Micro-grid Frequency Control via Quasi-Newton Recursion
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Solution Overview
Problem
Micro-grids with increasing wind and photovoltaic power generation face challenges in frequency stability due to fluctuations, intermittency, and low inertia, requiring effective frequency control to ensure safety, especially when operating in isolation.
Innovation Solution
A computer-implemented frequency control method for micro-grids that determines a cost increment rate of distributed energy resources, adjusts active power based on frequency differences and communication coefficients, using quasi-Newton recursion to optimize power adjustments while adhering to constraints, and employing a peer-to-peer communication architecture to avoid centralized control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed generators based on renewable energy are grid-connected to promote micro-grid development, then energy structure optimization and emissions reduction are achieved, but frequency stability deteriorates due to fluctuations, intermittency, and low inertia
Solution Approach 1:
The patent dynamically adjusts control parameters including damping coefficient, proportional coefficient, and integration coefficient based on system state. The controller modifies these parameters in real-time to adapt to varying renewable energy conditions while maintaining frequency stability, resolving the contradiction between reliability and adaptability.
Solution Approach 2:
The patent implements a closed-loop feedback control system that continuously monitors frequency deviations and adjusts the output of distributed generators accordingly. The feedback mechanism uses frequency difference signals to modify control actions, ensuring stable operation despite the intermittent nature of renewable energy sources.
2Reliability
If conventional frequency control methods are used in micro-grids with low inertia, then control simplicity is maintained, but dynamic performance deteriorates under rapid load changes and frequency fluctuations
Solution Approach 1:
The patent employs a dynamic controller that adapts its characteristics based on real-time system conditions. The controller adjusts its response dynamically rather than using fixed parameters, improving dynamic performance under rapid load changes while maintaining manageable complexity through structured adaptation rules.
Solution Approach 2:
The patent implements predictive elements in the control algorithm that anticipate frequency deviations before they occur. By preparing control actions in advance based on predicted trends, the system improves dynamic response without requiring overly complex real-time computation during critical moments.
3Reliability
If centralized control architecture is used for frequency control, then coordination is simplified, but system reliability deteriorates due to single-point failure risk
Solution Approach 1:
The patent divides the control function into distributed segments across multiple controllers rather than using a single centralized controller. Each controller operates semi-independently, eliminating single-point failure risks while maintaining coordination through standardized communication protocols, thus improving reliability without excessive complexity.
Solution Approach 2:
The patent introduces a communication intermediary layer that facilitates coordination between distributed controllers without requiring direct peer-to-peer connections. This intermediary structure simplifies the communication architecture while enabling reliable distributed operation, resolving the contradiction between reliability and complexity.
Data Source
AI summary
The present disclosure provides a frequency control method for a micro-grid and a control device. The method includes: determining a middle parameter at iteration k; determining a local gradient parameter at iteration k according to the cost increment rate at iteration k, the frequency difference between iterations k and k+1, and communication coefficients; performing a quasi-Newton recursion according to the middle parameter and local gradient parameter to acquire a recursion value; determining the cost increment rate at iteration k+1 according to the recursion value; determining an adjustment value of an active power according to the cost increment rate at iteration k+1; adjusting the active power according to the adjustment value if the adjustment value satisfies a constraint condition and judging whether the difference is smaller than a predetermined threshold; executing k=k+1 if yes and stopping the frequency control if no.