Energy Contour Forecasting Optimization with Smart Metering in Distribution Power Networks.

Area of Science:

• Electrical Engineering
• Power Systems Analysis
• Data Science

Background:

• Smart metering systems are crucial for modernizing power distribution networks and transitioning to smart grids.
• Beyond basic energy consumption, smart meters offer potential for enhanced grid management functions.
• Accurate energy demand forecasting is essential for efficient grid operation, cost reduction, and integration of renewables.

Purpose of the Study:

• To propose a novel method for estimating energy contours at the distribution level using smart metering data.
• To improve electricity supply quality, reduce operational costs, and enhance measurement and billing accuracy.
• To enable better long-term energy production and acquisition planning, including optimal capacity planning and real-time demand adaptation.

Main Methods:

• Utilizing a frequency feature-based method on time-series data from smart metering systems.
• Estimating the energy contour at the distribution level.
• Validating the methodology through a case study using real-world data from a European power grid.

Main Results:

• The proposed method accurately forecasts energy contours, crucial for optimal energy flow and grid management.
• The approach demonstrated a common error of 1%, with a maximum error of 15% during exceptional events.
• The study confirms the feasibility of precise energy demand prediction for improved grid planning and operation.

Conclusions:

• Smart metering data, analyzed via frequency features, provides a powerful tool for energy contour estimation and demand forecasting.
• This approach significantly contributes to optimizing energy production, acquisition, and capacity planning in power distribution networks.
• The findings support the integration of renewable energy sources by enabling better adaptation to demand fluctuations and improving overall grid stability.