Abstract: A system for automating matching of energy load with energy generation for energy consumers, utilities, and gird operators, comprising a processor and a memory containing instructions configuring the processor to collect, from a grid network connecting load centers and generation sources, energy datasets including energy attribute records and energy load data, match each load center to the generation sources based on the energy datasets, matching further comprises generating actionable energy models, each one of the actionable energy models representing a configurable allocation of the energy attribute records, based on the energy datasets, and matching the load centers and generation sources as a function of at least one actionable energy model, populate an allocation report for each load center, and transmit the allocation report to an external device in communication with the processor.

Abstract: A system for automating matching of energy load with energy generation for energy consumers, utilities, and grid operators, comprising a processor and a memory containing instructions configuring the processor to collect, from a grid network connecting load centers and generation sources, energy datasets including energy attribute records and energy load data, match each load center to the generation sources based on the energy datasets, matching further comprises generating actionable energy models, each one of the actionable energy models representing a configurable allocation of the energy attribute records, based on the energy datasets, and matching the load centers and generation sources as a function of at least one actionable energy model, populate an allocation report for each load center, and transmit the allocation report to an external device in communication with the processor.

Abstract: A system for automating matching of energy load with energy generation for energy consumers, utilities, and gird operators, comprising a processor and a memory containing instructions configuring the processor to collect, from a grid network connecting load centers and generation sources, energy datasets including energy attribute records and energy load data, match each load center to the generation sources based on the energy datasets, matching further comprises generating actionable energy models, each one of the actionable energy models representing a configurable allocation of the energy attribute records, based on the energy datasets, and matching the load centers and generation sources as a function of at least one actionable energy model, populate an allocation report for each load center, and transmit the allocation report to an external device in communication with the processor.

Abstract: A method for calculating consumption-based carbon emissions in power grids is disclosed. The method includes receiving, from a first grid monitoring device monitoring a first grid, a plurality of first power output quantities from a plurality of power generators in the first grid; receiving, from a second grid monitoring device monitoring a second grid, a plurality of second power output quantities from a plurality of power generators in the second grid, wherein the first grid is connected to the second grid and can send and/or receive power to and/or from the second grid; computing and solving a linear system of equations based on variables representative of each the first grid and the second grid; and displaying, on a user interface, a consumption-based carbon emissions calculation describing carbon emissions consumed per megawatt-hours (MWh) produced by the first grid and the second grid based on solving the linear system of equations.

Abstract: Method and apparatus configured to receive a plurality of power flow data from at least a grid monitoring device connected to a grid network including a plurality of nodes, generate a power flow allocation for at least a node in the network as a function of the at least a power consumption datum and the at least a generation datum, determine a carbon flow as a function of the power flow allocation and a first set of stored relational rules, generate an objective function of a carbon flow and a second set of stored relational rules, minimize the objective function of a carbon flow as a function of the carbon optimization model and an optimization algorithm, generate a grid modification as a function of the minimization; and modify a grid parameter of the grid network as a function of the grid modification.

Abstract: A method for calculating consumption-based carbon emissions in power grids is disclosed. The method includes receiving, from a first grid monitoring device monitoring a first grid, a plurality of first power output quantities from a plurality of power generators in the first grid; receiving, from a second grid monitoring device monitoring a second grid, a plurality of second power output quantities from a plurality of power generators in the second grid, wherein the first grid is connected to the second grid and can send and/or receive power to and/or from the second grid; computing and solving a linear system of equations based on variables representative of each the first grid and the second grid; and displaying, on a user interface, a consumption-based carbon emissions calculation describing carbon emissions consumed per megawatt-hours (MWh) produced by the first grid and the second grid based on solving the linear system of equations.

Abstract: Method and apparatus configured to receive a plurality of power flow data from at least a grid monitoring device connected to a grid network including a plurality of nodes, generate a power flow allocation for at least a node in the network as a function of the at least a power consumption datum and the at least a generation datum, determine a carbon flow as a function of the power flow allocation and a first set of stored relational rules, generate an objective function of a carbon flow and a second set of stored relational rules, minimize the objective function of a carbon flow as a function of the carbon optimization model and an optimization algorithm, generate a grid modification as a function of the minimization; and modify a grid parameter of the grid network as a function of the grid modification.

Abstract: A system for machine-learning for prediction grid carbon emissions includes a computing device configured to receive, from a first local grid monitoring device monitoring a first local grid, a plurality of first power output quantities of a plurality of power generators in the first local grid, train an emission projection machine-learning process, wherein training further includes compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum and training the emission projection machine-learning process using the plurality of training data entries, and generate, using the emission projection machine-learning process, a projected carbon emission rate as a function of the plurality of power output quantities.

Abstract: Method and apparatus configured to receive a plurality of power flow data from at least a grid monitoring device connected to a grid network including a plurality of nodes, generate a power flow allocation for at least a node in the network as a function of the at least a power consumption datum and the at least a generation datum, determine a carbon flow as a function of the power flow allocation and a first set of stored relational rules, generate an objective function of a carbon flow and a second set of stored relational rules, minimize the objective function of a carbon flow as a function of the carbon optimization model and an optimization algorithm, generate a grid modification as a function of the minimization; and modify a grid parameter of the grid network as a function of the grid modification.

Abstract: A system for machine-learning for prediction grid carbon emissions includes a computing device configured to receive, from a first local grid monitoring device monitoring a first local grid, a plurality of first power output quantities of a plurality of power generators in the first local grid, train an emission projection machine-learning process, wherein training further includes compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum and training the emission projection machine-learning process using the plurality of training data entries, and generate, using the emission projection machine-learning process, a projected carbon emission rate as a function of the plurality of power output quantities.

Abstract: A system for machine-learning for prediction grid carbon emissions includes a computing device configured to receive, from a first local grid monitoring device monitoring a first local grid, a plurality of first power output quantities of a plurality of power generators in the first local grid, train an emission projection machine-learning process, wherein training further includes compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum and training the emission projection machine-learning process using the plurality of training data entries, and generate, using the emission projection machine-learning process, a projected carbon emission rate as a function of the plurality of power output quantities.