampling, or third-party information. Using the carbon intensity of the consumer's alternative supplier can obtain a very accurate value of the grid carbon intensity because in case of energy shortage, the energy consumer obtains energy from the alternative supplier's grid.

[0021] Preferably, the method further includes determining a real-time carbon content related to the energy consumed by the energy consumer in the set of matching measurements from the consumer carbon intensity of the set of matching measurements and the measured energy consumption value in the set of matching measurements, and outputting the real-time carbon content.

[0022] The real-time carbon content is expressed in grams of carbon dioxide emissions and serves as a measure of the consumer's carbon emissions from the perspective of energy consumption.

[0023] Preferably, the method further includes comparing the consumer carbon intensity with a first threshold value, and when the consumer carbon intensity matches or exceeds the first threshold value, taking an action to reduce the consumer carbon intensity. Therefore, actions can be executed according to the case where the consumer carbon intensity value is high, so that the consumer carbon intensity can be surely reduced.

[0024] Preferably, the action to reduce the consumer carbon intensity includes at least one of activating an alarm to warn the energy consumer, activating an additional energy generator or sending an instruction to increase the energy generation by the energy generator to increase the generated energy, and notifying the energy consumer to reduce the energy consumption.

[0025] The actuated energy generator and / or additional energy generator can be a green energy generator associated with a relatively low or zero value carbon intensity. Thus, increasing energy generation or activating energy generation from said additional energy generator and / or energy generator will result in more "green" energy being generated as a percentage of the total energy generated. This means that overall, the carbon intensity of energy generation and ultimately the consumer carbon intensity associated with the energy consumption of the energy consumer is reduced. Notifying the energy consumer to reduce energy consumption can include sending warnings, reminders, etc. for display on devices associated with the consumer, or sending other notifications. The device can be a personal computing device associated with the consumer, such as a computer, tablet, mobile phone, etc. Alternatively, the device can be part of or incorporated into an energy consumption sensor. Thus, the energy consumption sensor can include a display, speaker, etc. for notifying or providing information to the consumer. The actions performed in this way can be communicated to the consumer device via a suitable communication channel such as the Internet, telephone, Wi-Fi, LAN, mobile phone data, or other communication network.

[0026] Preferably, the period is 1 second, 1 minute, 1 hour, 1 week, 1 month, or 1 year. Thus, the consumer carbon intensity, energy deficit or surplus, and / or real-time carbon content determined based on a set of real-time matching measurements can be averaged and accumulated over a longer period. Since these averages and accumulations depend on real-time measurements, accuracy and reliability are maintained.

[0027] Preferably, the method further includes measuring at a frequency of 1 Hz or more and in real time.

[0028] Preferably, there are a plurality of the energy generators, whereby the method measures, in real time, the energy generated by the plurality of energy generators by means of a plurality of energy generation sensors, and obtains a plurality of generated energy measurement values for each of the plurality of energy generators over a certain period; receives a plurality of generator data packets for each of the energy generators, each of the generator data packets including one of the plurality of generated energy measurement values, and the associated generator metadata including time data corresponding to the time when one of the plurality of consumed energy measurement values was measured; matches the plurality of generated energy measurement values from each of the plurality of energy generators with the plurality of consumed energy measurement values according to the time data from the generator metadata and each of the consumer metadata, to obtain a set of a plurality of matching measurement values, each of the sets of matching measurement values including the generated energy measurement value from each energy generator; and for each set of the matching measurement values over a certain period, determines and outputs a real-time energy shortage or excess by comparing the generated energy measurement values from each of the plurality of energy generators with the consumed energy measurement values that match them.

[0029] Accordingly, a set of matching measurement values includes a plurality of generated energy measurement values corresponding to a plurality of energy generators. These plurality of matched generated energy measurement values match each other and can also match the consumed energy measurement values. Accordingly, the method can advantageously incorporate measurement values and data from various sources.

[0030] Preferably, the plurality of energy generators includes multiple types of energy generators. The energy generators may include green and / or conventional energy generators. Examples of the types of energy generators may include wind power, solar power, tidal power, hydro power, nuclear power, gas, coal, etc. The plurality of energy generators may include different energy generators of the same type.

[0031] Preferably, the method further includes determining, from the measured energy generation values of each of the energy generators, a real-time percentage of the total energy generated by the plurality of energy generators that belongs to each individual and / or type of the energy generators, and outputting a real-time percentage of the total energy generated by the plurality of energy generators that belongs to each type of the energy generators.

[0032] The real-time percentage may be a percentage of the total energy generated. Since the percentage is determined from a set of matching measurement values, the real-time aspect of the set of matching measurement values is preserved in the real-time percentage. The real-time percentage may be averaged over a certain period according to the total energy generated over multiple sets of matching measurement values. The real-time percentage advantageously provides an indicator of which of the plurality of energy generators is supplying the most or least energy at any given time.

[0033] Preferably, there are a plurality of the energy consumers, whereby the method includes steps of measuring, by the plurality of energy consumption sensors, the energy consumed by the plurality of energy consumers; receiving, for each of the energy consumers, a plurality of consumption data packets, each consumption data packet including one of the plurality of measured consumed energy values, and the associated consumer metadata including time data corresponding to the time at which one of the plurality of measured consumed energy values was measured; matching, according to the generator metadata and the time data from each consumer metadata, the plurality of generated energy measurement values with the plurality of consumed energy measurement values from each of the plurality of energy consumers to obtain a set of a plurality of matching measurement values over a certain period, each set of the matching measurement values including the consumed energy measurement value from each energy consumer; and determining and outputting, for each set of the matching measurement values over a certain period, a real-time energy shortage or excess by comparing the generated energy measurement value with the matching consumed energy measurement value from the plurality of energy consumers.

[0034] Accordingly, the method can be executed with respect to a plurality of consumers and a plurality of energy generators.

[0035] Preferably, the step of determining the consumer carbon intensity for each set of matching measurement values includes calculating the individual consumer carbon intensity of each energy consumer represented by the set of matching measurement values by obtaining the carbon intensity of an alternative supplier of each energy consumer represented by the set of matching measurement values, dividing the real-time energy shortage of the set of matching measurement values into a plurality of energy shortage portions based on the consumed energy measurement values within the set of matching measurement values, where each energy shortage portion belongs to the energy consumer of the plurality of energy consumers represented by the set of matching measurement values, taking a weighted average of the individual alternative supplier carbon intensities of each energy consumer weighted according to the energy shortage portion caused by the energy consumer and the carbon intensity of the energy generator weighted according to the corresponding generated energy measurement value of the set of matching measurement values to calculate the individual consumer carbon intensity of each energy consumer, and outputting the individual consumer carbon intensity of each energy consumer represented by the set of matching measurement values.

[0036] Determining the individual consumer carbon intensity of each energy consumer according to that energy consumer means that the consumer carbon intensity is more accurate and reliable based on the specific situation of the energy consumer. In particular, when determining the consumer carbon intensity based on the carbon intensity of an alternative supplier related to a specific energy consumer rather than using the carbon intensity of the power grid, the actual carbon intensity value of the energy supplied from outside one or more energy generators monitored by the energy generator sensor is reflected more accurately and precisely.

[0037] When a plurality of energy generators are being monitored, multiple energy generators can be considered in the determination of the individual consumer carbon intensity.

[0038] The determination of the individual carbon intensities is determined using the average of all the carbon intensities of the energy generators and the alternative supplier carbon intensities, where the alternative supplier carbon intensities are weighted according to the determined energy shortfall. The energy shortfall may be proportional to the energy consumed by each energy consumer across a plurality of energy consumers. Alternatively, the energy shortfall may be determined by equally dividing the energy shortfall among the energy consumers, or by dividing the energy shortfall according to the priority order of the energy consumers. A smaller shortfall is attributed to the consumers with a higher priority order.

[0039] Preferably, the generator metadata includes one or more aspects of data indicating the type of the energy generator used, data indicating the entity associated with the energy generator used, and / or data indicating the activity or state associated with the energy generator used, and / or the consumer metadata includes one or more aspects of data indicating the type of the energy consumer, data indicating the entity associated with the energy consumer, and / or data indicating the activity or state associated with the energy consumer.

[0040] Preferably, the method further includes the steps of matching the plurality of generated energy measurement values with the plurality of consumed energy measurement values according to one or more aspects of the generation metadata and / or the consumer metadata to obtain a plurality of sets of secondary matching measurement values over a certain period, and outputting the secondary matching measurement values.

[0041] In this way, a secondary set of matching measurement values, or a linked secondary data group, can be formed. Similar processing regarding the energy mix, carbon intensity, and carbon content can also be performed on the secondary set of matching measurement values. The matching measurement values can be stored in a relational database.

[0042] Preferably, the output includes storing the data in a database in a pre-set format.

[0043] Storing in a pre-set format enables comparison, accumulation, or collective processing of each set of matching measurement values. Also, third parties such as energy consumers or operators of energy generators can access the data stored in the database without converting or restructuring the data.

[0044] Preferably, the output further includes outputting any one or more of a set of matching measurement values according to the time metadata, the consumer metadata, and the generator metadata.

[0045] Accordingly, a set of matching measurement values and / or a secondary set of matching measurement values can be stored, displayed, or processed based on common metadata.

[0046] Preferably, the method further includes storing in a directed acyclic graph at least two of a display of the energy generator, a display of the energy consumer, a plurality of sets of matching measurement values between the energy generator and the energy consumer, a real-time energy deficit or surplus determined for each matching measurement value over a certain period, and a record of one or more calculations corresponding to one or more determination steps.

[0047] The acyclic graph advantageously provides data sources such as sensors of energy generators and energy consumers, operations on data such as calculations of totals, averages, deficits / surpluses, carbon content, energy mix, carbon intensity, and an audit trail of the results provided by these operations. This enables important analysis by third parties such as energy consumers or operators of generators.

[0048] The pre-set format is Provenance (PROV), and it is desirable to use the Provenance Data Model (PROV-DM). The vocabulary of PROV-DM can be adjusted based on a specific implementation of the energy tracking method and the specific application in which it is deployed.

[0049] Preferably, the method further includes storing the hash of the directed acyclic graph in a blockchain or distributed ledger using a cryptographic hash function.

[0050] The hash can be a hash of the directed acyclic graph and / or a set of matching measurements. A set of matching measurements, or multiple sets of matching measurements, form a data set. The hash and the data set can be used to confirm that the data set is valid and has not been tampered with using an appropriate algorithm.

[0051] Preferably, the method further includes measuring, in real time, the energy generated by the energy generator with a first group of sensors, the first group of sensors including at least two types of sensors arranged within the energy generation chain and / or at the same location as the energy generation sensor, together with the energy generation sensor.

[0052] When multiple sensors are arranged on the energy generator to form the first group of sensors, the redundancy of the sensors is ensured, and even if one sensor in the first group fails, the energy generated can be measured using another sensor. When multiple sensors are arranged in the first group, the failure can also be identified. The first group of sensors can include secondary sensors such as weather sensors, temperature sensors, humidity sensors, irradiance sensors, etc. By using such sensors in combination with the energy generation sensor, context regarding the measured generated energy value can be provided.

[0053] Preferably, the method further includes a step of verifying the generated energy measurement value of the energy generation sensor based on the measurement values of the first sensor group.

[0054] Preferably, the method further includes a step of measuring the energy generated by the renewable energy generator.

[0055] Preferably, the method further includes a step of synchronizing a first clock signal used by the energy generation sensor with a second clock signal used by the energy consumption sensor before measurement.

[0056] By synchronizing the first clock signal and the second clock signal, high-frequency real-time measurements can be accurately matched. The clock signals can be synchronized periodically, for example, every 1 second, every 10 seconds, every 1 minute, every 5 minutes, every 10 minutes, every 1 hour, or every 1 day.

[0057] According to a second aspect, there is provided a real-time energy tracking system including one or more energy generation sensors configured to measure in real time the energy generated by one or more energy generators, one or more energy consumption sensors configured to measure in real time the energy consumed by one or more energy consumers, and a processor communicably connected to the one or more energy generation sensors and the one or more energy consumption sensors, the processor being configured to execute the method described in the first aspect.

[0058] According to a third aspect, a computer program for performing real-time energy tracking, which, when executed by the processor, comprises: receiving a plurality of generator data packets, each generator data packet including one of a plurality of real-time generated energy measurement values, and the associated generator metadata including time data corresponding to the time at which one of the plurality of real-time generated energy measurement values was measured; receiving a plurality of consumption data packets, each consumption data packet including one of a plurality of real-time consumed energy measurement values, and the associated consumer metadata including time data corresponding to the time at which one of the plurality of real-time consumed energy measurement values was measured; matching the plurality of generated energy measurement values with the plurality of consumed energy measurement values according to the time data from the generator metadata and each consumer metadata to obtain a plurality of sets of matching measurement values over a certain period; and for each set of matching measurement values over a certain period, determining and outputting a real-time energy shortage or excess by comparing the generated energy measurement value with the consumed energy measurement value that matches it. A computer program is provided that is configured to cause the processor to execute the above steps.

[0059] According to an additional aspect, there is provided an additional energy tracking method, comprising the steps of: measuring in real time, by an energy generation sensor, the energy generated by an energy generator, and obtaining a plurality of measured energy generation values over a certain period; measuring in real time, by an energy consumption sensor, the energy consumed by an energy consumer, and obtaining a plurality of measured energy consumption values over a certain period; receiving a plurality of generator data packets, each of the generator data packets including one of the plurality of measured energy generation values, and the associated generator metadata including time data corresponding to the time at which one of the plurality of measured energy generation values was measured; receiving a plurality of consumption data packets, each of the consumption data packets including one of the plurality of measured energy consumption values, and the associated consumer metadata including time data corresponding to the time at which one of the plurality of measured energy consumption values was measured; matching the plurality of measured energy generation values with the plurality of measured energy consumption values according to the time data from the generator metadata and each consumer metadata, and obtaining a plurality of sets of matching measured values over a certain period; and for each set of matching measured values over a certain period, determining and outputting the carbon intensity of the generated energy based on the carbon intensity of the energy generator.

[0060] There may be multiple energy generators and / or energy consumers. In this case, the carbon intensity of the generated energy can be determined by calculating a weighted average of the carbon intensities of the multiple energy generators, weighted based on the amount of energy generated by each of the multiple energy generators.

[0061] The carbon intensity of the generated energy can be further averaged over the multiple sets of matching measured values to provide a carbon intensity score over a certain period.

[0062] Additional aspects may be combined with one or more features of the first aspect described above.

[0063] The methods described herein can be implemented in the form of a computer program comprising computer program code means adapted to perform all the steps of any of the methods described herein when the program is run on a computer, in machine-readable form on a tangible storage medium by software, the computer program being embodied on a computer-readable medium. Examples of tangible (or non-transitory) storage media include disks, thumb drives, memory cards, etc., and do not include propagated signals. The software is suitable for execution on parallel or serial processors and the steps of the method can be performed in any suitable order or simultaneously.

[0064] This application recognizes that firmware and software have value and can be a separately tradable commodity. This is designed to include software that is operated or controlled on "dumb" or standard hardware to perform desired functions. It also includes software that "describes" or defines the configuration of hardware, such as HDL (Hardware Description Language) software used in the design of silicon chips or the configuration of field-programmable gate arrays to perform the desired functions.

[0065] Preferred features can be combined as appropriate, as will be apparent to those skilled in the art, and can also be combined with any aspect of the present invention. Embodiments of the present invention will be described by way of example with reference to the following drawings.

Brief Description of the Drawings

[0066]

Figure 1

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Mode for Carrying Out the Invention

[0067] The present application relates to a system and method for performing accurate and reliable energy tracking in an energy network or power grid.

[0068] The system for performing energy tracking can be added to an existing energy network. Here, the terms "energy network" and "power grid" are used interchangeably.

[0069] This system can be used to track green energy, which is energy with low carbon dioxide emissions. This system can track, store, and output useful parameters such as energy surplus (excess) and energy shortage by comparing the generated energy (supply) and the consumed energy (demand). The comparison of the generated energy and the consumed energy is performed based on the recorded data measured and recorded at high frequency and in real time. The data points in the recorded data are matched with each other to create a data set. The matched data set can be regarded as a linked data group or a set of matched measurement values. This data set can be stored in a relational database. Useful parameters include the carbon content, also called the real-time carbon content, which means the amount of carbon dioxide resulting from energy consumption, the consumer carbon intensity and the carbon intensity, also called the carbon intensity score, which means the amount of carbon dioxide per unit of generated energy / power, and the energy mix, also called the ratio or percentage of the generated energy.

[0070] Figure 1 shows a schematic diagram of an embodiment of an energy tracking system 100 operating within a power grid. The energy tracking system 100 includes one or more energy generation sensors 102a, one or more energy consumption sensors 104a, and a computing device 108.

[0071] The power grid includes one or more energy generators 102, one or more energy consumers 104, and a network 106 of connection lines, transmission lines, storage mechanisms, and substations that supply energy from the energy generator 102 to the energy consumer 104. Figure 1 shows multiple energy generators 102 and energy consumers 104.

[0072] The energy generator sensor 102a directly measures the energy generated by each energy generator 102. The energy consumption sensor 104a directly measures the energy consumed by each energy consumer 104. The measurement values obtained by the energy generator sensor 102a and the energy consumption sensor 104a are sent to the computing device 108 for processing.

[0073] The energy generators 102 monitored by the energy generation sensors can include any type of energy generator. In one example, the energy generator 102 includes at least one type of renewable energy generator such as a solar energy generator, a wind energy generator, a tidal energy generator, or a hydro energy generator. The energy generator 102 may be a nuclear energy generator or a conventional energy generator such as an energy generator powered by gas or coal.

[0074] The energy generator sensor 102a is configured to measure at least the energy generated by the energy generator 102 supplied to the power grid. The energy generator sensor 102a is configured to detect and measure in real time the energy generated by the energy generator 102 and provide continuous data reflecting the energy output of the energy generator 102. The energy generator sensor 102a can be a power meter or a smart meter that is sensitive at high frequencies (≧1 Hz). The energy generator sensor 102a is configured to provide data to the computing device 108 in the form of pre-set energy generator data packets. The energy generator data packets can be directly assembled and transmitted by the energy generator sensor 102a or formed by each computing device connected to or incorporated into the energy generator sensor 102a. For example, smart meters and power meters usually include programmable computers and can be programmed to provide data in data packets according to pre-set formats. Additionally or alternatively, the energy generator sensor 102a includes software or a hardware plugin that captures raw sensor data and uses this to generate energy generator data packets according to pre-set formats. In this way, the system can collect data in real time using the existing energy generator sensor 102a and provide energy generator data packets in the required format. Alternatively, the data can be received at the computing device 108 and then converted or extended to be stored in the pre-set format as energy generator data packets.An energy generator data packet includes measurement values or readings from the energy generator sensor 102a of the energy generated by the energy generator, and associated generator metadata indicating the measurement time at which the measurement or reading was performed by the energy generator sensor 102a. The measurement time may be referred to as time metadata. The data packet of the energy generator may further include additional metadata. The additional metadata may include at least one or more of the display of the energy generator at which the measurement was taken, the display of a particular component or part of the energy generator at which the measurement was taken, identification information related to the energy generator such as the owner, company, name, etc., the activities related to the energy generator at the time the measurement was taken, and other auxiliary sensor data. The additional metadata may be added at the energy generator sensor 102 and / or the computing device 108 and may form the energy generator data packet in a pre-set format.

[0075] Activities related to an energy generator can be actions performed on the energy generator at the time of measurement or actions performed by the energy generator. For example, the energy generator can be in a repair, shutdown or low-output state, a maintenance state, or an active energy generation state. Thus, this activity can generate energy. For example, if the energy generator is a solar power plant, the work performed at the solar power plant can be "cleaning", in which case the solar power modules (PVMs) undergo automatic or manual cleaning maintenance operations. Further auxiliary sensor data included in the metadata can include environmental parameters such as the temperature, humidity, solar irradiance, wind direction, wind speed, and precipitation of the energy generator. If the auxiliary sensor data is included in the metadata of the energy generator data packet, the energy generator packet is generated by each computing device connected to the energy generator sensor 102a or by the computing device 108. Each computing device is connected to the energy generator sensor 102a and one or more auxiliary sensors configured to provide auxiliary sensor data. Each computing device is configured to use this information for collation and provide the energy generator data packet to the computing device 108 of the system 100. Alternatively, one or more auxiliary sensors directly provide the auxiliary sensor data to the computing device 108. The auxiliary sensor data can then be incorporated into the energy generator data packet formed at the computing device 108. In this case, the data of the auxiliary sensor can be matched with the measurement data of the energy generator based on the time metadata and additional metadata such as the location of the auxiliary sensor and the ID of the energy generator. As described above, the data packet of the energy generator is constructed at the computing device 108 to be in a preset format.

[0076] If either the measurement data or the associated metadata is not in a pre-set format, each computing device is configured to convert or reconfigure the data and associated metadata into a pre-set format for sending energy generator data packets to computing device 108. The data can be reconfigured into the pre-set format by the energy generator sensor 102a, each computing device connected to or incorporated in the energy generator sensor 102a, or an extended process executed on the computing device 108. The pre-set format will be described in detail later.

[0077] The energy generator sensor 102a is configured to measure the energy generated by the energy generator 102 and provide an energy generator data packet to the computing device 108, while the energy consumption sensor 104a is configured to perform a similar function with respect to the energy consumer 104. In particular, the energy consumption sensor 104a is configured to measure at least the energy consumed by the energy consumer 104 from the power grid. The energy consumption sensor 104a is configured to detect and measure in real time the energy consumed by the energy consumer 104 and provide continuous data reflecting the energy demand of the energy consumer 104. The energy consumption sensor 104a can be a smart meter. The smart meter is configured to detect and transmit energy consumption data in real time at a high frequency (≧1Hz). The energy consumption sensor 104a can be configured to transmit data in a pre-set format. The energy consumption sensor 104a is configured to provide data to the computing device 108 in an energy consumption data packet of a pre-set format. The energy consumption data packet can be directly assembled and transmitted by the energy consumption sensor 104a, or can be formed by each computing device connected to the energy consumption sensor 104a. The energy consumption data packet includes a measurement value or reading of the energy consumed by the energy consumer 104 by the energy consumption sensor 104a, and associated consumer metadata indicating the measurement time at which the measurement or reading was performed by the energy consumption sensor 104a. The measurement time can be referred to as time data. The energy consumption data packet can further include additional metadata.The additional metadata may include at least one or more of the display of the energy consumer where the measurement was taken, the display of a specific component or part of the energy consumer where the measurement was taken, identification information related to the energy consumer such as the owner, company, name, etc., the location of the energy consumer 104, the activities related to the energy consumer 104 at the time the measurement was taken, and other auxiliary sensor data.

[0078] Activities related to the energy consumer can be the actions performed by the energy consumer 104 at the time the measurement was taken. For example, the energy consumer 104 can perform energy consumption actions such as turning on lighting, electrical appliances, or electrical systems at the energy consumer 104. Further auxiliary sensor data included in the metadata can include parameters such as the light level of the energy consumer 104 and the temperature of the energy consumer 104. When the auxiliary sensor data is included in the metadata of the energy consumption data packet, the energy consumption data packet is generated by each computing device connected to the energy consumption sensor 104a. Each computing device is connected to the energy consumption sensor 104a and each of the auxiliary sensors configured to provide auxiliary sensor data. Each computing device is configured to use this information to provide an energy consumption data packet to the computing device 108 of the system 100.

[0079] If either the measurement data or the related metadata is not in a pre - set format, each computing device is configured to convert or re - configure the data and related metadata to be in a pre - set format for transmitting the energy consumption data packet to the computing device 108. The pre - set format will be described in detail later.

[0080] Computing device 108 is configured to receive and store energy consumption data packets and energy generator data packets from energy consumer 104 and energy generator 102 respectively. In computing device 108, the energy consumption data packets and the energy generator data packets are matched to each other according to their time data. In particular, when the energy generator data packet contains time data indicating a system time or time that matches the system time or time of the energy consumption data packet, computing device 108 is configured to link these specific data packets so that a relationship is formed between the data packets. This can be implemented in several ways. In one example, the link between data packets is a shared timestamp of one or more data packets. Each data packet is assigned a packet identifier (ID) that can be a unique number, a string, etc. The link is maintained or stored in a relational database by storing the IDs of the linked data packets together. As will be understood by those skilled in the art, other methods of storing linked data packets can also be implemented.

[0081] Multiple energy generator data packets can be linked to individual energy consumption data packets, and similarly, multiple energy consumption data packets can be linked to individual energy generator data packets. Thus, computing device 108 can form links between multiple energy generator data packets and multiple energy consumption data packets according to the time data of these data packets. Further, an energy generator data packet can be linked to other energy generator data packets according to its time data, and similarly, an energy consumption data packet can be linked to other energy consumption data packets according to its time data. This can occur when there are multiple consumers 104 and energy generators 102 measured within system 100.

[0082] For example, the first energy generator sensor may provide the computing device 108 with a first energy generator data packet indicating a measured value of 1.527 kWh generated by the first energy generator at 07:04:14 on February 20, 2022. In this example, the time and date represent the time data of the energy generator sensor data packet. The second energy generator sensor associated with the second energy generator may provide a second energy generator data packet indicating a measured value of 0.473 kWh generated by the second energy generator at 07:04:14 on February 20, 2022. When the computing device 108 receives the first and second energy generator packets, the time metadata of these packets is compared. Since the time and date of the second energy generation packet, and thus the time metadata, matches the time and date of the first energy generation packet, and thus the time metadata, the computing device 108 is configured to link the first energy generation packet and the second energy generation packet. The first energy consumption sensor may also provide a first energy consumption data packet indicating a measured value of 0.5 kWh consumed at 07:04:14 on February 20, 2022. In this case, the first energy consumption data packet is linked to the first and second energy generation data packets based on matching time metadata.

[0083] By using real-time sensing at both the energy generator and consumer of the system, it is guaranteed that computing device 108 receives data packets corresponding to a continuous stream of measurement data from both the energy generator and consumer. This means that the data can be continuously linked so that there are no discontinuous temporal jumps in the measured values of energy generation and consumption. In other words, by frequently measuring in real time the energy generated and consumed by the sensors, computing device 108 can calculate the energy within system 100 at very narrow time intervals (e.g., less than 1 second) rather than at longer discrete time intervals (e.g., 30 minutes).

[0084] As explained above, when data is collected over long discrete periods, it is not possible to accurately match the energy consumption data and the energy generation data. In particular, measuring data over longer periods increases the likelihood of incorrect matching of the consumed and generated energy and results in inaccurate statistics of generation and consumption. For example, the energy generated may appear to match the energy consumed over a longer discrete period, but the energy may be generated at a time different from when the energy is consumed within that period. In this case, an energy mismatch occurs because the generated and consumed energy are not the same. By using frequent real-time data, these problems are resolved and computing device 108 can effectively store the measured values of energy generation and consumption that provide an instantaneous snapshot of system 100 at a particular moment.

[0085] It should be understood that the real-time sensing of the energy generator sensor 102a and the energy consumption sensor 104a is performed at a high frequency. For example, the real-time sensing can occur at a rate of 1 Hz, 5 Hz, or more. For example, the sensing can occur at 1 - 100 Hz. The real-time aspect of the sensing represents how often and how fast the sensor measurements are generated and stored. "Real-time" is considered to include the latency time required to perform these operations computationally. The frequency at which the measurement data and related time metadata are sensed and stored is preferably a frequency of 1 Hz or more. However, it should be understood that the frequency of measurement and storage of measurement data can be extended to measurements every minute, every two minutes, and every five minutes without losing the advantages of the systems and methods described herein. The measurement of the energy generator and the measurement of energy consumption can be performed at a frequency according to the constraints or attributes of the energy generator 102. One such constraint can be the startup time of the energy generator 102 being monitored. For example, in the case of a gas generator, the startup time is about 10 seconds. To ensure that the energy measurements are accurately and precisely collected from the gas generator (so as not to miss the ramp-up), the frequency of real-time measurement needs to be equal to or faster than one measurement every 10 seconds (0.1 Hz). Therefore, it is desirable to perform measurements at a frequency exceeding 1 Hz, and it is also possible to perform measurements between once per second and once per five minutes.

[0086] The link between data packets can be established by any suitable means, such as using a linked relational database, adding relationship metadata to each data packet, or storing the data packet together with or following the linked data packet. Data packets sharing a link can be called a linked data group. There are multiple linked data groups, each corresponding to a different time.

[0087] The energy generator sensor 102a and the energy consumption sensor 104a are configured to operate in real time to frequently measure the generated energy and the consumed energy, but it should be understood that it is not essential for data packets containing measurement data to be transmitted to the computing device 108 in real time and / or for the computing device 108 to match the measured values in real time, and this is optional. In some embodiments, the data packet can be transmitted after a certain period of time from the time of measurement and / or can be locally stored in the sensor or each computing device before being transmitted to the computing device 108 at predetermined intervals. In this way, the data packets can be transmitted to the computing device 108 in batches. Similarly, the computing device 108 can execute steps of comparing the received data packets and matching the data packets at predetermined intervals. By performing the processing of the sensor measurement data in this way rather than in real time, the sensor / computing device 108 can be configured to transmit and receive at predetermined intervals, so it is not necessary for the communication between the sensor and the computing device 108 to be always active, and the communication efficiency of the system is improved. Also, since the matching only needs to be performed at predetermined intervals, the power / calculation efficiency of the computing device 108 is also improved. In fact, the system 100 can process the data after a certain period of time from when the data is acquired / measured in order to optimize the processing efficiency. The measurement itself is performed in real time, and the data packet containing the measured value also includes time metadata indicating the measurement time, so the real-time nature of the data is maintained even when it is processed later. Therefore, the comparison and matching of data by the computing device 108 can include the comparison and matching of historical data packets.

[0088] After a computing device matches one or more data packets (energy generator and / or energy consumption data packets) to form at least one linked data group, the computing device 108 is configured to further process the linked data group to compare the generated energy with the energy consumed within the linked data group.

[0089] First, a comparison is performed between the energy generated and the energy consumed in the linked data group, and the presence of an energy deficit or energy surplus is determined. If the total energy generated is less than the total energy consumed, an energy deficit occurs. If the total energy generated is greater than the total energy consumed, an energy surplus occurs. Accordingly, the measurements from the energy generator data packets within the linked data group are summed, and the measurements from the energy consumption data packets within the linked data group are summed. Next, these two sums are compared to each other. This difference results in an energy deficit or energy surplus. The computing device 108 may output data indicating the presence and magnitude of the energy deficit or surplus. The energy consumption data packets correspond to the measured energy consumption values from one or more specific consumers 104. Accordingly, the calculation of the energy surplus or energy deficit is directly related to the consumption of one of the plurality of specific consumers 104. This means that, by a first process for determining the energy surplus or energy deficit, the amount of energy consumed by one or more specific consumers 104 can be effectively measured with respect to the energy generated by the energy generator 102 under monitoring. Since the measurement is performed in real time at a high frequency, this measurement value will measure the energy consumption and energy generation of the consumers in real time. This real-time measurement can be used to identify the energy generated from a specific generator 102 and attribute it to a specific consumer 104, and to calculate the carbon intensity of the energy consumed by each consumer 104, according to the following further processes. These additional processes will be described in detail later.

[0090] Continuing with the above example, the linked data group related to the time metadata of 07:04:14 on February 20, 2022, may include a first energy generator data packet indicating a measurement of 1.527 kWh generated by the first energy generator, a second energy generator data packet indicating a measurement of 0.473 kWh generated by the second energy generator, and a first consumption data packet indicating a measurement of 0.5 kWh consumed. Performing the above comparison in this example includes comparing the total energy generated (1.527 + 0.473 = 2 kWh) with the total energy generated and the total energy consumed (2 kWh - 0.5 kWh = 1.5 kWh excess). Thus, in this example, the computing device 108 compares the generated energy with the consumed energy and determines that there is a 1.5 kWh excess in the linked data group. The computing device 108 is configured to store the result of this comparison in the memory associated with the linked data group.

[0091] If it is determined that there is an energy excess, that is, if the energy generated by the energy generator 102 is more than the energy consumed by the energy consumer 104, the computing device 108 may output or store data indicating the excess. This data can be used to provide the excess generated energy to the grid in a balancing market or to sell it to a third party as part of a special-purpose power purchase agreement. Alternatively, the excess energy is curtailed to the power transmission network.

[0092] If an energy shortage is identified, the proportion of the energy required from other sources on the power grid to make up for the shortage is determined. Usually, this proportion is equal to the deficit. Expanding on the above example, if the linked data group includes a second energy consumption data packet of 3.5 kWh with the same time metadata of 07:04:14 on February 20, 2022, the total energy consumed is 4 kWh, but the total energy generated is only 2 kWh. That is, a shortage of 2 kWh has occurred. Therefore, the computing device 108 is configured to determine that 2 kWh of energy, corresponding to 50% of the total energy consumed, is required from the power grid to make up for the shortage. The proportion of the consumed energy supplied from the power grid can be stored in the memory and associated with the linked data group.

[0093] In some embodiments, the energy tracking system 100 is used to track how "green" a consumer's energy consumption is by introducing a measurement of the carbon footprint of the consumer's energy consumption into the energy tracking method and system. In particular, this method and system can introduce a measure for tracking the "carbon intensity" of energy consumption, measured as CO2 in units of g CO2e / kWh. To introduce this measure, the carbon intensity of the energy generated by the energy generator 102 is determined or obtained, and a process is performed to attribute the energy consumed for each individual consumer 104 to one or more specific energy generators 102. From the knowledge of these two variables (the carbon intensity on the generator 102 side and the attributes of the location where the consumed energy is generated), the carbon intensity (also called the "carbon intensity score") of each energy consumer 104 can be determined. This process will be described in more detail.

[0094] The energy generator 102 to be monitored may include renewable (green) energy generators and non - green energy generators. The renewable energy generators 102 monitored by the energy generator sensor 102a may include generators 102 such as solar power plants, wind power plants, tidal power plants, and hydroelectric power plants. The energy generated from these generators 102 can be considered to have no carbon intensity (carbon intensity is zero, 0g CO2e / kWh), or to have a predetermined carbon intensity indicating the relative carbon cost for manufacturing, maintenance, etc. for each of these generators 102 per unit of the generated energy. For example, in the case of a wind power plant, a carbon intensity score of 1g CO2e / kWh can be assigned corresponding to the carbon cost of installation and maintenance.

[0095] The non - renewable energy generators 102 to be monitored may be associated with a higher carbon intensity score (e.g., 100g CO2e / kWh).

[0096] The carbon intensity score of each energy generator 102 is provided to the computing device 108 by the energy generator 102, stored in the memory, and each known energy generator 102 being monitored may be associated with the corresponding carbon intensity score. Alternatively, each generator carbon intensity score can also be measured using various scientific methods in the energy generator 102 or estimated based on the type of the energy generator 102.

[0097] Energy generated from outside the energy generator under monitoring is assumed to be supplied from the power grid. The carbon intensity of the energy from the power grid, which is substantially outside system 100, is calculated using the grid carbon intensity of the power grid estimated or imported from data reports provided by external data providers. For example, the energy mix report of the power grid is periodically imported into computing device 108, and from the report, the grid carbon intensity of the power grid at the time related to the linked data group is identified or calculated. The grid carbon intensity of the power grid may be expressed in g CO2e / kWh per 1 kWh of energy supplied by the power grid, or may be expressed, for example, in g / kW (grams of carbon per unit of power per second).

[0098] Once the carbon intensity score of each energy generator 102 under monitoring is determined and stored in computing device 108, and the carbon intensity of the power grid is determined and stored, the carbon intensity of the energy consumption of energy consumer 104 can be established by computing device 108. For each linked data group corresponding to the matched energy generator and / or energy consumption data packet, each energy generator carbon intensity score represented in the linked data group is averaged according to the proportion of the energy provided by each energy generator 102 in the linked data group. This also includes the contribution from the power grid. In particular, when there is an energy shortage, that is, when the energy consumption is more than the energy generated in the linked data group, the power grid is determined to supplement the shortage.

[0099] Continuing with the above example, the linked data group includes a first energy generator measurement of 1.527 kWh generated by the first energy generator, a second energy generator measurement of 0.473 kWh generated by the second energy generator, a first energy consumption measurement of 0.5 kWh consumed by the first consumer, and a second energy consumption measurement of 3.5 kWh consumed by the second consumer. The total energy consumed is equal to 4 kWh, while the total energy generated is only 2 kWh. That is, there is a shortage of 2 kWh. Assuming that the first and second energy generators 102 are renewable energy generators and the carbon intensity of the energy generated by these energy generators is 0 g CO2e / kWh and the grid carbon intensity is 100 g CO2e / kWh, the carbon intensity of energy consumption can be calculated for the linked data group. In particular, the carbon intensity of the consumed energy is equal to the weighted average of the carbon intensities of the generated energies, weighted according to the energy generated by each power generation energy source. Therefore, the carbon intensity of the consumed energy is (100 g CO2e / kWh × 2 kWh + 0 g CO2e / kWh × 1.527 kWh + 0 g CO2e / kWh × 0.473 kWh) / 4 kWh = 50 g CO2e / kWh. The carbon intensity of the consumed energy can be referred to as the consumer carbon intensity.

[0100] The consumer carbon intensity is stored in the computing device 108 with respect to the linked data group. Since the consumer carbon intensity is calculated based on the real-time energy generator measurements and real-time energy consumption measurements within the linked data group, the consumer carbon intensity calculated for the linked data group also represents a real-time measurement of the carbon intensity at the specific time corresponding to the linked data group.

[0101] Consumer carbon intensity can be further processed to determine further parameters of the energy consumption of each energy consumer. In particular, consumer carbon intensity can be used to calculate the carbon content of a linked data group. The carbon content effectively indicates the mass of carbon attributable to the energy consumed by each consumer. The carbon content is measured in g or kg. To determine the carbon content of each consumer represented by a linked data group, multiply the consumer carbon intensity by the energy consumption of each consumer.

[0102] Following on from the above example, if the consumer carbon intensity of the consumers represented by the linked data group is 50 g CO2e / kWh and the linked data group includes a first energy consumption measurement of 0.5 kWh consumed by a first consumer and a second energy consumption measurement of 3.5 kWh consumed by a second consumer, the carbon content of each consumer is calculated as follows. For the first consumer, 50 g CO2e / kWh × 0.5 kWh = 25 g, and for the second consumer, 50 g CO2e / kWh × 3.5 kWh = 175 g.

[0103] Another way to determine the carbon content per consumer 104 is to divide the total real-time carbon content according to the relative proportion of the energy consumed. In this example, assuming that the carbon intensity of the energy generation of the energy generator 102 represented by the linked data group is 0 g CO2e / kWh, the computing device 108 may be configured to multiply the energy shortage by the grid carbon intensity of the power grid to calculate the total real-time carbon content of the energy consumed by the consumer 104. In this example, the real-time carbon content is 2 kWh multiplied by 100 g CO2e / kWh, corresponding to 200 g of carbon. The first energy consumption data packet of 0.5 kWh corresponds to 0.5 / 4 or 12.5% of the total energy consumed, and the second energy consumption data packet of 3.5 kWh corresponds to 3.5 / 4 or 87.5% of the total energy consumed. Therefore, in this example, the computing device 108 is configured to divide the 200 g of real-time carbon content such that 12.5% of the total real-time carbon content is attributed as the first real-time carbon content portion to the energy consumer 104 associated with the first energy consumption data packet, and 87.5% of the total real-time carbon content is attributed as the second real-time carbon content portion to the energy consumer 104 associated with the second energy consumption data packet. According to this process, if the total real-time carbon content is 200 g of carbon, the first real-time carbon content portion is equal to 25 g of carbon, and the second real-time carbon content portion is equal to 175 g of carbon. This total real-time carbon content and / or real-time carbon content per consumer 104 is stored and associated with a linked data group. Carbon content refers to the mass of carbon dioxide resulting from the energy consumed by energy consumer 104. Since the carbon content is calculated for a linked data group that includes real-time energy generator and / or real-time energy consumption measurements, the carbon content is also a real-time measurement of the carbon corresponding to the energy consumed by a particular consumer at the time represented by the linked data group.

[0104] If a conventional or renewable energy generator has a non-zero carbon intensity associated with each energy generation, as described above, computing device 108 is configured to calculate the real-time carbon content by including the contribution from the energy generated by the energy generator. For example, the first and second energy generators may be associated with a predetermined carbon content of 2 g CO2e / kWh. In this case, the real-time carbon content is 200 g of carbon from the power grid and 4 g of carbon from the first and second energy generators 102 (2 kWh × 2 g CO2e / kWh), meaning that a total of 204 g of carbon is used for the 4 kWh of energy consumed.

[0105] It is optional to include such contributions from the energy generator 102. Instead, it should be understood that the carbon contribution can also be approximated to 0 g so that the carbon contribution only occurs from the energy used from the power grid. Also, the carbon intensity of the energy generator 102 is set differently depending on the type of the energy generator. For example, it should be understood that the carbon intensity of solar energy generation can be different from that of wind energy generation. Furthermore, non-green energy generators 102 such as coal, nuclear, and gas energy generators can also be monitored using the energy generator sensor 102a as described above. In this case, the measured values and data from these non-green energy generators can also be considered for the real-time carbon content.

[0106] Further processing can also be performed on the linked data group to determine the parameters of individual consumers. As described above, the consumer carbon intensity is calculated for the entire linked data group. The carbon content of each consumer is derived from the carbon intensity of the linked data group and the energy consumption per consumer. In some cases, the linked data group includes energy consumption measurement values from multiple consumers. In other words, the data from multiple energy consumption sensors 104a are matched in the matching process to form a linked data group. It is possible to attribute the energy consumption, and thus the carbon consumption, to the consumers 104 related to the consumed energy. In particular, when the linked data group includes energy consumption measurement data from multiple consumers 104, the real-time carbon content and carbon intensity score of each consumer 104 can be calculated based on the relative energy consumption within the linked data group.

[0107] In some embodiments, the estimated or imported grid carbon intensity may depend on the consumer 104 associated with the energy consumption data packet included in the linked data group. This can occur when the consumer 104 in question has a particular "alternative supplier" associated with or related to the energy generator 102 monitored by the energy generator sensor 102a. In this case, the grid carbon intensity may be directly selected or imported from the alternative supplier by the computing device 108. The alternative supplier and / or operator may be configured to provide the carbon intensity to the computing device 108 or to estimate the carbon intensity of the alternative supplier based on data obtained by the computing device 108.

[0108] If there are multiple energy consumers 104 associated with energy consumption data packets within a linked data group, each energy consumer 104 may be associated with a different alternative supplier. Since each alternative supplier may be associated with a different carbon intensity, it may not be accurate to calculate the real-time carbon content and / or carbon intensity score of each energy consumer 104 using a single carbon intensity. In this case, before calculating the real-time carbon intensity score or carbon content of each consumer, computing device 108 may divide the determined energy shortage into shortage portions according to the relative proportion of energy consumption of each energy consumer 104 associated with the energy consumption data packets within the linked data group. Continuing with the above example, the first energy consumption data packet of 0.5 kWh corresponds to 0.5 / 4 or 12.5% of the total energy consumed, and the second energy consumption data packet of 3.5 kWh corresponds to 3.5 / 4 or 87.5% of the total energy consumed. The shortage is 2 kWh as calculated above. Assuming that the first and second energy consumption data packets correspond to the first and second energy consumers 104, the 2 kWh shortage is divided into a first shortage portion of 0.125 × 2 = 0.25 kWh corresponding to the shortage caused by the first energy consumer and a second shortage portion of 0.875 × 2 = 1.75 kWh corresponding to the shortage caused by the second energy consumer.

[0109] The first energy consumer and the second energy consumer may have different alternative suppliers. The first energy consumer may have a first alternative supplier with a carbon intensity of the supplied energy equal to 300 g CO2e / kWh, while the second energy consumer may have a second alternative supplier with a carbon intensity of the supplied energy equal to 50 g CO2e / kWh. Using this information, the computing device 108 is configured to calculate the individual real-time carbon intensity scores and / or real-time carbon content of the energy consumed by each of the first and second energy consumers. In particular, the carbon intensity scores are calculated per consumer, rather than for the data groups linked as described above. Thus, for the first consumer, the carbon intensity is calculated by averaging the carbon intensities from all energy generation sources (including the first alternative supplier), weighting by the relative differences in energy generation between these sources, and dividing by the energy consumed by the first consumer. The first alternative supplier condition in the calculation is weighted by the first shortfall portion calculated as described above. This means that it is assumed that the first consumer obtains a share of the energy shortage from the first alternative supplier. In the above example, the first consumer carbon intensity is as follows. (300 g CO2e / kWh × 0.25 kWh + 0 g CO2e / kWh × 1.527 kWh + 0 g CO2e / kWh × 0.473 kWh) / 0.5 kWh = 150 g CO2e / kWh. Similarly, the individual carbon intensity of the second consumer is equal to the weighted average of the carbon intensities of all energy generation sources (including the second alternative supplier). Thus, in the above example, the second consumer carbon intensity is (50 g CO2e / kWh × 1.75 kWh + 0 g CO2e / kWh × 1.527 kWh + 0 g CO2e / kWh × 0.473 kWh) / 3.5 kWh = 25 g CO2e / kWh.

[0110] The real-time carbon content can be calculated by multiplying the carbon intensity by the energy consumption per consumer using the calculated carbon intensity. For example, the first energy consumer consumes energy with a carbon content of 150 g CO2e / kWh × 0.5 = 75 g CO2e. The second energy consumer consumes energy with a carbon content of 25 g CO2e / kWh × 3.5 = 87.5 g CO2. In this case, although the second consumer causes a higher carbon content than the first consumer, the second consumer carbon intensity score is much lower than that of the first consumer.

[0111] Alternatively, instead of directly calculating each consumer carbon intensity score, the carbon content of the consumer can also be calculated. In this way, the carbon content of the first consumer is calculated by multiplying the first deficit by the carbon intensity of the first alternative supplier. This results in 0.25 kWh × 300 g CO2e / kWh = 75 g CO2e. The real-time carbon content of the second consumer is calculated by multiplying the second deficit by the carbon intensity of the second alternative supplier. This results in 1.75 kWh × 50 g CO2e / kWh = 87.5 g CO2e.

[0112] Therefore, in this method, it becomes possible to individually estimate the real-time carbon intensity and / or carbon content of each energy consumer related to the linked data group based on the related alternative suppliers. Note that this method assumes that energy generation is distributed proportionally to energy consumption and that energy deficits are distributed proportionally to energy consumption.

[0113] However, there are various ways to identify the cause of energy shortage. For example, when there are multiple consumers 104, energy can be provided from the energy generator 102 to be monitored to the consumers 104 according to the priority list. In this example, green power (electricity from renewable resources) can be provided according to the priority list or is assumed to be provided, and the consumers 104 with higher priorities can consume more or a larger proportion of green power than the consumers with lower priorities in the priority list. The priority list is stored in the computing device 108 and can be pre-agreed with multiple consumers. Next, the energy shortage of each consumer 104 can be calculated based on the priority list. In the priority list, a threshold amount of green energy consumed can be permitted for each consumer within the priority list. When the threshold amount is provided, green energy can be provided to the next consumer in the priority list or to the consumers with lower priorities in an equivalent manner until all the green energy is consumed. Alternatively, until the demand is met, green energy can be provided to the customer with the highest priority first and then repeatedly shifted to the next highest priority customer.

[0114] It should be understood that the carbon intensity score itself may be a real-time parameter calculated for each linked data group or a score averaged across multiple linked data groups.

[0115] The carbon intensity score is output by computing device 108, stored in a database, displayed to an operator, consumer, or other user, or transmitted via a communication network to another device. The carbon intensity score effectively provides an indicator of how green / brown an individual consumer's energy consumption is. Thus, the carbon intensity score can function as a basis for further processing to determine whether measures need to be taken to reduce the carbon intensity of the energy consumption of energy consumer 104. In particular, the computing device may be configured to compare a real-time carbon intensity score for each linked data group, or an overall carbon intensity score averaged over a period of time including a plurality of linked data groups, with one or more carbon intensity thresholds. The one or more carbon intensity thresholds are set as needed by consumer 104, an energy provider, a grid operator, etc., and may define an upper limit of carbon intensity tolerance for the energy consumption of individual consumer 104. For example, the carbon intensity threshold may be set by a consumer to 100 g CO2e / kWh, 200 g CO2e / kWh, 500 g CO2e / kWh, 1 kg CO2e / kWh, etc. Alternatively, or additionally, the carbon intensity threshold may be set by system 100. The one or more carbon intensity thresholds are stored by computing device 108. The determined carbon intensity score is compared by computing device 108 with one or more carbon intensity thresholds, either in real time or periodically. If the determined carbon intensity score matches or exceeds the carbon intensity threshold, computing device 108 is configured to execute a response action. The response action varies depending on the type of carbon intensity threshold that is matched or exceeded. If the exceeded carbon intensity threshold is set by the consumer, the response action may include transmitting a notification, message, and / or alarm from computing device 108 to consumer 104 or a device associated with consumer 104 indicating that the carbon intensity threshold has been exceeded.Notifications, messages, and / or alarms can further instruct the consumer 104 to reduce energy consumption or instruct the consumer 104 to access a database associated with a computing device 108 that stores processes and calculations associated with the linked data group. From this database, the consumer 104 can check how individual energy consumption changes over a period of time, so the consumer 104 can perform corrective analysis and make decisions to adjust energy consumption habits to lower the carbon intensity score. An example of the data in the database accessible by the consumer 104 is shown in FIG. 5 described below.

[0116] If a carbon intensity threshold that matches or exceeds the carbon intensity score is set by the system 100, the response actions can include sending a notification, message, or alarm from the computing device 108 to one or more energy generators 102. The notification, message, or alarm can instruct the generator 102 or the operator of the generator 102 to perform control actions associated with the energy generator 102, such as increasing energy generation, changing one or more parameters of the energy generator 102, and / or performing corrective actions. For example, the computing device 108 can send an instruction to a hydroelectric energy generator 102 to start or increase the supply of hydroelectric power to lower the carbon intensity score. Further response actions that can be performed are for the computing device 108 to request that energy be supplied from an energy storage facility to compensate for the current energy shortage and lower the carbon intensity score below the carbon intensity threshold. For example, the computing device 108 can request that the battery be drained to supply energy to the grid to lower the carbon intensity score.

[0117] The linked data group is further processed by computing device 108, and an energy mix corresponding to the energy consumption of a particular consumer associated with the linked data group can be determined. In particular, computing device 108 can be configured to calculate relative and / or absolute energy consumption by type of generator using both measurements of energy consumption from one or more energy consumption data packets within the linked data group and measurements of energy generation from one or more energy generator data packets within the linked data group. This includes determining, from the metadata within the energy generator data packet, the type of energy generator responsible for generating the energy indicated by the energy generation measurement of the energy generator data packet, accumulating and summing the energy generation measurements corresponding to energy generators of the same determined type, determining the proportion of the total generated energy corresponding to each determined type of energy generator, and applying the determined proportion of the generated energy to the energy consumed by each consumer to obtain the proportion of the energy consumed by the consumer attributable to each determined type of energy generator. Continuing with the previous example, the linked data group consists of a first energy generator data packet indicating a measured value of 1.527 kWh generated by a first energy generator, a second energy generator data packet indicating a measured value of 0.473 kWh generated by a second energy generator, and a first consumption data packet indicating a measured value of 0.5 kWh consumed, and there are no other consumers in the linked data group. The first energy generator data packet may include metadata indicating that the first energy generator is a "solar" type energy generator. Alternatively, the metadata of the first energy generator data packet may include a name or ID associated with the first energy generator, such as an owner.Computing device 108 may be configured to identify the type of the first energy generator as "solar" from a look-up table stored in the memory or the like using this metadata. The look-up table includes linked entries indicating the type of energy generator for each known identifier and / or name. The data packet of the second energy generator may include metadata indicating that the second energy generator is of the "hydroelectric power generation" type. The computing device is configured to determine the percentage of energy for each type of energy generator. In this example, 1.527 kWh / 2 kWh = 76.35%, which is from the solar type of energy generator. Similarly, 0.473 kWh / 2 kWh = 23.65%, which is from the hydroelectric power generation type of energy generator. Once these percentages are determined, they are applied to the energy consumed by each consumer. Therefore, it is assumed that 76.35% of the consumed energy is from solar and 23.65% is from hydroelectric power generation. These represent the relative energy consumption values for each type of generator. For absolute values, when 0.5 kWh of energy is consumed, the relative values are applied. Therefore, 76.35% of 0.5 kWh = 0.38175 is from solar, and 0.5 kWh × 23.65% = 0.11825 kWh is from hydroelectric power generation. The relative and / or absolute values may be stored in the memory and output by the computing device 108. The calculated absolute and / or relative mix may be extended to all monitored energy generators 102 including conventional energy generators and the mix supplied from the power grid.

[0118] Alternatively, the generated energy may be accumulated and totaled according to each individual energy generator 102 instead of according to the type of generator, and determining the contribution of each individual energy generator to the energy consumed when determining the energy mix as described above is included.

[0119] Any of the above processes may be implemented by the system to prioritize which one or more of the plurality of energy consumers 104 to supply with green power. For example, the carbon intensity score may be used as a means to audit the carbon footprint of the energy consumer 104. If the energy consumer 104 wishes to reduce its carbon dioxide emissions, the system can be configured to relocate or elevate the consumer 104 within the priority list as described above, i.e., the consumer 104 is given a higher priority with respect to receiving green power.

[0120] One or more of the above processing tasks may be executed by the computing device 108. The computing device 108 is configured to store the results of the above processing and may output them to the consumer 104 via a display or via a communication network. Alternatively, the computing device 108 may permit access to the stored data via a secure encrypted communication link using various techniques.

[0121] The above description of the process is illustrated with respect to a single group of linked data, but it should be understood that the same process may be performed on multiple groups of linked data at different times. As described above, each linked data group includes data packets that are linked (matched) based on time metadata within the data packet. The processing of each linked data group is accumulated over a period of time. For example, assume there are five individual linked data groups over a period of 5 seconds, which correspond to five sets of energy generation measurements and five sets of energy consumption measurements recorded in real time at a frequency of 1 Hz by sensors 102a and 104a. The five linked data groups are processed individually and may then be accumulated or combined. Exemplary data showing this process is presented in Table 1 below.

[0122]

Table 1

[0123] As shown in Table 1, when the excess / deficit is accumulated over five linked data groups, the total deficit is 3 kWh. Similarly, the real-time carbon content is accumulated, showing that 700 g of carbon is attributable to the energy consumption over 5 seconds by drawing energy from the power grid to compensate for the individual deficits of each linked data group. The carbon intensity score is averaged across the entire linked data group, and the carbon intensity score over 5 seconds is 20.48 g CO2e / kWh.

[0124] From Table 1, the advantage of recording the measured values and measurement times by sensors 102a and 104a in real time and at high frequency is clear. In particular, looking at the excess rows in Table 1, it was found that groups 1, 4, and 5 each show an energy deficit (indicated by a negative excess value). Therefore, each of these groups is related to the real-time carbon content (in this example, it is assumed that carbon is generated only from the power grid). The deficits of these groups total 7 kWh, and the total carbon content corresponds to 700 g CO2e. However, looking at the "Total" column, the deficit over 5 seconds is only 3 kWh. If the "Total" column were the only available data (i.e., if the real-time measurement frequency were 0.2 Hz), the carbon content would be shown as 300 g CO2e, but in reality it is 700 g CO2e. Therefore, using higher-frequency real-time measurements is more accurate.

[0125] Similarly, the carbon intensity is averaged for each linked data group over a 5 - second period, and the overall carbon intensity score is 23.33 g CO2e / kWh. However, when only the "Total" column is used, the carbon intensity appears to be 3 kWh × 100 g CO2e / kWh / 30 kWh = 10 g CO2e / kWh. This shows the problem associated with measuring data over a longer sample time. In reality, accuracy and precision are lost. By performing the measurement in real - time and matching the data sets according to the time metadata, the process is carried out accurately and precisely, and the actual carbon content and carbon intensity over a certain period (5 seconds in Table 1) are determined.

[0126] Figure 2 shows this concept, demonstrating how much more useful, accurate, and precise real - time energy measurements become when they are generated and stored at a higher frequency. In particular, Figure 2 shows a series of schematic graphs including a first graph 202, a second graph 204, and a third graph 206, each graph showing the relationship between the measured power generation (MW) and time (seconds). The scale of the time axis ranges from 0 seconds to 1 second. In the first graph 202, the measurement frequency is 1 Hz, meaning only one measurement is recorded, and the generated energy corresponds to 31.9 kWh. The second graph 204 shows the measurements taken at a frequency of 5 Hz. That is, five measurements were made. As can be seen from the second graph 204, the total generated energy is the same as that in the first graph 202 (31.9 kWh), but the distribution of energy generation over time is not constant as shown in the first graph 202 and actually peaks at 139 MW between 0.6 seconds and 0.8 seconds. This effect is amplified in the third graph 206, which includes measurements taken at a frequency of 10 Hz. The total energy generated in the third graph 206 is the same as that in the first graph 202 and the second graph 204, but the distribution of the generated energy is not constant and has more fluctuations than shown in the second graph 204. In particular, a minimum power of 87 MW is generated between 0.3 seconds and 0.4 seconds, and a maximum power of 144 MW is generated between 0.7 seconds and 0.8 seconds.

[0127] This variability in energy generation underscores the importance and utility of recording real-time high-frequency measurements related to energy generation and consumption. According to various embodiments, both energy generation measurements and energy consumption measurements are recorded in real-time at high frequencies and then matched to each other accordingly, creating multiple linked data groups. Each linked data group corresponds to a specific measurement time or timestamp within a continuous measurement time stream. By performing the matching at this level of granularity, the accuracy and precision of the high-frequency measurements are maintained before aggregating energy generation and consumption statistics over longer periods (such as 10 minutes, 30 minutes, 1 hour, 1 day, etc.). Further processing (e.g., calculation of energy surplus or deficit, carbon intensity per consumer, carbon content per consumer, or energy consumption mix per consumer) performed on the matched data within the linked data groups provides measurement tools that can optimize energy consumption at the individual consumer level. In particular, the system according to the embodiments described herein provides very accurate and precise data regarding the carbon emissions of each consumer and each energy generator. From this data, an energy generator, system, or energy consumer can perform actions to monitor and manage the carbon intensity of the energy consumption of a particular energy consumer.

[0128] In the above description regarding the processing of linked data groups, it has been shown that the processing is performed for each linked data group. However, it is understood that such processing does not necessarily need to be executed individually and may be executed once for each set of linked data groups at long intervals. In Table 1, the period is 5 seconds, but the extended period can be of any length, such as 30 seconds, 1 minute, 1 hour, 1 day, 1 week, 1 month, 1 year, etc. Therefore, the calculation of the values of excess, deficiency, carbon content, and carbon intensity can be performed periodically. In each of these instances, since the data is still measured in real time and used to form linked data groups, the above advantages are maintained over a long period. Since the data is stored in linked groups, data integrity is maintained and multiple linked data groups can be processed at once. For example, based on the deficiency of each group, the real-time carbon content of a set of linked data groups can be calculated periodically. Similarly, the carbon intensity score can be calculated periodically and / or after a long period based on the accumulation of real-time carbon content. Therefore, the processing can be performed iteratively for each linked data group or periodically for each set of linked data groups. Each linked data group holds the matching of data at a specific point in time indicated by time metadata, so the final result does not change regardless of which type of processing is executed.

[0129] Figure 3 shows a schematic diagram of an example of an energy tracking system 100. The energy tracking system 100 operates within the power grid 110, is connected to the energy generator 102 on the energy generator side, and is connected to the energy consumer 104 on the energy consumer side. The energy tracking system 100 includes an energy generator sensor 102a, an energy consumption sensor 104a, and a computing device 108. Figure 3 also shows examples of an energy generator data packet 112 and an energy consumption data packet 114 that are at least partially formed by the energy generator sensor 102a and the energy consumption sensor 104a, respectively.

[0130] As illustrated in FIG. 3, the energy generator data packet 112 includes real-time energy generator measurement data from the energy generator sensor 102a indicating the amount of energy generated by the energy generator 102. The energy generator data packet 112 also includes associated metadata including a measurement time indicating the time at which the measurement or reading was performed by the energy generator sensor 102a. The measurement time may be regarded as time metadata. The measurement time is measured according to the global clock signal received from the global clock 118. The global clock may be provided by any suitable system including a clock such as a global positioning system (GPS). The energy generator data packet 112 also includes additional metadata such as a display of the energy generator (Puerto de Oro) where the measurement was made, activities related to the energy generator (cleaning) at the time the measurement was made, and other auxiliary sensor data. Further auxiliary sensor data is provided by the auxiliary sensors 116a-116c. The first auxiliary sensor 116a is an irradiance sensor, the second auxiliary sensor 116b is a temperature sensor, and the third auxiliary sensor 116c is a service log / schedule related to the energy generator 102. The auxiliary sensor data provides information about the energy generator 102.

[0131] The energy consumption data packet 114 also includes metadata including a measurement time indicating the time at which the measurement or reading was performed by the energy consumption sensor 104a. The measurement time may be regarded as time metadata. The measurement time is measured according to the global clock signal received from the global clock 118. The energy consumption data packet 114 further includes additional metadata including a display of the location of the energy consumer 104 (Bogota), activities related to the consumer 104 where the measurement was made (kettle), and an identification of the consumer or meter related to the energy consumer 104.

[0132] The energy generator data packet 112 and the energy consumption data packet 114 are transmitted to the computing device 108. In FIG. 3, the computing device 108 is illustrated as a central server.

[0133] Although only one energy generator 102 is shown in FIG. 3, it should be understood that there may be multiple different energy generators 102 monitored by a plurality of energy generator sensors 102a. These sensors can be of various types configured to measure various aspects or components of the energy generator 102.

[0134] In FIG. 3, the energy generator 102 is a solar energy generator with a solar string. The solar energy generator is a photovoltaic power plant that includes several components used to form a system necessary to convert solar energy into electrical energy suitable for power transmission through the power grid. Such a system includes support equipment to balance the system and enable continuous operation. The components of the system include inverters, controllers, transformers, wiring, connector boxes, switches, monitoring devices, charge regulators, energy storage devices, etc. This support infrastructure is called the "balance of system" (BOS). In a photovoltaic power plant, individual photovoltaic modules (PVMs) are usually connected to one string. Multiple strings are connected to a junction box, which is connected to an inverter and a transformer to supply energy to the power grid. Additional strings can be added to the system to effectively modularize the system.

[0135] System 100 may include a plurality of energy generator sensors 102a for each energy generator 102. In the case of the solar energy generator 102 of FIG. 3, system 100 may include energy generator sensors 102a in one or more of these components. For example, each of the plurality of sensors may be associated with a PVM, string, combiner box, inverter, and / or transformer, etc. Using a plurality of sensors 102a for each energy generator 102 enables sensor redundancy, allowing the generated energy to be measured even if one or more, rather than all, of the sensors fail. Further, using a plurality of sensors allows for more reliable energy measurements as the measured values from each sensor can be combined, averaged, or compared to identify incorrect readings. The plurality of sensors may include, for example, temperature, power factor, impedance, current, voltage sensors, etc. Finally, using a plurality of sensors on different components within the energy generator can be beneficial in fault identification procedures as irregular measurements from a particular sensor associated with a particular component may indicate that there is a fault in that particular component. For example, using an imaging sensor to provide a thermography image and detect and classify the heating pattern across the PV panel can help indicate the presence of a fault in the PV plant.

[0136] Furthermore, the performance of the energy generator 102 and / or the energy generator sensor 102 can also be verified using the auxiliary sensors 116a - 116c and the auxiliary sensor data included in the energy generator data packet 112. For example, in FIG. 3, using the auxiliary sensor data indicating the irradiance and temperature in the energy generator 102 of a solar power plant, the predicted energy generation level of the energy generator 102 can be calculated. Generally, the higher the temperature, the lower the output, and the higher the irradiance, the higher the energy output. If such levels are not met by the energy generator 102 or not detected by the energy generator sensor 102a, it is determined that there is a fault in one of these components. The computing device 108 can perform this analysis and output a notification or an error message if it is determined that a fault may exist.

[0137] Similarly, for reliability improvement, providing sensor redundancy, and for use in fault identification, a plurality of sensors 104a can be provided for each energy consumer 104 and / or for individual components of the energy consumer 104.

[0138] As shown in FIG. 3, the energy generation data packet 112 and the energy consumption data packet 114 indicate the same time (07:04:14) for energy generation and energy consumption. Therefore, these data packets are matched by the computing device 108. Since the energy generated and consumed are measured in real time and at high frequency by the energy generation sensor 102a and the energy consumption sensor 104a, various energy measurements are continuously made and recorded with respect to the time measurement by the sensors. In order for the computing device 108 to match the correct energy generation measurement value with the corresponding energy consumption measurement value at the same time, the global clock 118 is maintained separately from the energy generation sensor 102a and the energy consumption sensor 104a. The global clock signal from the global clock 118 supplies the same timing reference common to the energy generator signal 102a and the energy consumption sensor 104a, and enables the real-time measurements performed simultaneously to be recorded in the time metadata. The global clock 118 may provide a global clock signal to each of the auxiliary sensors 116a to 116c for the same purpose. As a result, the energy generator data packet 112 includes metadata collected at exactly the same time as the collected energy generation measurement data. Alternatively, the respective clock signals of the energy generator sensor 102a and the energy consumption sensor 104a may be synchronized using, for example, the Network Time Protocol (NTP).

[0139] As described above, data packets linked or matched to each other based on time metadata (e.g., timestamp) are stored in the relational database of the computing device 108, or references to the links are stored in the relational database of the computing device 108.

[0140] As shown in FIG. 3, the energy generator data packet 112 and the energy consumption data packet 114 are in a pre - set format. The pre - set format is used to ensure that the computing device 108 can interact with all data packets and that each sensor 102a and 104a can provide data that the computing device 108 can understand. Further, if the same pre - set format is used for data transmission, analysis, and processing throughout the system 100, it becomes possible to easily and effectively export or access data to users such as energy generator operators and consumers 104.

[0141] The energy generator sensor 102a and the energy consumption sensor 104a may not provide the measured values of energy generation and energy consumption in a pre - set format. In this case, the measured values of energy generation and energy consumption are converted into a pre - set format by the computing device 108. In particular, the pre - set format is related to a predetermined vocabulary of terms and / or headers that describe the functions of the system, such as energy generators, energy consumers, their names, identification information, locations, and other metadata names and headings. The predetermined vocabulary is shared with the energy generator 102 and / or the energy consumer 104, enabling these devices to access and understand the data defined by the data packets in the pre - set format. The predetermined vocabulary is determined based on the specific applications and settings in which the system is used, and thus it is necessary to understand that it depends on the types of energy generators within the system and the components / sensors used for data provision.

[0142] Computing device 108 is configured to receive pre-formed data packets in a pre-set format and, based on the received measurements and metadata (including time metadata), create energy generator data packets 112 and energy consumption data packets 114. If the received data is not in the pre-set format, computing device 108 is configured to obtain measurements of energy generation and energy consumption from sensors 102a and 104a and store them in a time series database with respect to time metadata (such as timestamps) obtained from a synchronization clock. Additional metadata describing the source of the measurement data, such as the function of the sensor, the clock synchronization method, the location of the sensor, and the devices connected to the sensor, is added to the measurement data and time data, and data packets are formed. These data packets in the time series database are then matched as described above to form linked data groups that are stored in another relational database of computing device 108.

[0143] As shown in FIG. 3, the pre-set format may be based on the provenance data model (PROV-DM). Provenance is information about the entities, activities, and people involved in the creation of data or things, and can be used for evaluating their quality, reliability, and trustworthiness. PROV-DM is a conceptual data model that forms the basis of the W3C's Provenance (PROV) specification family. PROV-DM is composed of the following six components respectively. (1) Entities and activities, and the times at which they were created, used, or ended, (2) Derivation of entities from entities, (3) Agents responsible for the generated entities and the generated activities, (4) The concept of bundles, that is, the mechanism to support the provenance of provenance, (5) Properties that link entities referring to the same thing, (6) Collections that form the logical structure of members. Essentially, provenance explains the use and generation of entities by activities that can be affected in various ways by agents. Generally speaking, an entity is something physical, digital, conceptual, or of other types with some fixed form. In system 100, the energy within system 100 is an entity. Energy is indicated by the measured values of energy generation and consumption. These can be represented by further entities such as a dataset including current, voltage, power, power factor, reactive power, and active power, etc., and these are processed by activities that generate the generated energy or consumed energy entities. An activity is something that occurs to an entity and acts with the entity, and may include consumption, processing, conversion, change, rearrangement, use, or generation of the entity. In system 100, the activity can be energy generation or consumption, and processing such as accumulation / summation of the generated energy, calculation of carbon content, calculation of carbon intensity, etc. An agent is something that bears some responsibility for the ongoing activity, the existence of the entity, or the activity of another agent. An agent can be a specific type of entity or activity. In system 100, the energy consumer 104 and the energy generator 102 are agents. There may also be different agents.Such agents may further include any party involved in the use, management, monitoring, and / or supply of assets that form part of the energy system (e.g., electricity consumers, providers of engineering services, sensor system operators, etc.).

[0144] In system 100, PROV-DM is used to form energy generator data packets 112 and energy consumption data packets 114. In particular, the data packets include the identification of activities related to the generation and consumption of energy, the time when energy is generated or consumed, and properties such as metadata and time metadata that link entities. Computing device 108 is configured to generate PROV data packets including sensor data, asset data, additional metadata, etc. The PROV data packets are stored in PROV-STORE. PROV-STORE may hold pointers to specific data points held in another table (such as within a time-series database) rather than the exact data points. Computing device 108 is configured to match energy generator data packets 112 and energy consumption packets 114 based at least on time metadata to create linked data groups. The linked data groups and the processes performed on that data as described above are used by computing device 108 to generate a relational database of the organized data. The database may be implemented as a PROV trace, which is a type of directed acyclic graph showing the links between entities, activities, and agents.

[0145] The vocabulary used to describe the system can be called the PROV vocabulary. The PROV vocabulary describes consumers, operators of generators, sensors, energy matching or the difference in consumed / produced energy (deficit / excess), carbon intensity calculators, carbon content, etc. The vocabulary is not fixed. It is extended to cover all entities, activities, and agents encountered in various energy production / consumption scenarios. The PROV vocabulary can be used by third parties to extend datasets in PROV. For example, the operator of an electric vehicle charging point can use this vocabulary to describe the agents being monitored (sensors, chargers, vehicles, users), activities (charging, discharging), and entities (energy, connection status). The generated PROV data is stored in the PROV-STORE by the computing device 108 and is further connected to the entities / agents / activities already held in the PROV-STORE by the relevant entities / agents / activities. Thus, it becomes possible to provide access to third parties to extend the PROV-STORE with third-party data in a pre-set format.

[0146] FIG. 4 shows a schematic diagram of an example of a PROV trace 300 that can be generated by computing device 108. As described above, a PROV trace is a directed acyclic graph that includes linked information. The PROV trace 300 includes agents 302, entities 304, and activities 306. Agents 302 include energy generators 302a, energy consumers 302b, energy generator sensors 302c, and energy consumption sensors 302d. Entities include, for example, data related to measurements such as measurements from sensors, and calculations and processes executed by computing device 108. Thus, entities can include the total energy generated from the accumulation of energy generation measurements, the total energy consumed from the accumulation of energy consumption measurements, energy surplus or deficit, real-time total carbon content, carbon intensity score, and real-time carbon content per individual consumer. Activities include, in addition to energy generation and consumption, the total of energy generator measurements, the total of energy consumption measurements, determination of energy surplus or deficit, determination of energy mix, real-time carbon content, carbon intensity score, real-time carbon content / carbon intensity per individual energy consumer, etc., and data processing by a computing device.

[0147] FIG. 4 shows how these agents 302, entities 304, and activities 306 effectively provide an audit trail of data stored, linked, and retrieved by computing device 108 using PROV, and the processes executed thereon, to arrive at one or more results such as real-time carbon content and carbon intensity score.

[0148] By storing the data in this way, the computing device 108 can provide a transparent history or audit trail of the data within the system 100 and how that data was manipulated, and can export this or provide it to consumers or third parties via access control.

[0149] In some embodiments, the PROV trace can be stored using a distributed ledger or a blockchain protected by a cryptographic hash function. In this way, the integrity of the PROV trace is ensured. Since multiple copies of the PROV trace are held in the distributed ledger, a third party can confirm that the PROV trace was generated and stored at the time the owner claimed it was generated and stored. The PROV trace is uploaded to the distributed ledger by the owner of the PROV data at a time (daily, hourly, minute by minute, etc.) considered appropriate and includes details of the links to the databases (sensor data, relational data, etc.) that form the basis of the PROV data. The detailed implementation (read / write) of such a distributed ledger depends, as will be understood, on the particular distributed ledger technology used. The data sets containing data recorded by sensors and stored in a time series database are not stored in the blockchain itself. These are recorded "off-chain". The PROV data may include pointers to off-chain data sets as described above. Persons who need access to the PROV-TRACE or the data sets are provided with read / write permissions to the blockchain (e.g., to add a signature to a document) or keys to view the data sets and perform their own calculations. The sharing and use of such keys follows common public / secret key sharing or other similarly secure key sharing methods.

[0150] PROV traces or stored databases, including energy consumption and power generation measurement data, and excess / deficit determination, real-time carbon content, carbon intensity scores, and energy mix, can be provided by computing device 108 to one or more energy consumers 104 and / or one or more energy generators 102 via a report or file transmitted from computing device 108. As described above, such information can be provided via a communication network such as a display or the Internet. In particular, computing device 108 is configured to communicate with energy generator 102 and energy consumer 104 over a network. The operator or consumer of the energy generator can access or request access to the database stored by computing device 108 as needed. In response to such a request, computing device 108 can provide data from the stored database corresponding to a particular energy generator or consumer via the network. The data can take the form of a report such as a PROV certificate. The data can take the form of a PROV trace. For example, the data can be made available via a virtual interface on a web page.

[0151] Although the above described a pre-set format using PROV, it should be understood that other data models or protocols can be used to provide a pre-set format or to create means for tracking and auditing a shared vocabulary, energy generation and consumption, and operations (e.g., calculations) using such data.

[0152] FIG. 5 shows an example of a virtual interface 400 provided to consumer 104 for the purpose of providing information from the stored database of computing device 108. Interface 400 includes decision data 402 calculated from the processing of one or more linked data groups as described above, visual data 404, 406, and 408 showing the energy mix provided by one or more energy generators 102, and a visual representation 410 of additional metadata obtained from energy generator data packets and / or energy consumption data packets. The decision data 402 in FIG. 5 includes a percentage indicating the proportion of the consumed energy generated from carbon-based sources relative to the total consumed energy. In FIG. 5, 3.34% of the energy is supplied from non-green energy generators. FIG. 5 shows one way in which data acquired, processed, and stored in a database by computing device 108 is provided to consumer 104. In FIG. 5, a visual representation 410 is provided using location metadata from multiple energy generators and location metadata from energy consumers. FIG. 5 also shows the corresponding period 412 for the data provided to consumer 104. Thus, the embodiments described herein can be used to provide real-time carbon reporting to energy consumers.

[0153] FIG. 6 shows a flow diagram 500 of a method executed by system 100. In a first step 501, the method includes measuring in real time the energy generated by the energy generator and the energy consumed by the energy consumer.

[0154] In a second step 502, the method includes recording the time of each energy generation and energy consumption measurement as time metadata. This second step is executed together with the first step 501, and each measurement value is recorded together with the corresponding measurement time value.

[0155] In the third step 503, the method includes providing the computing device with energy generator data packets and energy consumption data packets. As described above, the energy generator packets include the measured values of the energy generator, the associated measurement times, and other generator metadata. The energy consumption packets include the measured values of the energy consumption, the associated measurement times, and other consumer metadata. It should be understood that the energy generation packets and the energy consumption packets may be generated by the computing device. In this case, the computing device receives sensor data including time metadata along with the energy generation measured values and / or the energy consumption measured values. Next, the computing device is configured to generate data packets from this sensor data.

[0156] In the fourth step 504, the method includes matching the energy generator data packets with the energy consumption data packets based on the time metadata to generate linked data groups. The linked data groups may be referred to as sets of matching measured values. Each linked group shares the same time metadata.

[0157] In the fifth step 505, processing is performed on the linked data groups. The processing may include determining, for each linked data group, the energy mix, the real-time carbon content, the carbon intensity, and / or the energy surplus or deficit. If the energy generator under monitoring is a renewable energy generator, the carbon content and the carbon intensity are calculated based on the deficit compensated by the energy from the grid. These parameters are calculated as described above with reference to FIG. 1.

[0158] In step 506, saving and / or outputting the result of the process is included in the method. The result can then be communicated to the operator or consumer of the energy generator and can be used to provide report information or to trigger further processing or response actions.

[0159] It should be understood that method 500 may include additional features and steps described above and with respect to FIGS. 1 to 5.

[0160] The methods described herein can be implemented in the form of a computer program comprising computer program code means adapted to perform all the steps of any of the methods described herein when the program is run on a computer, in a machine-readable form on a tangible storage medium by software, the computer program being embodied on a computer-readable medium. Examples of tangible (or non-transitory) storage media include disks, thumb drives, memory cards, etc., and do not include propagated signals. The software is suitable for execution on parallel or serial processors and the steps of the method can be performed in any suitable order or simultaneously.

[0161] This application recognizes that firmware and software are valuable, separately tradable commodities. This is aimed at covering software that runs or controls on "dumb" or standard hardware to perform the necessary functions. It also includes software that "describes" or defines the configuration of hardware, such as HDL (Hardware Description Language) software issued to perform the necessary functions in the design of silicon chips or the configuration of field programmable chips.

[0162] As will be apparent to those skilled in the art, the above features and embodiments can be combined as appropriate, and can be combined with any aspect of the present invention, except when such a combination is explicitly specified as impossible or when those skilled in the art understand such a combination to be self-evidently impossible.

[0163] In the above embodiment, a system 100 including a computing device 108 has been described. The computing device 108 can be a server. The server can include a single server or a network of servers. In some examples, the server functionality is provided by a network of servers distributed across an entire geographical area, such as a worldwide distributed server network, and a user (consumer or operator) can be connected to an appropriate one of the network servers, for example, based on the user's location.

[0164] In the above description, for clarity, embodiments of the present invention have been described with reference to a single user. In practice, it is understood that the system can be shared by multiple users and, in some cases, can be shared simultaneously by a very large number of users.

[0165] The embodiments described above are fully automated. In some examples, a user or operator of the system can manually direct some of the steps of the method being performed.

[0166] In the described embodiments, computing device 108 and other similar devices may be implemented as any form of computing device and / or electronic device. Such devices may include one or more processors that are microprocessors, controllers, or other suitable types of processors that process computer-executable instructions to control the operation of the device in order to collect and record routing information. In some examples, such as when a system-on-chip architecture is used, the processor may include one or more fixed function blocks (also referred to as accelerators) that implement part of the method in hardware (rather than software or firmware). Platform software including an operating system or other suitable platform software may be provided to the computing-based device to enable application software to be executed on the device.

[0167] The various functions described herein can be implemented in hardware, software, or a combination thereof. When implemented in software, the functions can be stored or transmitted as one or more instructions or code on a computer-readable medium. Computer-readable media includes, for example, computer-readable storage media. Computer-readable storage media can include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, and other data. Computer-readable storage media is any available storage media that can be accessed by a computer. By way of non-limiting example, such computer-readable storage media can include RAM, ROM, EEPROM, flash memory or other memory devices, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other media that can be used to store the desired program code in the form of instructions or data structures and that can be accessed by a computer. As used herein, disks and discs include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-ray (RTM) disc (BD). Additionally, propagated signals are not included within the scope of computer-readable storage media. Computer-readable media also includes communication media that facilitates transferring a computer program from one place to another. For example, a connection can be a communication media. For example, communication media includes coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwave when software is transmitted from a website, server, or other remote source. Combinations of the above should also be included within the scope of computer-readable media.

[0168] Alternatively, or in addition, the functions described herein can be performed, at least in part, by one or more hardware logic components. For example, hardware logic components that can be used include, but are not limited to, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), system on chip (SOC), complex programmable logic devices (CPLD), and the like.

[0169] Although shown as a single system, it should be understood that a computing device can be a distributed system. Thus, for example, multiple devices can communicate via a network connection and jointly perform tasks described as being performed by a computing device.

[0170] Although shown as a local device, a computing device can be located remotely and accessed via a network or other communication link (e.g., using a communication interface).

[0171] As used herein, the terms "computer" and "computing device" refer to any device having processing capabilities to execute instructions. Those skilled in the art will understand that such processing capabilities are incorporated into a variety of devices, and thus the term "computer" includes personal computers, servers, mobile phones, personal digital assistants, and many other devices.

[0172] One skilled in the art will understand that the storage devices used to store program instructions can be distributed throughout the network. For example, a remote computer may store examples of processes described as software. A local computer or terminal computer can access the remote computer and download some or all of the software for executing the program. Alternatively, the local computer can download some of the software as needed, execute software instructions at the local terminal, or execute software instructions at the remote computer (or computer network). One skilled in the art will also understand that all or part of the software instructions may be executed by dedicated circuits such as DSPs, programmable logic arrays, etc. by utilizing the prior art known to those skilled in the art.

[0173] References to "one" item refer to one or more of these items. As used herein, the term "comprising" means including the steps or elements of a specified method, but such steps or elements do not constitute an exclusive list, and the method or apparatus may include additional steps or elements.

[0174] As used herein, the terms "component" and "system" are intended to encompass computer-readable data storage composed of computer-executable instructions that, when executed by a processor, perform a specific function. The computer-executable instructions may include routines, functions, etc. It should also be understood that a component or system may be localized on a single device or distributed over multiple devices.

[0175] Furthermore, as used herein, "exemplary" means "serving as an illustration or example of something." Further, to the extent that the term "comprising" is used in either the detailed description or the claims, such term is intended to be inclusive in the same manner as the term "comprising" is interpreted when used as a transitional term in a claim.

[0176] Furthermore, the operations described herein may be implemented by one or more processors and / or may include computer-executable instructions stored on a computer-readable medium. The computer-executable instructions may include routines, subroutines, programs, execution threads, and the like. Further, the results of the operations of the method may be stored on a computer-readable medium or displayed on a display device.

[0177] The order of the steps of the methods described herein is exemplary, but the steps may be performed in any suitable order or, where appropriate, simultaneously. Further, steps may be added or substituted in any of the methods, or individual steps may be deleted, without departing from the scope of the subject matter described herein. Combinations of aspects of any of the above embodiments with aspects of any of the other above embodiments may further form embodiments without losing the desired effects.

[0178] The description of the above preferred embodiments is provided by way of example only, and those skilled in the art may make various modifications. The above description includes examples of one or more embodiments. Of course, it is not possible to describe every modification and change of the above devices and methods for the purpose of describing the above aspects, but those skilled in the art can recognize that many more changes and permutations of various aspects are possible.

Claims

1. A real-time energy tracking method implemented by a computer, The process involves measuring the energy generated by an energy generator in real time using an energy generation sensor, and obtaining multiple energy generation measurements over a certain period of time. The process involves measuring the energy consumed by energy consumers in real time using an energy consumption sensor and obtaining multiple energy consumption measurements over a certain period of time. A step of receiving a plurality of generator data packets, each of which generator data packets includes one of the plurality of generated energy measurements, and the associated generator metadata includes time data corresponding to the time when one of the plurality of generated energy measurements was measured, A step of receiving multiple consumption data packets, each of which consumption data packets includes one of the multiple energy consumption measurements, and associated consumer metadata includes time data corresponding to the time when one of the multiple energy consumption measurements was measured, The steps include matching the multiple generated energy measurements with multiple consumed energy measurements according to the time data from the generator metadata and each consumer metadata, and obtaining multiple sets of matched measurements over a certain period of time, A real-time energy tracking method comprising the step of determining and outputting a real-time energy deficit or surplus by comparing the generated energy measurement with a matching energy consumption measurement for each set of matching measurement values ​​over a certain period of time.

2. For each of several sets of matching measurements, the steps include obtaining the grid carbon intensity related to the power grid connected to the energy consumer, The steps include obtaining the energy generator carbon intensity related to the energy generator, A step of determining the consumer carbon intensity associated with the set of matching measurements, wherein the consumer carbon intensity is The grid carbon intensity, weighted according to the real-time energy deficit of the set of matching measurements, The step is a weighted average of the set of matching measurements with the energy generator carbon intensity weighted according to the corresponding generated energy measurement, The method according to claim 1, further comprising the step of outputting the consumer carbon strength.

3. For each set of matching measurements over the aforementioned period The steps include accumulating the aforementioned consumer carbon strength, The steps include: determining the average consumer carbon strength by averaging the consumer carbon strength over a certain period of time; The method according to claim 2, further comprising the step of outputting the average consumer carbon strength.

4. The step of obtaining the grid carbon intensity is, The steps include determining the energy consumer associated with the aforementioned energy consumption measurement, The steps include determining alternative energy suppliers related to the aforementioned energy consumer, The method according to claim 2 or 3, comprising the step of obtaining an alternative supplier carbon intensity related to the alternative energy supplier such that the grid carbon intensity is set as the alternative supplier carbon intensity.

5. A step of determining the real-time carbon content related to the energy consumed by the energy consumer in the set of matching measurements, based on the consumer carbon intensity of the set of matching measurements and the energy consumption measurements in the set of matching measurements. The method according to claim 2, further comprising the step of outputting the real-time carbon content.

6. The steps include comparing the consumer carbon intensity with a first threshold, If the consumer carbon intensity matches or exceeds the first threshold, The method according to claim 1, further comprising the step of taking action to reduce the consumer carbon intensity.

7. The aforementioned actions are, To activate an alarm to warn the aforementioned energy consumer, To increase the energy generated, activate an additional energy generator or transmit an instruction to increase the energy generation by the energy generator, and The method according to claim 6, comprising at least one of notifying the energy consumer to reduce energy consumption.

8. The method according to claim 1, wherein the period is one second, one minute, one hour, one week, one month, or one year.

9. The method according to claim 1, wherein the step of measuring in real time includes measuring at a frequency of 1 Hz or higher.

10. There are multiple energy generators, and as a result, the method is The steps include: measuring the energy generated by the multiple energy generators in real time using multiple energy generation sensors, and obtaining multiple generated energy measurement values ​​for each of the multiple energy generators over a certain period of time; A step of receiving a plurality of generator data packets for each of the energy generators, wherein each of the generator data packets includes one of a plurality of generated energy measurement values, and the associated generator metadata includes time data corresponding to the time when one of the plurality of consumed energy measurement values ​​was measured, A step of obtaining a plurality of sets of matching measurements by matching a plurality of generated energy measurements from each of the plurality of energy generators with a plurality of consumed energy measurements, wherein each set of matching measurements includes a generated energy measurement from each energy generator. The method according to claim 1, comprising the step of determining and outputting a real-time energy deficit or surplus by comparing the energy output measurements from each of the plurality of energy generators with matching energy consumption measurements for each set of matching measurements over a certain period of time.

11. The method according to claim 10, wherein the plurality of energy generators include a plurality of types of energy generators.

12. A step of determining, from the measured energy output of each energy generator, the real-time proportion of the total energy generated by the plurality of energy generators that is attributable to each individual and / or type of energy generator, The method according to claim 11, further comprising the step of outputting a real-time percentage of the total energy generated by the plurality of energy generators that is attributed to each type of energy generator.

13. There are multiple energy consumers, and therefore, the method The steps include measuring the energy consumed by the aforementioned plurality of energy consumers using the aforementioned plurality of energy consumption sensors, A step of receiving a plurality of consumption data packets for each of the energy consumers, wherein each consumption data packet includes one of the plurality of energy consumption measurements, and the associated consumer metadata includes time data corresponding to the time when one of the plurality of energy consumption measurements was measured, A step of obtaining a plurality of sets of matching measurements over a certain period of time by matching the plurality of generated energy measurements with a plurality of consumed energy measurements from each of the plurality of energy consumers according to the time data from the generator metadata and the respective consumer metadata, wherein each set of matching measurements includes a consumed energy measurement from each of the energy consumers, The method according to claim 1, comprising the step of determining and outputting a real-time energy deficit or surplus by comparing the generated energy measurement with a matching energy consumption measurement from the plurality of energy consumers for each set of matching measurement values ​​over a certain period of time.

14. The step of determining the consumer carbon intensity for each set of matching measurements is: The individual consumer carbon intensity of each energy consumer represented by the set of matching measurements is To obtain the carbon intensity of alternative suppliers for each energy consumer represented by the set of matching measurements, The real-time energy deficit of the set of matching measurements is divided into the multiple energy deficit portions based on the energy consumption measurements within the set of matching measurements, and each energy deficit portion is attributed to the energy consumer of each of the multiple energy consumers represented by the set of matching measurements. Weighted according to the energy deficit caused by the aforementioned energy consumer, The carbon intensity of each of the aforementioned energy consumers' individual alternative suppliers, and The individual consumer carbon intensity of each energy consumer is calculated by taking a weighted average of the energy generator carbon intensities weighted according to the corresponding generated energy measurement values ​​of the set of matching measurements, and The method according to claim 13, further comprising the step of determining by outputting the individual consumer carbon intensity of each energy consumer represented by the set of matching measurements.

15. The aforementioned generator metadata is Data indicating the type of energy generator used, Data indicating entities related to the energy generator used, and / or Includes one or more embodiments of data indicating activity or status related to the energy generator used, and / or, the consumer metadata is Data showing the types of energy consumers, Data indicating entities related to the aforementioned energy consumer, and / or The method according to claim 1, comprising one or more embodiments of data indicating activities or states related to the energy consumer.

16. Furthermore, according to one or more embodiments of the generated metadata and / or the consumer metadata, The steps include matching the plurality of generated energy measurement values ​​with the plurality of consumed energy measurement values ​​to obtain a plurality of sets of secondary matching measurement values ​​over a certain period of time, The method according to claim 15, comprising the step of outputting the secondary matching measurement value.

17. The method according to claim 1, wherein the output includes saving the data to a database in a pre-configured format.

18. The method according to claim 17, further comprising outputting one or more of the following: a set of matching measurements according to the time metadata, the consumer metadata, and the generator metadata.

19. The display of the energy generator, the display of the energy consumer, multiple sets of matching measurements of the energy generator and the energy consumer, real-time energy shortages or surpluses determined for each matching measurement over a period of time, and records of one or more calculations corresponding to one or more determination steps, wherein the display of the energy generator, the display of the energy consumer, multiple sets of matching measurements of the energy generator and the energy consumer, real-time energy shortages or surpluses determined for each matching measurement over a period of time, and records of one or more calculations corresponding to one or more determination steps. The method according to claim 17 or 18, further comprising the step of storing in a directed acyclic graph.

20. The method according to claim 19, further comprising the step of storing the hash of the directed acyclic graph on a blockchain or distributed ledger using a cryptographic hash function.

21. The method according to claim 1, further comprising the step of measuring the energy generated by the energy generator in real time using a first group of sensors, wherein the first group of sensors includes at least two types of sensors arranged in the energy generation chain together with the energy generation sensor and / or arranged in the same location as the energy generation sensor.

22. The method according to claim 21, further comprising the step of verifying the measured energy output of the energy generating sensor based on the measured values ​​of the first group of sensors.

23. The method according to claim 1, wherein the step of measuring the energy generated by the energy generator includes the step of measuring the energy generated by a renewable energy generator.

24. Before measuring, The method according to claim 1, further comprising the step of synchronizing a first clock signal used in the energy generation sensor with a second clock signal used in the energy consumption sensor.

25. A real-time energy tracking system, One or more energy generation sensors configured to measure the energy generated by one or more energy generators in real time, One or more energy consumption sensors configured to measure the energy consumed by one or more energy consumers in real time, A real-time energy tracking system comprising: a processor communicatively connected to one or more energy generating sensors and one or more energy consuming sensors, the processor configured to perform the method described in any one of claims 1 to 3, 5 to 18, or 20 to 24.

26. A computer program for performing real-time energy tracking, which, when executed by the processor, A step of receiving multiple generator data packets, each generator data packet containing one of a plurality of real-time generated energy measurements, and the associated generator metadata containing time data corresponding to the time when one of the plurality of real-time generated energy measurements was measured, A step of receiving multiple consumption data packets, each consumption data packet containing one of a plurality of real-time energy consumption measurements, and associated consumer metadata containing time data corresponding to the time when one of the plurality of real-time energy consumption measurements was measured, The steps include matching the multiple generated energy measurements with multiple consumed energy measurements according to the time data from the generator metadata and each consumer metadata, and obtaining multiple sets of matched measurements over a certain period of time, A computer program configured to cause a processor to perform the steps of determining and outputting a real-time energy deficit or surplus by comparing the generated energy measurement with a matching energy consumption measurement for each set of matching measurement values ​​over a certain period of time.

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