Voltage Stabilizer Control for Variable Distribution Loads
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Solution Overview
Problem
Existing electric power systems struggle to efficiently manage voltage levels in distribution circuits, particularly when dealing with high variation distributed generation and loads such as photovoltaic generation, distributed storage, and electric vehicle charging.
Innovation Solution
The implementation of a method that uses advanced metering infrastructure (AMI)-based data analysis to control secondary voltages directly, optimizing voltage levels to enhance the compatibility of the electrical delivery system with variable loads. This involves identifying common voltage connections, characterizing loads using linear models, and adjusting independent voltage control variables to maximize circuit responsiveness to load variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If voltage control is optimized for traditional loads, then energy efficiency is improved, but compatibility with high variation distributed generation and loads deteriorates
Solution Approach 1:
The system dynamically adjusts voltage control strategies based on real-time load characteristics. When high variation loads are detected, the system switches from energy efficiency optimization to load compatibility mode, adjusting voltage levels to accommodate the variable nature of distributed generation and loads like electric vehicles and photovoltaic systems.
Solution Approach 2:
The system changes control parameters based on load type. For traditional loads, voltage is optimized for energy efficiency. For high variation loads, the system modifies voltage control parameters to prioritize stability and compatibility, allowing voltage to fluctuate within acceptable ranges to accommodate the variable nature of these loads.
2Adaptability or versatility
If the system accommodates high variation loads, then adaptability is improved, but voltage stability deteriorates
Solution Approach 1:
The system performs preliminary identification of high variation loads using load profiles and historical data. Once identified, the system proactively adjusts voltage control parameters before significant voltage instability occurs, preparing the system to handle the variable nature of these loads while maintaining stability.
Solution Approach 2:
The system continuously monitors voltage levels and load characteristics, using feedback to adjust control strategies in real-time. When voltage instability is detected during high variation load operation, the system responds by adjusting voltage levels and control parameters to restore stability while maintaining compatibility with the variable loads.
3Device complexity
If traditional voltage control methods are used, then system complexity is minimized, but measurement precision and control reliability deteriorate
Solution Approach 1:
The system introduces an intermediary layer between traditional voltage control and the distribution network. This intermediary control system uses load identification algorithms and profile matching to bridge simple control mechanisms with the need for precise voltage measurement and control, enhancing reliability without requiring complete system redesign.
Data Source
AI summary
A method, apparatus, system and computer program is provided for controlling an electric power system, including implementation of a voltage control and conservation (VCC) system used to optimally control the independent voltage and capacitor banks using a linear optimization methodology to minimize the losses in the EEDCS and the EUS. An energy validation process system (EVP) is provided which is used to document the savings of the VCC and an EPP is used to optimize improvements to the EEDCS for continuously improving the energy losses in the EEDS. The EVP system measures the improvement in the EEDS a result of operating the VCC system in the “ON” state determining the level of energy conservation achieved by the VCC system. In addition the VCC system monitors pattern recognition events and compares them to the report-by-exception data to detect HVL events. If one is detected the VCC optimizes the capacity of the EEDS to respond to the HVL events by centering the piecewise linear solution maximizing the ability of the EDDS to absorb the HVL event. The VCC stabilizer function integrates voltage data from AMI meters and assess the state of the grid and initiates appropriate voltage control actions to hedge against predictable voltage risks.