Power Grid Carbon Flow Optimization at Node Level
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Solution Overview
Problem
Current power grid systems lack effective methods to optimize carbon emissions, as they rely on grid-wide averages that fail to account for network topology and temporal/spatial variations, leading to inefficient carbon emission tracking and reduction.
Innovation Solution
An apparatus and method utilizing a processor to receive power flow data from a grid monitoring device, generating power flow allocation, calculating carbon flow, and optimizing carbon emissions by modifying grid parameters based on a carbon optimization model and optimization algorithm, thereby minimizing carbon footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If grid-wide average methods are used for carbon emission tracking, then the system complexity is reduced, but the measurement precision of carbon emissions deteriorates
Solution Approach 1:
The patent segments the power grid into individual nodes and traces carbon emissions along specific power flow paths from generators to consumers. This segmentation allows precise attribution of carbon emissions to specific nodes and time periods, resolving the contradiction by maintaining low system complexity through modular tracking while achieving high measurement precision at the node level.
Solution Approach 2:
The patent implements local quality by tracking carbon emissions specifically at individual nodes and time periods rather than using uniform grid-wide averages. This enables differentiated carbon intensity calculations for each node, improving measurement precision while keeping the overall system manageable through localized data collection and processing.
2Measurement precision
If node-level carbon flow tracking is implemented, then the measurement precision of carbon emissions is improved, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary carbon flow tracing mechanism that uses power flow data as a mediator to attribute carbon emissions from generators to consumers. This intermediary approach simplifies the tracking system by using existing power flow measurements rather than requiring direct carbon measurement at each node, thus improving precision while limiting complexity growth.
Solution Approach 2:
The system uses existing grid monitoring infrastructure and power flow data to self-determine carbon emissions at each node. By leveraging already-collected operational data, the patent avoids the need for additional complex measurement devices, achieving node-level precision without proportionally increasing system complexity.
3Productivity
If real-time optimization of carbon emissions is performed, then the productivity of carbon reduction is improved, but the device complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where carbon flow information is continuously calculated and used to guide power flow optimization decisions. This feedback loop enables real-time carbon reduction by adjusting power dispatch based on carbon intensity, improving productivity while maintaining manageable complexity through iterative optimization rather than complete system redesign.
Solution Approach 2:
The system dynamically adjusts power flow allocations based on real-time carbon intensity calculations and changing grid conditions. This dynamic optimization improves carbon reduction productivity by adapting to temporal and spatial variations in carbon emissions, while the modular dynamic nature of the adjustments prevents excessive system complexity.
Data Source
AI summary
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.