Power Dispatching Model Reconstruction for Faster Robust Allocation
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Solution Overview
Problem
Existing dual solution methods for power dispatching suffer from slow solution speeds, large approximate errors, and difficulty in measurement, leading to insufficient accuracy and instability in power system dispatch.
Innovation Solution
A method is introduced that transforms the two-stage distributed robust dispatching model into a finite-dimensional problem using adaptive multi-faceted approximation, allowing for rapid solution and improved accuracy in resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing dual solution methods are used for power dispatching, then the solution process is relatively simple, but the solution speed is slow and the approximate error is large
Solution Approach 1:
The patent segments the complex power dispatching problem into a two-stage distributed robust optimization model, where the first stage determines preliminary decisions and the second stage performs decision adjustments under uncertainty. This segmentation allows for more precise handling of each stage's specific characteristics, improving both solution speed and accuracy by avoiding the need to solve the entire problem as a single complex optimization task.
Solution Approach 2:
The patent transforms the original infinite-dimensional robust optimization problem into a finite-dimensional problem by introducing a discrete approximation of the uncertainty set. This dimensionality change converts the continuous infinite-dimensional decision variables into discrete finite-dimensional variables, enabling the use of efficient numerical optimization algorithms while maintaining solution accuracy.
2Measurement precision
If existing dual solution methods are used for power dispatching, then the computational complexity is manageable, but the measurement and accuracy of solution results are insufficient
Solution Approach 1:
The patent changes the parameter representation by transforming the infinite-dimensional functional variables into finite-dimensional vectors through discrete approximation of the uncertainty set. This parameter transformation maintains the essential characteristics of the robust optimization problem while enabling precise numerical solution methods, thereby improving solution accuracy without excessively increasing computational complexity.
3Productivity
If the two-stage distributed robust model is solved directly, then the theoretical optimality is maintained, but the solution speed is slow due to infinite-dimensional variables
Solution Approach 1:
The patent segments the infinite-dimensional optimization problem into finite-dimensional subproblems by discretizing the uncertainty set. This segmentation maintains the theoretical optimality of the two-stage robust optimization framework while enabling fast numerical solution through standard optimization algorithms, thus resolving the contradiction between solution speed and accuracy.
Solution Approach 2:
The patent creates a finite-dimensional copy of the original infinite-dimensional problem by discretizing the functional space. This copied finite-dimensional model preserves the essential structure and optimality properties of the original problem while being computationally tractable, allowing rapid solution without sacrificing reliability.
Data Source
AI summary
A method of optimizing resource allocation based on adaptive multi-faceted cutting of a power dispatching model is provided. The method includes constructing and analyzing a two-stage distributed robust model by using acquired operation state information of a power system and identifying therefrom a second-stage infinite-dimensional decision variable. A model is reconstructed by dimensionality reduction and a dispatching model is solved to obtain a dispatching strategy scheme for realizing optimal resource allocation. The two-stage distributed robust dispatching model is transformed into a finite-dimensional problem, so that rapid solution is realized. By improving the solving efficiency and the accuracy of the power dispatching model, the working efficiency of the power system is obviously improved.