Distributed Optimal Power Flow for Unbalanced Radial Networks
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Solution Overview
Problem
Current power grid systems face challenges in efficiently managing optimal power flow, especially in unbalanced networks, due to the complexity of distributed power generation and consumption patterns, which require advanced control methods to minimize power loss and optimize operational parameters.
Innovation Solution
The implementation of node controllers with distributed power control applications that utilize an iterative process, including alternating direction method of multipliers (ADMM) and convex relaxation, to calculate updated operating parameters for nodes in an unbalanced radial network, enabling efficient power distribution and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If distributed power control is implemented in unbalanced networks, then adaptability and real-time optimization improve, but computational complexity and processing time increase
Solution Approach 1:
The patent segments the distributed power control problem into localized subproblems that can be solved independently at each node using ADMM. Each node controller solves its own optimization subproblem using only local information and messages from neighboring nodes, rather than solving one large global optimization problem. This segmentation reduces computational complexity while maintaining adaptability.
Solution Approach 2:
The patent applies local quality by enabling each node to perform distributed optimization using only local network conditions and parameters. Each node controller makes decisions based on local measurements and exchanges limited information with neighbors, rather than requiring global network state information. This local approach reduces computational burden while preserving adaptability to local conditions.
2Measurement precision
If iterative optimization processes are used for optimal power flow, then solution accuracy improves, but computational time and latency increase
Solution Approach 1:
The patent applies partial action by implementing convex relaxation that solves a simplified version of the optimal power flow problem. The ADMM algorithm with convex relaxation provides a sufficiently accurate solution for practical purposes without requiring the full complexity of exact nonlinear optimization. This partial solution approach achieves acceptable accuracy while dramatically reducing computational time and latency for real-time operation.
3Productivity
If centralized control methods are used for power flow optimization, then computational efficiency improves, but system scalability and distributed adaptability worsen
Solution Approach 1:
The patent implements self-service by enabling each node controller to autonomously solve its own optimization subproblem using local information and messages from neighbors. Each node performs distributed ADMM iterations independently, making the system self-organizing and scalable. This eliminates the need for a centralized controller while maintaining computational efficiency through parallel processing at distributed nodes.
Data Source
AI summary
Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control on an unbalanced network. One embodiment includes a node controller including a distributed power control application; a plurality of node operating parameters describing the operating parameter of a node in an unbalanced network; wherein the processor is configured by the distributed power control application to: send node operating parameters to nodes in the set of at least one node; receive operating parameters from the nodes in the set of at least one node; calculate a plurality of updated node operating parameters using an iterative process to determine updated node operating parameters using the node operating parameters that describe the operating parameters of the node, and the operating parameters of the set of at least one node, where each iteration in the iterative process involves evaluation of a subproblem; and adjust node operating parameters.