Uncertainty Effects on Smart Grid Services for Low-Voltage Distribution Networks.

Area of Science:

• Electrical Engineering
• Power Systems
• Smart Grids

Background:

• Distribution system operators face challenges managing uncertainties from distributed renewable generation (DG) and demand response.
• Active management strategies require robust monitoring infrastructure to ensure grid stability.

Purpose of the Study:

• To investigate how sensor penetration and measurement accuracy impact voltage regulation and congestion management in distribution networks.
• To evaluate the effectiveness of a smart grid service framework utilizing a genetic algorithm for optimizing flexibility resources.

Main Methods:

• A smart grid service framework was developed using a genetic algorithm to optimize flexibility resources.
• The framework was tested on the IEEE European Low Voltage Test Feeder under various monitoring scenarios.
• Scenarios varied sensor penetration levels and measurement accuracy (accuracy classes 2% and 0.5%).

Main Results:

• Increasing sensor penetration from 0% to 100% dramatically improved congestion management, reducing residual congestion cases from 46.2% to over 91.9% (with 2% accuracy sensors).
• Full monitoring with 0.5% accuracy sensors further enhanced congestion management, achieving 97.9% effectiveness.
• Voltage violations were completely eliminated under full monitoring conditions.

Conclusions:

• Sensor penetration is the dominant factor influencing the effectiveness of voltage regulation and congestion management.
• Widespread sensor deployment with appropriate measurement accuracy is fundamental for reliable and efficient smart grid operation.
• The study highlights the necessity of advanced monitoring infrastructure for integrating renewable energy sources and demand response.