Apparatus and method for optimizing carbon emissions in a power grid

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.

What is claimed is: 
     
         1 . An apparatus for optimizing carbon emissions in a power grid network, the apparatus comprises:
 at least a processor; and   a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to:   receive a plurality of power flow data from at least a grid monitoring device communicatively connected to a grid network including a plurality of nodes, wherein the plurality of power flow data comprises at least a generation datum, at least a power consumption datum;   generate a power flow allocation for at least a first node and a second node in the network as a function of the at least a power consumption datum and the at least a generation datum;   determine, for at least a node of the plurality of nodes, a carbon flow as a function of the power flow allocation and a first set of stored relational rules;   generate an objective function of carbon flow and a second set of stored relational rules;   minimize the objective function of 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.   
     
     
         2 . The apparatus of  claim 1 , wherein the processor is configured to determine the power flow allocation as a function of a power flow machine learning model. 
     
     
         3 . The apparatus of  claim 1 , wherein the processor is further configured to generate an energy storage power model as a function of the at least a power datum. 
     
     
         4 . The apparatus of  claim 4 , wherein the processor in configured to determine an energy storage carbon intensity as a function of the energy storage power model. 
     
     
         5 . The apparatus of  claim 5 , wherein the processor is further configured to generate an energy storage carbon emission model as a function of the energy storage carbon intensity. 
     
     
         6 . The apparatus of  claim 6 , wherein the processor is further configured to determine the optimized grid parameters as a function of the energy storage carbon emission model. 
     
     
         7 . The apparatus of  claim 1 , wherein the processor is further configured to generate a projected carbon flow as a function of a carbon optimization model. 
     
     
         8 . The apparatus of  claim 1 , wherein the processor is further configured to reduce power output from at least a power generator as a function of the modified grid parameter. 
     
     
         9 . The apparatus of  claim 1 , wherein modifying the grid parameter further comprises using a Volt/Var control. 
     
     
         10 . The apparatus of  claim 1 , wherein the processor is further configured to generate a graphical representation of the carbon flow. 
     
     
         11 . A method for optimizing carbon emissions in a power grid, the method comprising:
 receiving, by at least a processor, a plurality of power flow data from at least a grid monitoring device communicatively connected to a grid network including a plurality of nodes, wherein the plurality of power flow data comprises at least a generation datum and at least a power consumption datum;   generating, by the at least a processor, a power flow allocation for at least a first node and a second node in the network as a function of the at least a power consumption datum and the at least a generation datum;   determining, by the at least a processor, a carbon flow as a function of the power flow allocation and a first set of stored relational rules for at least a node of the plurality of nodes;   generating, by the at least a processor, an objective function of a carbon flow and a second set of stored relational rules;   minimizing, by the at least a processor, the objective function of a carbon flow as a function of the optimization algorithm;   generating, by the at least a processor, a grid modification as a function of the minimization; and   modifying, by the at least a processor, a grid parameter of the grid network as a function of the grid modification.   
     
     
         12 . The method of  claim 11 , wherein the method further comprises determining the power flow allocation as a function of a power flow machine learning model. 
     
     
         13 . The method of  claim 11 , wherein the method further comprises generating an energy storage power model as a function of the power flow data. 
     
     
         14 . The method of  claim 14 , wherein the method further comprises determining an energy storage carbon intensity as a function of the energy storage power model. 
     
     
         15 . The method of  claim 15 , wherein the method is further comprises generating an energy storage carbon emission model as a function of the energy storage carbon intensity. 
     
     
         16 . The method of  claim 16 , wherein the method further comprises determining the optimized grid parameters as a function of the energy storage carbon emission model. 
     
     
         17 . The method of  claim 11 , wherein the method further comprises generating a projected carbon flow as a function of a carbon optimization model. 
     
     
         18 . The method of  claim 11 , wherein the method further comprises reducing power output from at least a power generator as a function of the modified grid parameter. 
     
     
         19 . The method of  claim 11 , wherein modifying the grid parameter further comprises using a Volt/Var control. 
     
     
         20 . The method of  claim 11 , wherein the method further comprises generating, by the at least a processor, a graphical representation of the carbon flow.