Wind Solar Power Cluster Output Description Using Time-Varying Period Division
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Solution Overview
Problem
Current methods for describing wind and solar power output lack accuracy in capturing time-varying characteristics, leading to instability and inefficiency in power system dispatching, especially as grid-connected scales increase, due to the inherent intermittency of these energy sources.
Innovation Solution
A method that divides the daily output process of wind and solar power clusters using an output error function as an evaluation criterion, employing hierarchical clustering for optimal time division and kernel density estimation to establish multiple probability distributions for each period, ensuring consistent output characteristics within periods and determining the optimal number of time slots based on economic efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single probability distribution function is used to describe wind and solar power output, then the model is simple, but it cannot accurately capture time-varying characteristics leading to poor reliability and concentration
Solution Approach 1:
The patent divides the daily output process of wind and solar power clusters into multiple time periods based on time-varying characteristics. Each period has its own probability distribution function, allowing accurate capture of different output patterns at different times while maintaining manageable complexity through structured segmentation.
Solution Approach 2:
The patent transitions from a static single probability distribution model to a dynamic multi-period model that adapts to changing output characteristics throughout the day. The model dynamically adjusts probability distributions based on the specific time period, capturing the inherent time-varying nature of wind and solar power generation.
2Measurement precision
If the number of time slots is increased to improve accuracy, then time-varying characteristics are better captured, but computational complexity and subjective determination increase
Solution Approach 1:
The patent uses clustering algorithms to objectively determine the optimal number of time periods and their boundaries by analyzing actual power output data. This transforms the subjective decision of how many time slots to use into an objective parameter optimization problem, where the number of periods is automatically determined based on data characteristics rather than arbitrary selection.
Solution Approach 2:
The patent employs iterative clustering algorithms that use feedback from the power output data itself to determine the optimal time period division. The algorithm continuously refines the number and boundaries of time periods based on how well they capture the underlying patterns in the data, eliminating subjective determination while achieving optimal accuracy.
3Reliability
If traditional scenario simulation or uncertainty aggregation methods are used, then existing frameworks are maintained, but they fail to adequately address time-varying uncertainty at multiple time scales
Solution Approach 1:
The patent introduces a time period dimension to the traditional uncertainty modeling framework. By dividing the day into multiple periods and establishing separate probability distributions for each, the model captures uncertainty variations across different time scales, transforming a single-dimension uncertainty model into a multi-dimensional one that reflects the true time-varying nature of renewable power generation.
Data Source
AI summary
A method for describing power output of a cluster of wind and solar power stations considering time-varying characteristics. The error function is employed to characterize the degree of difference in power output within periods, and split-level clustering is used to determine the optimal period division under different period division quantities. The economic efficiency theory is introduced to determine the ideal number of periods, avoiding the randomness and unreasonableness that may result from relying on the subjective determination of the number of clusters. This method can reasonably divide the wind and solar power output period, fully reflecting the time-varying law of wind and solar power generation. The results also can accurately reflect the distribution characteristics of the power output of the power station group at each time period, and the power output each time period shows better reliability, concentration, and practicality.