Predictive Grid Control for Microgrid Stability
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Solution Overview
Problem
Conventional droop control techniques in microgrids fail to address stability and reliability issues associated with high penetration of intermittently available renewable sources, often requiring oversizing of dispatchable resources, which is inefficient and increases atmospheric emissions.
Innovation Solution
A three-dimensional droop control technique dynamically updated using a grid model and forecast information, which generates coefficients for control functions relating real and reactive power to voltage frequency and magnitude, enabling compensation for coupling effects between these parameters and optimizing asset control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional droop control techniques are used, then the control scheme is simple, but stability and reliability deteriorate with high penetration of intermittently available renewable sources
Solution Approach 1:
The patent implements dynamic droop control where control parameters are continuously adjusted based on real-time predictions of renewable generation and load demand. The system transitions from static conventional droop control to a dynamic adaptive control scheme that responds to changing grid conditions, thereby maintaining stability and reliability under high renewable penetration.
Solution Approach 2:
The system performs preliminary actions by generating predictions of renewable generation and load demand before control decisions are made. These predictive models provide advance information about grid conditions, allowing the control system to proactively adjust droop parameters to prevent stability issues rather than reacting to problems after they occur.
2Reliability
If dispatchable resources are oversized to deal with power fluctuations, then reliability improves, but energy efficiency deteriorates and atmospheric emissions increase
Solution Approach 1:
The patent changes the operational parameters of dispatchable resources by using predictive droop control to optimize their output based on forecasted renewable generation and load conditions. Instead of operating at fixed oversized capacities, resources dynamically adjust their parameters (power output, droop coefficients) to match actual needs, reducing energy waste and emissions while maintaining reliability.
Solution Approach 2:
The system implements feedback mechanisms where predictions of renewable generation and load demand are continuously fed into the control algorithm. This feedback loop allows dispatchable resources to receive real-time information about grid conditions and adjust their operation accordingly, avoiding the need to run at oversized capacities and thereby improving energy efficiency and reducing emissions.
3Reliability
If dispatchable resources are oversized to deal with power fluctuations, then reliability improves, but atmospheric emissions increase
Solution Approach 1:
The patent changes the operational parameters of dispatchable resources by using predictive droop control to optimize their output based on forecasted renewable generation and load conditions. Instead of operating at fixed oversized capacities, resources dynamically adjust their parameters (power output, droop coefficients) to match actual needs, reducing energy waste and emissions while maintaining reliability.
4Adaptability or versatility
If conventional droop control is used, then device complexity is low, but adaptability to intermittent renewable sources deteriorates
Solution Approach 1:
The patent implements dynamic droop control where control parameters are continuously adjusted based on real-time predictions of renewable generation and load demand. The system transitions from static conventional droop control to a dynamic adaptive control scheme that responds to changing grid conditions, thereby maintaining stability and reliability under high renewable penetration.
Solution Approach 2:
The system performs preliminary actions by generating predictions of renewable generation and load demand before control decisions are made. These predictive models provide advance information about grid conditions, allowing the control system to proactively adjust droop parameters to prevent stability issues rather than reacting to problems after they occur.
Data Source
AI summary
A prediction of an electric power consumption and/or a generation of at least one of a load and a source coupled to an electric power grid is generated responsive to a forecast of an event. A desired state of an asset on the grid is identified responsive to the prediction. A functional representation of a control scheme for the asset is identified based on a sensitivity of the control parameter to a variance in operation of the electric power grid with respect to the prediction.