Local Volt/Var Control for Stable DER Voltage Regulation
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Solution Overview
Problem
Power distribution networks face challenges in maintaining system stability and power quality due to uncoordinated power injections and sudden changes in distributed energy resources (DERs), which can lead to voltage regulation issues.
Innovation Solution
A device and method that utilize a machine learning network to calculate a reactive power setpoint for DERs based on local voltage values, allowing for controlled reactive power output to regulate voltage in the network, independent of other DERs, and trained using historical data and cost functions to ensure stability and optimal power flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If DERs operate independently without coordination, then each DER can autonomously regulate local voltage, but system stability deteriorates due to uncoordinated power injections and sudden generation changes
Solution Approach 1:
The patent implements feedback control by training neural networks to map local voltage measurements to reactive power setpoints. Each DER continuously monitors its local voltage and adjusts its reactive power output based on the learned relationship, creating a closed-loop control system that maintains voltage within acceptable ranges while ensuring system-wide stability through the stabilizing effect of the learned equilibrium function.
2Reliability
If complex coordination mechanisms are implemented across multiple DERs, then system stability improves, but device complexity and communication requirements increase
Solution Approach 1:
The patent divides the complex problem of coordinated DER control into independent local control units. Each DER is equipped with its own neural network that operates autonomously using only local voltage measurements. This segmentation eliminates the need for complex inter-DER communication and coordination mechanisms while maintaining system stability through the collective stabilizing effect of individually trained controllers.
Solution Approach 2:
Each DER performs self-service by autonomously determining its own reactive power setpoint based on local voltage conditions. The neural network at each DER independently maps local voltage measurements to appropriate reactive power adjustments without requiring information from other DERs or centralized coordination, thereby simplifying the overall control system architecture.
3Reliability
If reactive power is adjusted to regulate voltage, then voltage stability improves, but line losses may increase due to additional reactive power flow
Solution Approach 1:
The patent changes the control parameter from direct reactive power injection to reactive power setpoint adjustment based on local voltage. The neural network learns the optimal reactive power setpoint that achieves voltage regulation while minimizing unnecessary reactive power flow. This parameter transformation allows the system to achieve voltage stability with reduced energy losses by operating at optimized operating points.
Data Source
AI summary
A device may calculate a reactive power setpoint associated with a distributed energy resource (DER) electrically coupled to a power distribution network, based on a local voltage value associated with the DER. The device may control a reactive power output of the DER in association with regulating voltage at the power distribution network, based on the reactive power setpoint.