Dynamic Load Model for Power Distribution Prediction
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Solution Overview
Problem
Current load models in electric power distribution systems fail to accurately predict variations in demand caused by changes in supply voltage, leading to inefficiencies in managing and controlling power distribution.
Innovation Solution
Development of systems and methods to create and refine load models using data from various sources within the power distribution system, including voltage and current measurements, to identify and simulate the behavior of different load types, such as constant impedance, constant current, and constant power loads, allowing for optimized control strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional load models are used in electric power distribution systems, then the system operation is simple, but the prediction accuracy of power consumption varies with voltage changes is poor
Solution Approach 1:
The patent applies dynamics by transitioning from static load models to dynamic load models that adapt to changing voltage conditions. The system continuously updates load model parameters based on real-time voltage measurements and historical data, allowing the model to dynamically reflect the actual power consumption characteristics under varying voltage conditions, thereby improving prediction accuracy without requiring complete model redesign.
Solution Approach 2:
The patent implements parameter changes by adjusting load model parameters (such as power factor, impedance, and demand coefficients) based on measured voltage levels and their correlation with power consumption. The system identifies optimal parameter sets that correspond to different voltage conditions and applies these parameters to improve the accuracy of power consumption predictions across varying operating conditions.
2Measurement precision
If load model parameters are frequently updated to improve accuracy, then the prediction precision improves, but the computational time and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing load model parameters for various voltage conditions based on historical data analysis. When voltage changes occur, the system quickly retrieves and applies the pre-determined parameter set corresponding to the new voltage level, avoiding the need for time-consuming real-time calculations while maintaining high prediction accuracy.
Solution Approach 2:
The patent implements partial action by updating only the specific load model parameters that are most sensitive to voltage changes, rather than recalculating the entire model. The system identifies key parameters (such as those with high correlation coefficients to voltage) and updates only these parameters, reducing computational burden while maintaining overall model accuracy.
3Measurement precision
If detailed measurements and analysis are performed to improve load modeling, then the model accuracy improves, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent applies the extraction principle by isolating and focusing on the most influential factors affecting power consumption, such as voltage magnitude and its correlation with load behavior. The system extracts key parameters from extensive measurement data and uses only these critical factors for modeling, rather than processing all available data, thereby reducing system complexity while maintaining modeling accuracy.
Solution Approach 2:
The patent implements segmentation by dividing the load into different categories or components (such as constant power, constant impedance, and constant current loads) and modeling each segment separately based on its specific voltage-response characteristics. This segmented approach simplifies the overall modeling process by allowing independent analysis of different load types while capturing their individual behaviors accurately.
Data Source
AI summary
Disclosed are systems and methods for calculating load models and associated tunable parameters that may be used to describe the behavior of loads connected to an electric power distribution system. The load models may be utilized to predict variations in demand caused by changes in the supply voltage, and may be utilized in determining an optimized control strategy based on load dynamics. Any action which causes a disruption to the electric power distribution system may provide information regarding the composition or dynamics of connected loads. Such actions may be referred to as modeling events. Modeling events may occur with some frequency in electric power distribution systems, and accordingly, a number of data sets may be acquired under a variety of conditions and at a variety of times. Load models may include static load models, dynamic load models, or a combination of static and dynamic load models.