Power Flow Optimization Using Incomplete Dimensionality Augmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing power flow constraint methods face challenges in accurately modeling medium and low voltage distribution networks due to non-convex nonlinear characteristics, especially under heavy load and high distributed power supply penetration, leading to reduced accuracy and limited adaptability in optimization solutions.
Innovation Solution
The method divides power flow independent variables into control and disturbance variables, where only the disturbance variable is subjected to dimensionality augmentation, allowing the system to adapt to nonlinear characteristics while maintaining the control variable's linearized expression, thereby optimizing power flow constraints with higher accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a linearized power flow model is used, then the optimization solution is easy to obtain, but the accuracy is reduced under heavy load and high distributed power supply penetration
Solution Approach 1:
The patent applies dimensionality augmentation to disturbance variables, mapping them from a low-dimensional space to a high-dimensional space through a nonlinear mapping function. This allows the model to capture nonlinear characteristics of power flow under heavy load and high distributed power supply penetration while maintaining a linearized structure for control variables, thereby resolving the contradiction between computational ease and accuracy.
2Measurement precision
If a nonlinear power flow model is used, then the accuracy is improved, but the optimization solution becomes difficult to obtain
Solution Approach 1:
The patent segments power flow variables into control variables and disturbance variables. Control variables remain in the original space to maintain linearity and ease of optimization, while disturbance variables are transformed into a higher-dimensional space to capture nonlinear characteristics. This segmentation allows the model to achieve high accuracy without sacrificing computational tractability.
3Adaptability or versatility
If complete dimensionality augmentation is applied to all variables, then the adaptability to nonlinear characteristics is improved, but the complexity of the optimization model increases
Solution Approach 1:
The patent applies dimensionality augmentation selectively only to disturbance variables rather than all variables. This local application of the transformation captures nonlinear characteristics where they matter most (in disturbance responses) while keeping the control variable relationships linear and computationally simple, thereby resolving the contradiction between adaptability and model complexity.
Data Source
AI summary
Disclosed is an incomplete dimensionality augmentation-based optimization method for a data-driven power system. By dividing a power flow independent variable into a control variable and a disturbance variable, such as power of a controllable power supply and an uncontrolled voltage amplitude, only the disturbance variable is subjected to dimensionality augmentation, to adapt to the nonlinear characteristic of the power flow; and the control variable keeps a power flow constraint as a linearized expression of the control variable, thereby simplifying a power flow constraint form and solution, and achieving a higher-accuracy of optimization. The power optimization scheduling of distributed photovoltaic can be implemented by the optimization method provided in the present invention.