Distributionally robust optimization dispatch strategy for virtual power plants based on data generation...

Area of Science:

• Renewable Energy Systems
• Optimization Theory
• Artificial Intelligence

Background:

• Virtual power plants (VPPs) face challenges due to limited renewable energy data and generation/load uncertainties.
• These issues hinder reliable operation and economic dispatch in VPPs.

Purpose of the Study:

• To propose a distributionally robust optimization (DRO) scheduling strategy for VPPs using data generation augmentation.
• To address data scarcity and uncertainty in VPP operations for improved performance.

Main Methods:

• Integrated a physical photovoltaic (PV) model with deep learning (CNN, multi-head attention) for data generation, calibrated by historical meteorological data.
• Developed a DRO dispatch model using Wasserstein distance to handle source and load uncertainties.
• Reformulated the dispatch model into a tractable mixed-integer linear programming problem via linear decision rules and strong duality.

Main Results:

• The proposed strategy significantly improves data generation accuracy and robustness across various meteorological conditions.
• The DRO dispatch method effectively adapts to dynamic PV and load power variability.
• Demonstrated reductions in operating costs and enhancements in VPP operating reliability.

Conclusions:

• The data generation augmentation and DRO scheduling strategy effectively overcomes data scarcity and uncertainty challenges in VPPs.
• The approach offers a robust and adaptable solution for optimizing VPP operations, leading to economic and reliability benefits.