Carbon emission real-time measurement system and method

Abstract

A method for real-time carbon emission measurement, according to which real-time data indicative of a composition of at least one power source supplying power to a consumer during each predefined time window of a plurality of predefined time windows is received from an energy information gateway. For each predefined time window, a computerized device with at least one processor and associated memory, determines a power consumption value for each power source of the at least one power source. For each predefined time window and based on the power consumption value for each power source, the computerized device computes a carbon emission value which is displayed on a user device. Each predefined time window defines a discrete time interval configured to capture real-time variations in a composition of the at least one power source.

Description

[0001] 1

[0002] CARBON EMISSION REAL-TIME MEASUREMENT SYSTEM AND METHOD

[0003] Field of the Invention

[0004] The present invention relates generally to energy management and carbon accounting systems. More particularly, the invention pertains to methods and systems for real-time measurement, calculation, and reporting of carbon emissions associated with energy consumption from various power sources.

[0005] Background of the Invention

[0006] Increasing awareness of climate change and the need for sustainability have driven industries and governments to monitor and reduce carbon emissions. A significant portion of carbon emissions originates from energy consumption, particularly electricity derived from fossil fuel-based sources. In response, carbon accounting frameworks, such as the Greenhouse Gas Protocol, have been widely adopted to track and report emissions across different scopes.

[0007] Conventional carbon accounting systems typically operate using static emission factors— predefined average values that represent the amount of carbon dioxide equivalent (CO2e) emitted in the production of a unit of energy (e.g., per kWh), based on broad assumptions about the energy mix over time. These values are often published annually by governmental or environmental agencies and do not reflect the real-time variability of energy sources. Nor does the usage of this static emission factor reflect the specific consumption patterns of the organization using the emission factor to estimate its carbon emissions. For example, the same emission factor might be applied regardless of whether electricity was consumed during a period of high renewable availability (such as midday solar peaks) or during fossil fuel-dominant periods (such as evening hours). In addition, these systems rely heavily on historical energy usage data gathered through utility bills, monthly reports, or manually collected consumption logs. This retrospective data does not provide timely insights and is typically aggregated, which obscures fluctuations in carbon emission factors that may occur hourly, daily, or seasonally. 2

[0008] As a result, users are unable to respond to emission-intensive periods in real time, missing opportunities for carbon reduction or optimization. If real-time emission factor data were available and actionable, organizations could implement a variety of dynamic optimization techniques. For example, battery energy storage systems could be intelligently charged during low-carbon periods (e.g., when solar-generated or wind-generated power dominates the grid mix) and discharged during high- carbon periods to reduce reliance on fossil-based grid power. Similarly, energy- intensive operations— such as industrial processes, HVAC loads, or data center tasks— could be scheduled or shifted to coincide with cleaner energy availability. Facilities with on-site generation could prioritize usage of self-produced renewable energy when grid carbon emission factor (a measure of the average amount of carbon dioxide emissions per unit of electricity generated) is high, or curtail non-essential loads during dirty power intervals. These strategies would enable not only cost optimization but also active carbon footprint reduction, supporting compliance with emissions targets, sustainability goals, and regulatory frameworks.

[0009] There is therefore a need for a system and method for real-time measurement and attribution of carbon emissions from multiple energy sources, combining grid and on-site data to enable implementation of dynamic optimization strategies that minimize energy consumption during high-emission ("dirty") periods and maximize consumption during low-emission ("clean") periods.

[0010] Other objects and advantages of the invention will become apparent as the description proceeds.

[0011] Summary of the Invention

[0012] The present invention discloses a method and a system for real-time measurement of carbon emissions associated with energy consumption from multiple power sources. 3

[0013] In some embodiments, a method for real-time carbon emission measurement may include receiving real-time data indicative of a composition of at least one power source supplying power to a consumer during each of a plurality of predefined time windows. The method may further include determining, for each predefined time window, a power consumption value for each power source, and computing, for each predefined time window, a carbon emission value based on the power consumption value for each power source. The computed carbon emission value may then be displayed on a user device. Each predefined time window may define a discrete time interval configured to capture real-time variations in the composition of the at least one power source.

[0014] In some embodiments, each predefined time window may be less than or equal to one hour. In other embodiments, the duration of each predefined time window may be dynamically adjusted based on detected variations in the composition of the at least one power source. Receiving the real-time composition data may include obtaining such data from one or more entities, such as grid operators or energy market operators, or from at least one energy monitoring gateway associated with one or more power sources.

[0015] The at least one power source may include renewable or non-renewable power sources, grid-connected or non-grid-connected power sources, or combinations thereof. In certain embodiments, a power source may be a non-aggregate source of a single power source type or an aggregate power source comprising multiple types, wherein different types have different emission factors.

[0016] In some embodiments, computing the carbon emission value may include multiplying a carbon emission factor associated with each power source by the power consumption value for the power source within the predefined time window. The carbon emission factor may be determined based on publicly available data sources such as governmental agencies, grid operators, energy market operators, and recognized environmental organizations, or based on one or more contractual 4

[0017] agreements for energy procurement from specified power sources. In other embodiments, the carbon emission factor may be computed in real time by dividing a measured carbon emission value by an energy production value for the corresponding power source.

[0018] In certain embodiments, the method may include computing a "green" carbon emission value for each predefined time window by multiplying the carbon emission value by a relative energy contribution of production sources designated as renewable, where the carbon emission factor associated with each renewable source is below a predefined emission threshold. Similarly, a "dirty" carbon emission value may be computed by multiplying the carbon emission value by a relative energy contribution of production sources designated as dirty, where the carbon emission factor associated with each dirty source is above the predefined emission threshold. A user interface may display graphical representations of the carbon emission value, the green carbon emission value, and / or the dirty carbon emission value for each predefined time window.

[0019] In some embodiments, a system for real-time carbon emission measurement may include at least one energy information gateway and a processor configured to receive, from the gateway, real-time data indicative of a composition of at least one power source supplying power to a consumer during each predefined time window. The processor may determine a power consumption value for each power source, compute a carbon emission value, and provide the carbon emission value for display on a user device.

[0020] In certain embodiments, a system for dynamic energy storage control may be configured to obtain carbon emission values, one or more of which may be obtained by performing the real-time carbon emission measurement method described above. The processor of the system may receive, for each predefined time window, a carbon emission value associated with power supplied to a consumer, compare the carbon emission value to a first and a second emission threshold, and, based on the 5

[0021] comparison, control charging or discharging of an energy storage device. The energy storage device may be charged when the carbon emission value is below the first emission threshold and discharged when the carbon emission value exceeds the second emission threshold.

[0022] In one aspect, whenever energy production is provided by an energy storage device, the discharge of the energy stored in the energy storage device into the grid may be characterized by a corresponding carbon emission factor. This may be done by measuring and recording the carbon emission factor during a storage period and dividing the recorded carbon emission factor by the total amount of power that is discharged to the grid from the energy storage device, during a discharge period.

[0023] The amounts of transferred energy during storage and discharge periods may be equal or unequal.

[0024] In other embodiments, a system for dynamic electric load control may be configured to obtain carbon emission values, one or more of which may be obtained by performing the real-time carbon emission measurement method described above. The processor may receive, for each predefined time window, a carbon emission value associated with a power supply; determine, for each controllable consumer device powered by the power supply, an electric load profile for the device; and, based on the electric load profile, schedule activation of the device for a selected predefined time window in which the carbon emission value is lower than a predefined emission threshold.

[0025] Brief Description of the Drawings

[0026] In the drawings:

[0027] FIG. 1 is a method for real-time carbon emission measurement, according to some embodiments of the invention; and

[0028] FIG. 2 is a system for real-time carbon emission measurement, according to some embodiments of the invention. 6

[0029] Detailed Description of the Invention

[0030] The present invention discloses a method and a system for real-time measurement of carbon emissions associated with energy consumption from multiple power sources. Advantageously, the present invention overcomes the limitations of conventional carbon accounting systems by enabling real-time measurement, calculation, and attribution of carbon emissions based on actual energy consumption patterns and dynamic energy source composition. Unlike traditional methods that rely on static, retrospective emission factors and aggregated utility data, the invention provides granular, time-sensitive insights into the emission factor of energy use. This real-time visibility allows organizations to implement intelligent, automated control strategies— such as optimizing battery charge and discharge cycles, shifting energy-intensive operations to cleaner periods, and prioritizing onsite renewable usage— thereby actively reducing carbon emissions. By integrating grid, on-site renewable (e.g., solar and wind), and non-renewable (e.g., diesel generators) data sources, the system supports more accurate emissions tracking and facilitates proactive decision-making aligned with sustainability goals, regulatory compliance, and energy cost optimization.

[0031] Reference is now made to Figs. 1 and 2. Fig. 1 illustrates a method 100 for real-time carbon emission measurement, according to some embodiments of the invention. FIG. 2 illustrates a system 200 for real-time carbon emission measurement, according to some embodiments of the invention.

[0032] One or more components of system 200 may implement the steps of method 100. In some embodiments, system 200 for real-time carbon emission measurement may include at least one energy information gateway and at least one processor 210 (which may be in data communication with an associated memory containing dedicated software for performing all the required calculation and displaying results). The processor 210 may receive, from the at least one energy information gateway, real-time data indicative of a composition of at least one power source 60 7

[0033] supplying power to a consumer during each predefined time window of a plurality of predefined time windows. For each predefined time window, the processor 210 may determine a power consumption value for each power source 60 of the at least one power source. The processor 210 may compute, for each predefined time window and based on the power production value for each power source, a carbon emission value. The carbon emission value may be provided by the processor 210 for display on a user device 80.

[0034] The at least one energy information gateway may include an Energy Monitoring Gateway 240 (a device that collects real-time data from smart meters and other sensors, transmits it to a network, and sends it to a cloud platform for analysis, monitoring, and control) and a Grid Carbon Profile Gateway 230 (a device for collecting, analyzing, and presenting data about a power grid's carbon emission factors, often using a "carbon profile" that includes factors like kgCO2e per kWh), which comprises Production Mix Gateway and a Production Carbon Gateway. Each gateway may be implemented as a software-based interface, a hardware module, or a hybrid system that facilitates secure and structured communication with external data sources, such as grid operators, energy markets, or sensor networks. Depending on the deployment architecture, a gateway may reside on an edge device— enabling low-latency data acquisition and localized processing— or operate in a cloud environment. Communication may be enabled through standardized Application Programming Interfaces (APIs).

[0035] Method 100 may include the following steps. At step 110, real-time data indicative of a composition of at least one power source supplying power to a consumer during each predefined time window of a plurality of predefined time windows may be received. For example, processor 210 may receive real-time data indicative of a composition of the at least one power source supplying power to a consumer during each predefined time window of a plurality of predefined time windows from grid carbon profile gateway 230. 8

[0036] Each predefined time window may be defined as a discrete time interval configured to capture real-time variations in the composition of the at least one power source. In some embodiments, each predefined time window may be defined as less than or equal to one hour. Each predefined time window may be adjusted dynamically (e.g., by processor 210) based on detected variations in the composition of the at least one power source (e.g., at least one power source 60).

[0037] The at least one power source may include one or more power sources selected from a group that includes renewable power sources (e.g., solar farms, wind farms, biomass generation systems), non-renewable power sources (e.g., natural gas turbines, diesel generators, coal-fired systems), and combinations thereof.

[0038] The at least one power source may include one or more power sources selected from a group that includes grid-connected power sources (e.g., electricity supplied from a national or regional grid powered by a mix of coal, natural gas, nuclear, hydroelectric, wind, and solar plants), non-grid-connected power sources (e.g., onsite diesel generators, standalone wind turbines, and standalone renewable systems such as solar arrays), and combinations thereof.

[0039] Each power source may be selected from a group that includes a non-aggregate power source of a single power source type (e.g., a standalone diesel generator or a solar-panel array) and an aggregate power source comprising multiple power source types (e.g., a regional electricity grid combining fossil fuel, nuclear, and renewable energy sources). Different power source types may be associated with different emission factors, which refer to the amount of greenhouse gas emissions produced per unit of energy generated. Emission factors are typically expressed in kilograms of carbon dioxide equivalent per kilowatt-hour (kgCO2e / kWh). For instance, coal-produced electricity may have an emission factor of approximately 0.9 kg CO2e / kWh, while natural gas may be around 0.3-0.5 kg CO2e / kWh, and renewable sources such as wind and solar typically have near-zero operational emission factors (e.g., <0.05 kgCO2e / kWh). 9

[0040] The real-time data indicative of the composition of the at least one power source may be received, for each predefined time window, from one or more entities selected from a group that includes grid operators and energy market operators. The real-time data indicative of the composition of power sources on a grid (e.g., a grid 61) may be received, for each predefined time window, from one or more entities selected from a group that includes grid operators and energy market operators. For example, the real-time data indicative of the composition of power sources on the grid may be received, for each predefined time window, from Emissions and Grid Data Providers 220 (that offer information for calculating environmental impact and managing electricity use) through Grid Carbon Profile Gateway 230. Such Emissions and Grid Data Providers may include grid operators or authorized energy market entities, such as NOGA - the Israeli Independent System Operator (ISO - organization that manages the operation of an electricity grid to ensure reliability and promote competition), and the European Network of Transmission System Operators for Electricity (ENTSO-E), which aggregates and disseminates grid information from various Transmission System Operators (TSOs - organizations that are responsible for the safe, reliable, and efficient transmission of electricity over high-voltage grids from power plants to local distribution networks) across participating European countries.

[0041] The real-time data indicative of the composition of the at least one power source may be received, for each predefined time window, from at least one energy monitoring gateway (e.g., Energy Monitoring Gateway 240) associated with one or more power sources of the at least one power source. The at least one energy monitoring gateway (e.g., Energy Monitoring Gateway 240) may be associated with one or more standalone on-site power sources such as diesel generators (e.g., diesel generator 62), wind turbines (e.g., wind turbine 63), and solar arrays (e.g., solarpanel array 64), which may be independently monitored to determine their contribution to the overall power mix. 10

[0042] In some embodiments, the at least one energy monitoring gateway may include a self-production gateway (e.g., Self-Production Gateway 241) and a power meter gateway (e.g., Power Meter Gateway 242). The self-production gateway may be associated with standalone renewable energy sources such as wind turbines and solar panel arrays. The power meter gateway, on the other hand, may be associated with standalone fossil fuel-based generators.

[0043] At step 120, for each predefined time window, a power consumption value for each power source of the at least one power source may be determined. The power consumption value for each power source of the at least one power source may be determined by the processor (e.g., processor 210). The power production value for each power source may be determined, for each predefined time window, by receiving real-time data indicative of the power production of the power source from one or more entities selected from a group that includes grid operators and energy market operators. For example, the power production value for each power source connected to the grid may be determined by processor 210, for each predefined time window, by receiving real-time data indicative of the power production of each power source connected to the grid from one or more entities selected from a group that includes grid operators (e.g., NOGA, regional transmission system operators) and energy market operators (e.g., ENTSO-E, independent system operators, or market clearing authorities). These entities may provide metered or modeled data reflecting real-time power consumption values for each power source or group of sources, including aggregated consumption data for specific fuel types or generation categories.

[0044] The real-time data indicative of the power consumption of the power source may be received from at least one energy monitoring gateway associated with the power source. For example, the power production value for each onsite standalone power source may be determined by processor 210, for each predefined time window, by receiving real-time data indicative of the power production of each onsite standalone power source connected to the grid from the at least one energy 11

[0045] monitoring gateway associated with the onsite standalone power source (e.g., wind turbines, diesel generators, and solar arrays).

[0046] In some embodiments, the at least one energy monitoring gateway may include a self-production gateway (e.g., Self-Production Gateway 241) and a power meter gateway (e.g., Power Meter Gateway 242). The self-production gateway may be associated with standalone renewable energy sources (e.g., wind turbines and solar panel arrays). The power meter gateway may be associated with standalone fossil fuel-based generators (e.g., diesel generators).

[0047] At step 130, based on the power consumption value for each power source, a carbon emission value may be computed for each predefined time window. The carbon emission value may be computed by the processor (e.g., processor 210) for each predefined time window. A carbon emission value for each predefined time window may be computed, for each power source, by multiplying a carbon emission factor associated with the power source by the power consumption value for the power source within the predefined time window. The carbon emission factor may be expressed in kilograms of carbon dioxide equivalent per kilowatt-hour (kgCO2e / kWh), and the power production value in kilowatt-hours (kWh). Accordingly, the carbon emission value for each power source may be expressed in kilograms of carbon dioxide equivalent (kgCO2e).

[0048] For a predefined time window, an aggregate carbon emission value may be computed by summing the individual carbon emission values across multiple power sources, each having a different associated carbon emission factor. For example, if one power source has a power consumption value of 100 kWh with a carbon emission factor of 0.8 kg CO2e / kWh, and a second power source has a power consumption value of 200 kWh with a carbon emission factor of 0.1 kg CO2e / kWh, the aggregate carbon emission value for the predefined time window would be: (100 × 0.8) + (200 × 0.1) = 80 + 20 = 100 kg CO2e. 12

[0049] The carbon emission factor associated with each power source may be determined (e.g., by processor 210) based on emission factors that are obtained from publicly available data sources, which may include governmental agencies (e.g., U. S. Environmental Protection Agency (EPA), European Environment Agency (EEA)), grid operators (e.g., National Grid Operator [NOGA], European Network of Transmission System Operators for Electricity [ENTSO-E]), energy market operators (e.g., PJM Interconnection, California ISO, EPEX SPOT), and recognized environmental organizations (e.g., Intergovernmental Panel on Climate Change (IPCC), International Energy Agency (IEA), Carbon Trust). These sources may provide standardized or region-specific emission factors expressed in units such as kgCO2e / kWh, which may be used to calculate the carbon emission value for each power source within each predefined time window. For example, emission factors may include approximately 0.9 kg CO2e / kWh for coal-fired power plants, 0.5 kg CO2e / kWh for natural gas-fired plants, and near 0.0 kg CO2e / kWh for renewable energy sources such as wind and solar. These emission factors may be mapped directly to the carbon emission factor for the corresponding power source and used in the computation of carbon emission values based on the power production value for that source during the time window.

[0050] The carbon emission factor may be determined (e.g., by processor 210) based on one or more contractual agreements to procure energy from one or more specified power sources. Such agreements may include power purchase agreements (PPAs), renewable energy certificates (RECs - market-based instruments that represent the ownership of the environmental attributes of one megawatt-hour (MWh) of renewable electricity), direct procurement contracts, or guarantees of origin, which specify the type and source of energy delivered (e.g., wind farms, solar installations, or natural gas plants). The carbon emission factor may be directly assigned based on the known emission profile of the contracted power source, as disclosed in the agreement or verified by an independent certifying body. This approach enables the use of emission factors of the contracted power source even when the energy is delivered through the grid and not through the of the contracted power source, by attributing consumption to the contractual power source rather than the average 13

[0051] grid mix. For example, a facility with a PPA tied to a certified wind farm may apply a carbon emission factor of approximately 0.0 kg CO2e / kWh to the associated power consumption value, regardless of regional grid carbon emission factor.

[0052] For each predefined time window, the carbon emission factor associated with each power source may be computed (e.g., by processor 210) by receiving, for each power source, real-time data representing an energy production value and a corresponding carbon emission value within the predefined time window, and computing the carbon emission factor as a ratio of the carbon emission value to the energy production value. The energy production value may be expressed in kilowatt-hours (kWh), and the carbon emission value in kilograms of carbon dioxide equivalent (kgCO2e), resulting in a carbon emission factor in kgCO2e / kWh.

[0053] The energy production and carbon emission values may be obtained from one or more Emissions and Grid Data Providers (e.g., Emissions and Grid Data Providers 220), which may include national grid operators (e.g., NOGA), regional transmission system operators, and international energy data aggregators (e.g., ENTSO-E). These providers may deliver time-stamped, source-specific emissions and generation data, enabling dynamic calculation of the carbon emission factor for each power source as it varies over time.

[0054] For example, during a predefined one-hour time window, a power source such as a coal-fired power plant may report a carbon emission value of 450,000 kilograms of carbon dioxide equivalent (kg CO2e) and an energy production value of 500,000 kilowatt-hours (kWh). The carbon emission factor for that power source during the time window may be calculated by dividing the carbon emission value by the energy production value, i.e., 450,000 ÷ 500,000, resulting in a carbon emission factor of 0.9 kgCO2e / kWh. This value may then be used to compute the carbon emission value associated with the power consumption from that source during the same or subsequent predefined time window. 14

[0055] In some embodiments, a Dynamic Grid Carbon emission Algorithm (tracks and predicts the carbon emissions of electricity in real-time, often using methods like the flow network model or time-series prediction models) may be executed to determine a dynamic carbon emission factor for each production-side power source (e.g., coal plants, gas turbines, renewable sources) during each predefined time window (e.g., hourly). The algorithm receives as input a time series of energy production values (energy_produced_src_x) and associated production-side carbon emission values (carbon_emitted_src_x) for each power source. For each interval, and for each power source, if both energy production data and emitted carbon data are available, a carbon emission factor for that power source (carbon_emission_factor_src_x) may be computed as the ratio of carbon emissions to energy produced (e.g., in units of kg CO2e / kWh) as follows:

[0056] carbon_emission_factor_src_x = carbon_emitted_src_x / energy_produced_src_x.

[0057] In case of missing data points (e.g. an hour for which no energy or no emission was reported), different methods and heuristics may be applied to fill the date gaps, such as using a mean of the previous 10 values, a mean of the value before and after the gap, a linear completion of the gap and more.

[0058] If such data is unavailable, a previously established or static carbon emission factor may be used. The static carbon emission factor may be based on published emission factors for that energy source as provided by relevant organizations, such as the International Energy Agency (IEA), the IEA Greenhouse Gas R& D Programme (IEA GHG - a research and development program under the International Energy Agency (IEA), focusing on technologies to reduce greenhouse gas emissions), or the European Network of Transmission System Operators for Electricity (ENTSO-E). The output of this algorithm is a time-varying (dynamic) carbon emission factor for each power source, enabling refined emissions estimation based on real production conditions. 15

[0059] In some embodiments, a Grid Carbon Profile Algorithm may be used to calculate the hourly carbon emission of electricity supplied from the grid and to determine the proportion of energy classified as "green" or "dirty" within each predefined time window. The algorithm receives as input a time series of energy production values and corresponding carbon emission factors per production-side power source contributing to the grid. For each predefined time window, the algorithm calculates the total energy produced by all sources:

[0060] total_produced_energy = Σ (production_src_x)

[0061] and determines the production share (ratio) of each power source relative to this total:

[0062] ratio_src_x = production_src_x / total_produced_energy

[0063] The grid carbon emission factor is computed as a weighted sum of carbon emission factors across all contributing sources:

[0064] carbon_emission_factor = Σ (ratio_src_x * carbon_emission_factor_src_x)

[0065] Additionally, the algorithm computes the percentage of energy considered green or dirty by aggregating contributions from power sources categorized accordingly (e.g., renewable vs. fossil-based), based on their relative production ratios and emission profiles. If a source is considered green, its contribution to the green portion is calculated as:

[0066] green_percent += ratio_src_x * 100

[0067] green_emission_factor += ratio_src_x * carbon_emission_factor_src_x

[0068] Otherwise, its contribution to the dirty portion is calculated as: 16

[0069] dirty_percent += ratio_src_x * 100

[0070] dirty_emission_factor += ratio_src_x * carbon_emission_factor_src_x,

[0071] where green_percent, green_emission_factor, dirty_percent and dirty_emission_factor are all assigned initial values of zero.

[0072] In some embodiments, a Location-Based Algorithm may be executed to compute a localized carbon emission factor and energy mix profile, specific to the actual sources of consumed energy at a site (e.g., a building, a data center or factory). Inputs may include a time series of energy consumption values and associated carbon emission factors for each consumed power source (e.g., grid, on-site renewables, fossil fuel generators). For each predefined time window, the algorithm calculates the total consumed energy:

[0073] total_consumed_energy = Σ (consumption_src_x).

[0074] and the proportional contribution of each source:

[0075] ratio_src_x = consumption_src_x / total_consumed_energy.

[0076] A carbon emission value is then computed by weighting the carbon emission factor of each power source by its share of the total energy consumed:

[0077] carbon_emission_factor = Σ (ratio_src_x * carbon_emission_factor_src_x).

[0078] Green and dirty energy percentages are also computed, based on the classification of each power source (e.g., wind / solar as green, diesel / natural gas as dirty), allowing site-level emissions and energy mix reporting. These percentages are calculated using:

[0079] green_percent += ratio_src_x * 100 * green_percent_src_x, 17

[0080] dirty_percent += ratio_src_x * 100 * dirty_percent_src_x,

[0081] where green_percent and dirty_percent are both assigned initial values of zero.

[0082] At step 140, the computed carbon emission value may be displayed (e.g., by processor 210) on a user device. The carbon emission value may be displayed by presenting, on a user interface, a graphical representation of at least one of the following: the carbon emission value, the green carbon emission value, or the dirty carbon emission value, each computed for the plurality of predefined time windows.

[0083] In some embodiments, system 200 may include an Energy Storage Gateway 250 that is associated with one or more energy storage devices 70 (e.g., batteries). The Energy Storage Gateway 250 may be configured to monitor, manage, and report data related to the charge and discharge cycles, storage capacity, and energy flow associated with the connected energy storage devices 70.

[0084] In certain embodiments, processor 210 may dynamically control the charge and discharge cycles of the energy storage devices 70 via the Energy Storage Gateway 250 based on real-time carbon emission values. Processor 210 may dynamically control the charge and discharge cycles of the energy storage devices 70 via the Energy Storage Gateway 250 based on real-time carbon emission values computed by method 100. For example, when on-site renewable generation (e.g., from solar arrays 64 and wind turbines 63) is high and carbon emission values are low, Processor 210 may prioritize charging the energy storage devices 70; conversely, during periods of high carbon emissions factor— such as when the grid 61 is fossilfuel dominant or on-site diesel generators 62 are in use— the processor 210 may trigger discharging of stored energy from the energy storage devices 70 to minimize emissions associated with real-time energy consumption.

[0085] In some embodiments, a system for dynamic energy storage control may be provided that includes a processor configured to obtain carbon emission values. One 18

[0086] or more of the obtained carbon emission values may be obtained by performing method 100. The processor may be further configured to receive, for each predefined time window, a carbon emission value associated with power supplied to a consumer; compare the carbon emission value to a first emission threshold and a second emission threshold; and, based on the comparison, control a charge or discharge cycle of an energy storage device. In such embodiments, the energy storage device may be charged when the carbon emission value is below the first emission threshold and discharged when the carbon emission value exceeds the second emission threshold.

[0087] In one example, method 100 may calculate carbon emission values for power supplied to a commercial building in 1-hour predefined time windows. One or more of these calculated values may be provided to a system for dynamic energy storage control. In this example, the first emission threshold may be set to 200 g CO2 / kWh, and the second emission threshold may be set to 500 g CO2 / kWh.

[0088] At 13:00, the system for dynamic energy storage control may receive a carbon emission value of 180 g CO2 / kWh, which is below the first emission threshold. In response, the system may charge the energy storage device using grid power. At 18:00, the system may receive a carbon emission value of 550 g CO2 / kWh, which exceeds the second emission threshold. In this case, the system may discharge the energy storage device to supply the building's load, thereby reducing reliance on high-emission grid electricity during that period.

[0089] According to another embodiment, the proposed system further increases the measurement accuracy in cases where the energy production is provided by an energy storage device, such that energy storage and energy discharge are performed in different times, with different energy mix value for each time. For example, if the energy storage device consumes power from the grid to store energy at noon, during that time period, the energy mix of the power may comprise 30% green energy with a lower carbon emission factor. On the other hand, if the energy storage device 19

[0090] consumes power from the grid to store energy at night, during that time period, the energy mix of the power may comprise no green energy with a high carbon emission factor.

[0091] In order to accurately reflect different energy mix values during consumption at the discharge time, the system records the carbon emission factor during a storage period, which is divided by the total amount of power that is discharged to the grid as a result of this storage. This way, each kWh that is discharged to the grid is characterized by a corresponding carbon emission factor.

[0092] The amounts of transferred energy during storage and discharge periods may be equal or unequal.

[0093] In certain embodiments, a system for dynamic electric load control may be provided that includes a processor configured to obtain carbon emission values, with one or more of the obtained values being obtained by performing the method 100. The processor may be further configured to receive, for each predefined time window, a carbon emission value associated with a power supply; determine, for each controllable consumer device powered by the power supply, an electric load profile for the device; and, based on the electric load profile, schedule activation of the controllable consumer device for a selected predefined time window in which the carbon emission value is lower than a predefined emission threshold.

[0094] In one example, the processor of the system for dynamic electric load control may receive carbon emission values for power supplied to a residential property in 1-hour predefined time windows. The carbon emission values may be calculated according to method 100. The system may determine, for each controllable consumer device-such as a dishwasher, clothes dryer, or electric vehicle charger— an electric load profile that describes the device's power consumption pattern and operational flexibility. 20

[0095] For instance, the electric vehicle charger may have a load profile indicating that it requires 4 continuous hours of charging and can be scheduled anytime between 20:00 and 08:00. The system may identify that between 02:00 and 06:00, the carbon emission value is consistently lower than a predefined emission threshold of 250 g CO2 / kWh. In this case, the system may schedule activation of the charger to start at 02:00, thereby ensuring the charging occurs during low-emission periods while still meeting the user's requirement for a fully charged vehicle by morning.

[0096] In some embodiments, system 200 may include an application programming interface (API) configured to receive real-time carbon emission data from one or more third-party emissions providers and to transmit carbon emission values to external building management systems (BMS), supervisory control and data acquisition (SCADA) systems, task scheduling systems, online web traffic routing systems or reporting dashboards.

[0097] In certain embodiments, system 200 may include a forecast-based energy control module configured to: (a) receive a predicted carbon emission factor for at least one future predefined time window; and (b) schedule charging or discharging of an energy storage system based on the forecasted carbon emission profile, taking into account multiple objectives, including energy price and carbon emission values, and constraints such as device utilization and future availability.

[0098] The descriptions and examples set forth in this disclosure are provided for illustrative purposes only and are not intended to limit the scope of the invention. It will be understood by those skilled in the art that various changes, substitutions, and modifications may be made without departing from the spirit and scope of the invention. The invention is not limited to the particular structures, configurations, or methods described but encompasses all variations, alternatives, and equivalents as defined by the appended claims.

Claims

21Claims1. A method for real-time carbon emission measurement, comprising:a) receiving, from an energy information gateway, real-time data indicative of a composition of at least one power source supplying power to a consumer during each predefined time window of a plurality of predefined time windows;b) for each predefined time window, determining, by at least one processor and associated memory, a power consumption value for each power source of the at least one power source;c) for each predefined time window, computing by said at least one processor and based on the power consumption value for each power source, a carbon emission value; andd) displaying the carbon emission value on a user device,wherein each predefined time window defines a discrete time interval configured to capture real-time variations in a composition of the at least one power source.

2. The method of claim 1, wherein each predefined time window is less than or equal to one hour.

3. The method of claim 1, wherein each predefined time window is dynamically adjusted based on detected variations in the composition of the at least one power source.

4. The method of claim 1, wherein receiving the real-time data indicative of the composition of the at least one power source comprises, for each predefined time window, receiving the real-time data from one or more entities selected from a group comprising grid operators and energy market operators.

5. The method of claim 1, wherein receiving the real-time data indicative of the composition of the at least one power source comprises, for each predefined time window, receiving the real-time data indicative of the composition of the at22least one power source from at least one energy monitoring gateway associated with one or more power sources of the at least one power source.

6. The method of claim 1, wherein determining a power consumption value for each power source comprises, for each predefined time window, receiving realtime data indicative of power consumption of the power source from one or more entities selected from a group comprising grid operators and energy market operators.

7. The method of claim 1, wherein determining a power consumption value for each power source comprises, for each predefined time window, receiving the real-time data indicative of the power consumption of the power source from at least one energy monitoring gateway associated with the power source.

8. The method of claim 1, wherein the at least one power source comprises one or more power sources selected from a group comprising renewable power sources, non-renewable power sources, and combinations thereof.

9. The method of claim 1, wherein the at least one power source comprises one or more power sources selected from a group comprising grid-connected power sources, non-grid-connected power sources, and combinations thereof.

10. The method of claim 1, wherein each power source is selected from a group comprising a non-aggregate power source of a single power source type and an aggregate power source comprising multiple power source types, wherein different power source types have different emission factors.

11. The method of claim 1, wherein computing the carbon emission value for each predefined time window comprises, for each power source, multiplying a carbon emission factor associated with the power source by the power consumption value for the power source within the predefined time window.2312. The method of claim 11, comprising determining the carbon emission factor associated with each power source based on emission factors obtained from publicly available data sources, wherein the publicly available data sources comprise governmental agencies, grid operators, energy market operators, and recognized environmental organizations.

13. The method of claim 11, comprising determining the carbon emission factor associated with each power source based on one or more contractual agreements to procure energy from one or more specified power sources.

14. The method of claim 11, comprising, for each predefined time window, computing the carbon emission factor associated with each power source, the computing comprising:a) receiving, for each power source, real-time data representing an energy production value and a corresponding carbon emission value within the predefined time window; andc) computing the carbon emission factor as a ratio of the carbon emission value to the energy production value for the predefined time window.

15. The method of claim 11, comprising computing a green carbon emission value for each predefined time window, the computing comprising:multiplying, for each predefined time window, the carbon emission value by a relative energy contribution of production sources designated as renewable to total energy supplied to the consumer,wherein the carbon emission factor associated with each of the renewable power sources is below a predefined emission threshold.

16. The method of claim 11, comprising computing a dirty carbon emission value for each predefined time window, by:24multiplying, for each predefined time window, the carbon emission value by a relative energy contribution of production sources designated as dirty to total energy supplied to the consumer,wherein the carbon emission factor associated with each of the dirty power sources is above a predefined emission threshold.

17. The method of claim 1, wherein displaying the carbon emission value comprises presenting, on a user interface, a graphical representation of at least one of: the carbon emission value, the green carbon emission value, or the dirty carbon emission value, computed for each of the plurality of predefined time windows.

18. The method of claim 1, wherein whenever energy production is provided by an energy storage device, characterizing the discharge of the energy stored in said energy storage device into the grid by a corresponding carbon emission factor, by:a) measuring and recording the carbon emission factor during a storage period; andb) dividing the recorded carbon emission factor by the total amount of power being discharged to the grid from said energy storage device, during a discharge period.

19. The method of claim 1, wherein the amounts of transferred energy during storage and discharge periods are equal.

20. The method of claim 1, wherein the amounts of transferred energy during storage and discharge periods are unequal.

21. A system for real-time carbon emission measurement, comprising:a) at least one energy information gateway;b) a processor configured to:25b.1) receive, from at least one energy information gateway, real-time data indicative of a composition of at least one power source supplying power to a consumer during each predefined time window of a plurality of predefined time windows;b.2) determine, for each predefined time window, a power consumption value for each power source of the at least one power source;b.3) compute, for each predefined time window, based on the power consumption value for each power source, a carbon emission value; and b.4) provide the carbon emission value for display on a user device, wherein each predefined time window is configured to capture real-time variations in a composition of the at least one power source.

22. A system for dynamic energy storage control, comprising at least one processor configured to obtain carbon emission values, wherein one or more of the obtained carbon emission values are obtained by performing the method of claim 1, said at least one processor being configured to:a) receive, for each predefined time window, a carbon emission value associated with power supplied to a consumer;b) compare the carbon emission factor to one or more predetermined emission thresholds; andc) based on the comparison result, controlling the charge or discharge of an energy storage device.

23. A system according to claim 22, in which the energy storage device is charged when the carbon emission value is below a first predetermined emission threshold, and the energy storage device is discharged when the carbon emission value exceeds a second predetermined emission threshold.

24. A system for dynamic electric load control, comprising at least one processor configured to obtain carbon emission values, wherein one or more of the26obtained carbon emission values are obtained by performing the method of claim 1, said at least one processor being configured to:a) receive, for each predefined time window, a carbon emission value associated with a power supply;b) for each controllable consumer device powered by the power supply, determine an electric load profile for the controllable consumer device; and c) based on the electric load profile, schedule activation of the controllable consumer device for a selected predefined time window in which the carbon emission value is lower than a predefined emission threshold.

25. The system of claim 22, further comprising:a) an energy storage device being charged from a power grid;b) at least one processor and associated memory, being in data communication with said energy storage device, which are configured to:b.l) measure and record the carbon emission factor during a storage period for storing an amount of energy in said energy storage device; andb.2) divide the recorded carbon emission factor by the total amount of power being discharged to the grid from said energy storage device, during a discharge period, to thereby characterize the discharge of the energy stored in said energy storage device into the grid by a corresponding carbon emission factor.

26. The system of claim 25, in which the amounts of transferred energy during storage and discharge periods are equal.

27. The system of claim 25, in which the amounts of transferred energy during storage and discharge periods are unequal.

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