Robust modeling and evidence-based evaluation method for a active distribution network with EVs and CHPs.

Area of Science:

• Electrical Engineering
• Power Systems
• Renewable Energy Integration

Background:

• Increasing renewable energy in power grids necessitates managing output prediction errors.
• Electric vehicles (EVs) and demand response (DR) offer potential solutions for grid stability.
• Active distribution networks (ADNs) require robust operational strategies to accommodate variability.

Purpose of the Study:

• To develop a robust optimization model for distribution networks facing renewable energy uncertainty.
• To reduce the impact of renewable energy prediction errors on grid operations.
• To enhance renewable energy consumption and operational reliability in ADNs.

Main Methods:

• A three-stage robust optimization framework: day-ahead, intraday, and real-time.
• Day-ahead stage: Price-based DR for load shifting and cost minimization.
• Intraday stage: EV charging/discharging and rolling optimization with updated forecasts.
• Real-time stage: Incentive-based DR for smoothing fluctuations and ensuring stability.
• Simulations conducted on the IEEE 33-bus test system.

Main Results:

• The proposed three-stage strategy significantly enhances renewable energy consumption.
• Operational costs are reduced through optimized load shifting and EV integration.
• Grid reliability and voltage stability are improved, demonstrating effective management of renewable output variations.

Conclusions:

• The three-stage cooperative operation strategy effectively addresses renewable energy uncertainty in distribution networks.
• Integration of EVs and demand response is crucial for maximizing renewable energy utilization and grid stability.
• The model provides a reliable framework for operating active distribution networks with high renewable energy penetration.