Feeder Load Profile Analysis for EV Charging Grid Upgrades
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Solution Overview
Problem
Electric power grids face challenges in upgrading their capacity to support the high electrical load requirements of electric vehicle (EV) charging stations, leading to potential shortages and increased greenhouse gas emissions, as existing methods lack efficient and cost-effective solutions for determining necessary upgrades.
Innovation Solution
A process is developed to estimate existing load capacity along a power grid path, generate a feeder load profile, and identify necessary upgrades to achieve sufficient spare load capacity, allowing for accurate and cost-effective integration of EV charging stations by analyzing and simulating grid changes, including conductor type upgrades and component replacements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If the power grid capacity is increased to support EV charging stations, then the electrical load capacity is improved, but the cost of upgrades and system complexity increases
Solution Approach 1:
The patent segments the power grid into multiple feeders, each with its own load profile and capacity characteristics. By analyzing and upgrading individual feeders independently rather than the entire grid system, the complexity of the upgrade process is reduced while still achieving the goal of increasing overall electrical load capacity to support EV charging stations.
Solution Approach 2:
The system performs preliminary analysis of feeder load profiles and identifies required upgrades before actual EV charging station deployment. This advance planning allows for optimized upgrade paths that reduce overall system complexity and cost while ensuring adequate capacity is available when needed.
2Reliability
If traditional grid upgrade methods are used, then existing infrastructure is maintained, but the process is inefficient and costly
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors actual feeder load profiles and compares them against upgraded capacity. This feedback loop enables verification that upgrades are achieving desired results and allows for optimization of future upgrade decisions, improving overall upgrade efficiency while maintaining infrastructure reliability.
Solution Approach 2:
The system analyzes changes in load profile parameters (such as peak demand, load duration, and spatial distribution) to determine optimal upgrade strategies. By focusing on specific parameter improvements rather than blanket upgrades, the system achieves higher productivity and efficiency in the upgrade process while maintaining necessary infrastructure stability.
3Measurement precision
If comprehensive grid analysis is performed to identify optimal upgrades, then upgrade accuracy is improved, but computational time and resources increase
Solution Approach 1:
The patent divides the comprehensive grid analysis into smaller feeder-specific analyses. Each feeder is evaluated independently using standardized assessment criteria, which maintains high accuracy in identifying required upgrades while significantly reducing the total computational time and resources needed compared to analyzing the entire grid as a single system.
Data Source
AI summary
A system can obtain data that is sourced from an operator of a power grid and includes data items provided on a per-segment basis for feeders. The system can branch the data items into respective feeder groups and generate an ordered data structure based on an adjacency map for segments of a feeder group. The system can construct a feeder load profile based for a feeder on the ordered data structure. The feeder load profile depicts a load capacity for the feeder as a function of a distance from the substation. The system can then determine, based on the feeder load profile, that an available load capacity at the interconnection point is below a threshold required to support an electric vehicle (EV) charging station and identify upgrades based on variations in the feeder load profile to support the EV charging station.