Wind Power Dispatching with Spatio-Temporal Uncertainty Sets
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Solution Overview
Problem
Current optimal dispatching methods for wind power generation and energy storage combined systems face challenges in accurately handling the uncertainty of wind turbine output, leading to inefficient calculations and reduced credibility due to high dimensionality and non-linearity, as well as the need for large scenario sets in stochastic optimization methods.
Innovation Solution
The method constructs a wind power uncertainty set based on spatio-temporal coupling of wind turbine output, linearizes the optimal dispatching model, and uses a column-and-constraint generation (C&CG) algorithm to determine an optimal dispatching plan, effectively reducing conservativeness and improving solution efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If stochastic optimization method with scenario sampling is used to handle wind power uncertainty, then the uncertainty can be transformed into a deterministic problem, but the calculation scale becomes very large and calculation duration increases significantly
Solution Approach 1:
The patent extracts and separates the uncertainty handling from the main optimization problem by using a two-stage robust optimization framework. The first stage determines dispatching decisions, while the second stage validates against uncertainty scenarios, thereby extracting the computational burden from the main problem and improving overall calculation efficiency.
Solution Approach 2:
The patent segments the complex optimization problem into multiple manageable components: uncertainty set construction, two-stage robust optimization model formulation, and iterative solution process. This segmentation allows each component to be handled separately and efficiently, reducing the overall computational complexity.
2Productivity
If scenario reduction technology is used to decrease the number of scenarios, then calculation scale is reduced, but the scenario constraint becomes subjective and model accuracy decreases
Solution Approach 1:
The patent changes the fundamental parameter of uncertainty representation from discrete scenario probabilities to continuous uncertainty sets with spatio-temporal coupling constraints. This parameter change allows the model to maintain accuracy without relying on subjective scenario selection, as the uncertainty is characterized through mathematical constraints rather than discrete samples.
Solution Approach 2:
The patent performs preliminary characterization of wind power uncertainty by constructing spatio-temporal coupling uncertainty sets before the optimization process. This preliminary action captures the essential statistical properties and correlation structures of wind power, ensuring that the subsequent optimization maintains high accuracy without requiring extensive scenario sampling.
3Adaptability or versatility
If the optimal dispatching model is formulated with high-dimensional and nonlinear programming to accurately represent the system, then the model can capture complex system behavior, but the solution efficiency decreases due to inability to directly solve
Solution Approach 1:
The patent substitutes the traditional mechanical nonlinear programming solution approach with an iterative algorithmic approach. By using iterative optimization methods, the patent can handle the high-dimensional and nonlinear nature of the dispatching model while maintaining solution efficiency, as the iterative process converges to optimal solutions without requiring direct solution of complex nonlinear equations.
Solution Approach 2:
The patent introduces dynamics into the solution process by using an iterative optimization framework that adaptively adjusts the search direction and step size. This dynamic approach allows the model to efficiently navigate the high-dimensional nonlinear solution space, capturing complex system behavior while maintaining computational efficiency through adaptive convergence control.
Data Source
AI summary
An optimal dispatching method and system for a wind power generation and energy storage combined system are provided. Uncertainty of a wind turbine output is characterized based on spatio-temporal coupling of the wind turbine output and an interval uncertainty set. Compared with a traditional symmetric interval uncertainty set, the uncertainty set that considers spatio-temporal effects effectively excludes some extreme scenarios with a very small probability of occurrence and reduces conservativeness of a model. A two-stage robust optimal dispatching model for the wind power generation and energy storage combined system is constructed, and a linearization technology and a nested column-and-constraint generation (C&CG) strategy are used to efficiently solve the model.