Distributed Line Flow Computing for Power Grids
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Solution Overview
Problem
The electric energy industry faces challenges in efficiently managing transmission system congestion due to the lack of information sharing between utilities and control areas, leading to inefficient utilization of resources and financial distortions, and existing methods struggle to reconcile sensor data with operational objectives for N−1 security and optimal line flow management.
Innovation Solution
A distributed line flow computing method that uses a distributed Newton optimization approach to calculate power flows, allowing each line and node to independently update their values based on local information exchange, ensuring convergence within a predetermined threshold, thereby addressing the inefficiencies in current monitoring and management practices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed Newton optimization method is used for line flow computing, then computational efficiency and autonomy are improved, but system complexity increases
Solution Approach 1:
The patent divides the power system into independent nodes and lines, each performing local calculations. The global optimization problem is segmented into local sub-problems that can be solved independently using distributed Newton optimization, with each node/line computing its own contribution to the overall solution.
Solution Approach 2:
The patent transitions from centralized computing (single dimension) to distributed computing across multiple spatial dimensions. Each node and line operates in its own computational space, exchanging information through local communications, thereby adding a spatial distribution dimension to the optimization process.
2Productivity
If information sharing between utilities and control areas is implemented, then resource utilization efficiency is improved, but communication infrastructure complexity increases
Solution Approach 1:
The patent segments information sharing into local exchanges between adjacent nodes and lines rather than requiring global information sharing. Each component shares only the minimal necessary information with its immediate neighbors, reducing communication infrastructure requirements while maintaining resource utilization efficiency.
Solution Approach 2:
The patent implements local information sharing where each node and line communicates only with its direct neighbors. This local quality approach ensures that information exchange is tailored to specific local needs rather than requiring universal communication capabilities across the entire system.
3Adaptability or versatility
If decentralized decision-making is enabled in micro-grids, then autonomy and adaptability are improved, but coordination difficulty increases
Solution Approach 1:
The patent implements feedback mechanisms where each node and line receives information from neighbors about system state and adjusts its own operations accordingly. This continuous feedback loop enables autonomous decision-making at the local level while maintaining coordination through iterative information exchange that converges to a globally optimal solution.
Solution Approach 2:
The patent merges decentralized autonomous decisions with centralized coordination goals by formulating a unified optimization objective function that all nodes and lines work toward. Each local decision contributes to the global objective, combining the benefits of autonomy with the benefits of coordinated optimization.
Data Source
AI summary
The present disclosure relates to distributed line flow processing for a network having nodes with branches coupling adjacent ones of the nodes and components coupled to the nodes. In one embodiment, the disclosed process includes receiving an objective function having component variables, nodal output variables and branch flow variables for the network. Next, the component variables, nodal output variables and branch flow variables are initialized with initial values, and then values for the branch flow variables are calculated using a distributed Newton method. Finally, values for the component variables and the nodal output variables are calculated using values calculated for the branch flow variables until the values of the component variables, the nodal output variables, and the branch flow variables converge within a predetermined threshold range.