Probabilistic Wind Power Forecasting for Operating Reserve Quantification
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Solution Overview
Problem
Traditional deterministic methods struggle to accurately quantify operating reserves for power systems with high wind power penetration, as they fail to adapt to the uncertainty and variability caused by intermittent wind power sources, leading to challenges in maintaining energy balance and reducing operational costs.
Innovation Solution
An operating reserve quantification method using probabilistic wind power forecasting, which employs extreme learning machines to generate non-parametric prediction intervals and determines reserve requirements based on these intervals, incorporating reserve provision cost and deficit penalties as a loss function to optimize reserve decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deterministic methods are used to determine operating reserves, then the reserve calculation is simple and deterministic, but it cannot accurately adapt to the uncertainty and variability of wind power output
Solution Approach 1:
The patent transforms the deterministic reserve determination into a probabilistic framework by changing the parameter representation from fixed values to prediction intervals with confidence levels. The extreme learning machine outputs non-parametric prediction intervals that capture the uncertainty of wind power output, allowing operators to determine reserves based on desired reliability levels rather than fixed deterministic calculations.
Solution Approach 2:
The patent replaces traditional deterministic calculation methods with a machine learning-based probabilistic forecasting system. The extreme learning machine substitutes conventional statistical or mechanical approaches, providing adaptive prediction intervals that automatically adjust to wind power variability without requiring complex manual calibration.
2Reliability
If adequate operating reserves are maintained to compensate for wind power prediction errors, then system reliability is improved, but operational costs increase
Solution Approach 1:
The patent introduces dynamic reserve determination through probabilistic prediction intervals. Instead of fixed deterministic reserves, the required reserve capacity becomes a dynamic variable that adjusts based on the predicted uncertainty of wind power output. The extreme learning machine provides time-varying prediction intervals that reflect changing wind conditions, allowing operational reserves to be optimized dynamically rather than statically.
Solution Approach 2:
The patent changes the parameter representation of reserves from fixed deterministic values to probabilistic intervals with configurable confidence levels. By adjusting the confidence level parameter, operators can trade off between reliability and cost - higher confidence levels provide greater reliability but require larger reserves, while lower confidence levels reduce costs but decrease reliability. This parametric approach enables flexible optimization of the reliability-cost tradeoff.
Data Source
AI summary
The present invention discloses an operating reserve quantification method for power systems using probabilistic wind power forecasting and belongs to the field of power system operation optimization. This method constructs an operating reserve optimization model of power systems using probabilistic wind power forecasting, which utilizes extreme learning machine to output non-parametric prediction intervals of wind power and determines the positive and negative operating reserve requirements of the system by upper and lower boundaries of the prediction intervals. The cost-benefit trade-offs of reserve decision are realized by taking reserve provision cost and deficit penalty as a loss function of machine learning. The resultant reserve decision can effectively reduce system operation cost on the premise of ensuring good reliability. The present invention transforms complicated machine learning model into a mixed integer linear programming problem, which can be efficiently solved after implementing a feasible region tightening method.