Feeder Hosting Capacity Optimization for Renewable Grid Constraints
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Solution Overview
Problem
Current feeder hosting capacity (FHC) technologies are not optimized, limiting the accommodation of renewable energy due to voltage rise, thermal overloading, protection malfunctions, and power quality issues, and do not account for individual feeder characteristics or locational impact.
Innovation Solution
A swarm optimization-based intelligent scenario selection method using local and global search experiences to calculate local and global max voltage nodes, solving unbalance load flow, short circuit, and harmonics analysis for improved FHC.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If renewable energy penetration is increased in existing feeders, then green energy system sustainability is improved, but voltage rise, thermal overloading, and protection malfunctions occur
Solution Approach 1:
The patent implements dynamic feeder reconfiguration by allowing the feeder topology to change adaptively based on renewable energy penetration levels and system conditions. The system dynamically adjusts the network configuration to optimize power flow patterns, preventing voltage rise and thermal overloading while maximizing renewable energy accommodation capacity.
Solution Approach 2:
The patent changes key operating parameters including voltage limits, thermal limits, and protection settings to accommodate higher renewable energy penetration. By adjusting these parameters dynamically based on system conditions and renewable generation levels, the system maintains reliability while increasing green energy integration capacity.
2Measurement precision
If conventional FHC calculation methods are used, then calculation simplicity is maintained, but calculation accuracy and optimization are insufficient
Solution Approach 1:
The patent incorporates feedback mechanisms where FHC calculation results are used to update and refine subsequent calculations. The system continuously monitors system conditions and uses this feedback to adjust calculation parameters, improving accuracy while managing complexity through iterative optimization rather than overly complex one-shot calculations.
Solution Approach 2:
The patent performs preliminary FHC calculations and optimization before actual renewable energy integration decisions are made. By conducting advance simulations and calculations with various scenarios, the system determines optimal hosting capacity levels and reconfiguration strategies, avoiding the need for complex real-time calculations during operation.
3Adaptability or versatility
If uniform PV penetration threshold is applied to all feeders, then implementation simplicity is maintained, but locational impact and individual feeder characteristics are not considered
Solution Approach 1:
The patent applies local quality by tailoring FHC analysis and reconfiguration strategies to each individual feeder's characteristics, including its topology, load profile, and existing infrastructure. Instead of uniform thresholds, the system determines location-specific penetration limits and optimization approaches that account for local conditions, maximizing renewable integration capacity for each feeder uniquely.
Data Source
AI summary
Provided are embodiments of systems, devices and methods for improved optimization of FHC using a swarm optimization based intelligent scenario selection from local search (small step) and global search (large step) experiences for faster and better FHC.