AMI-Based Voltage Optimization Using Linear Regression
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Solution Overview
Problem
Current electric power systems face challenges in optimizing voltage and reducing energy demand effectively, as existing methods often require detailed loadflow models and are not efficient in identifying and addressing specific problems within the system.
Innovation Solution
The implementation of advanced metering infrastructure (AMI)-based data analysis for energy planning, which uses linear regression techniques to build simple models that predict voltage behavior and calculate energy savings without requiring detailed loadflow models, allowing for optimization of voltage ranges and identification of abnormal behavior in energy usage devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detailed loadflow models are used for voltage optimization and energy demand reduction, then measurement precision and reliability improve, but device complexity and loss of time increase
Solution Approach 1:
The patent extracts only the essential voltage measurement data from the complex power system using AMI meters, eliminating the need for detailed loadflow models. By taking out only the necessary measurement information (voltage at customer premises) and using simple regression analysis instead of comprehensive system modeling, the patent achieves accurate voltage optimization while dramatically reducing system complexity
Solution Approach 2:
The patent creates simplified copies of the power system behavior through linear regression models that replicate voltage-demand relationships without requiring actual detailed system models. These statistical copies capture the essential dynamics needed for optimization while avoiding the complexity of comprehensive loadflow analysis
2Measurement precision
If detailed loadflow models are used for energy planning, then projection accuracy improves, but loss of time and productivity decrease
Solution Approach 1:
The patent performs preliminary action by collecting and analyzing historical voltage and demand data in advance to establish regression models. These pre-built models enable rapid energy planning and projection without requiring time-consuming detailed loadflow analysis during the actual planning process, thus maintaining accuracy while reducing time loss
Solution Approach 2:
The patent uses statistical copies (regression models) to represent complex power system behavior, enabling fast energy planning projections. These simplified models capture the essential relationships between voltage and energy demand, providing accurate predictions without the computational burden of detailed loadflow models
3Device complexity
If AMI-based data analysis with linear regression is used, then device complexity and loss of time are reduced, but measurement precision and reliability may worsen
Solution Approach 1:
The patent applies self-service by using the existing AMI measurement infrastructure to provide its own data for regression analysis. The system uses the data already being collected by AMI meters for billing and monitoring purposes, eliminating the need for additional complex measurement systems while maintaining prediction accuracy through statistical modeling
Data Source
AI summary
A method, apparatus, system and computer program is provided for controlling an electric power system, including implementation of an energy planning process (EPP) system which can be used to plan a voltage control and conservation (VCC) system applied to an electrical distribution connection system (EEDCS). The EPP system plans modifications to the EEDCS as a result of operating the VCC system in the “ON” state, in order to maximize the level of energy conservation achieved by the VCC system control of the EEDCS. The EPP system may also identify potential problems in the EEDCS for correction.