Node Controllers for Radial Network Power Flow Optimization
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Solution Overview
Problem
Current power distribution networks face challenges in efficiently managing optimal power flow due to the complexity of radial networks and the increasing integration of distributed power generation, which requires advanced control methods to minimize power loss and optimize operational parameters.
Innovation Solution
The implementation of node controllers with a distributed power control application that uses an iterative process involving alternating direction method of multipliers (ADMM) and closed form solutions to calculate updated node operating parameters, enabling efficient power distribution and management in radial networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed power generation is integrated into the power distribution network, then power generation capacity and energy efficiency are improved, but network complexity and control difficulty increase
Solution Approach 1:
The patent segments the power distribution network into multiple zones with designated master nodes, dividing the complex control problem into smaller, manageable sub-problems. Each master node independently optimizes its zone using distributed algorithms, reducing overall network complexity while maintaining high power generation capacity from distributed sources.
Solution Approach 2:
The patent transforms the non-convex optimal power flow problem into a convex optimization problem by changing parameters through variable substitution and constraint relaxation. This parameter transformation enables efficient distributed control algorithms to converge to optimal solutions, managing network complexity while supporting distributed generation integration.
2Measurement precision
If traditional centralized control methods are used for optimal power flow, then computational accuracy is maintained, but computational time and latency increase
Solution Approach 1:
The patent divides the centralized control function into distributed control modules at multiple nodes throughout the network. Each module solves a local optimization problem using segmented variables, achieving near-optimal solutions with significantly reduced computational time compared to traditional centralized methods while maintaining sufficient accuracy for practical operation.
Solution Approach 2:
The patent performs preliminary convex relaxation and variable transformation to convert the non-convex optimal power flow problem into a convex form before distributed optimization. This preliminary action ensures that the distributed algorithm converges to accurate solutions while reducing computational iterations and latency.
3Measurement precision
If iterative optimization algorithms are applied to solve optimal power flow, then solution accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies parameter changes through convex relaxation techniques, transforming the non-convex optimal power flow equations into convex forms. This transformation maintains solution accuracy while enabling the use of efficient distributed convex optimization algorithms with lower computational complexity and faster convergence.
Solution Approach 2:
The patent creates local copies of the optimization problem at each master node, where each node solves an independent convex optimization sub-problem using local and exchanged information. This copying approach distributes computational complexity across multiple nodes while maintaining solution accuracy through coordinated optimization.
4Loss of energy
If real-time power flow optimization is implemented, then power loss minimization is achieved, but system responsiveness and adaptability requirements increase
Solution Approach 1:
The patent implements dynamic distributed optimization where master nodes continuously exchange information and update control variables in real-time. This dynamic approach enables the system to adapt to changing network conditions and distributed generation outputs while minimizing power loss through real-time optimization of power flow paths.
Solution Approach 2:
The patent incorporates feedback mechanisms where each master node receives power flow information from its zone, compares it with optimal targets, and adjusts control variables accordingly. This feedback loop enables real-time adaptation to network changes and distributed generation variations while continuously minimizing power loss.
Data Source
AI summary
Node controllers and power distribution networks in accordance with embodiments of the invention enable distributed power control. 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 and a set of at least one node selected from the group consisting of an ancestor node and at least one child node; wherein 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 the updated node operating parameters using the node operating parameters that describe the operating parameters of the node and the set of at least one node, where the iterative process involves evaluation of a closed form solution; and adjust node operating parameters.