AI-enhanced multi-timescale optimization strategy for virtual power plants: Advancing losad forecasting and dynamic...

Area of Science:

• Electrical Engineering
• Computer Science
• Energy Systems

Background:

• Integration of renewable energy sources (RESs) presents challenges in power grid stability due to uncertainty and intermittency.
• Existing Artificial Intelligence (AI) solutions for Virtual Power Plants (VPPs) often lack holistic integration of key components like load forecasting, system dispatch, and demand response.
• This fragmentation limits the effective management of deep uncertainties inherent in modern power grids.

Purpose of the Study:

• To develop a novel AI-enhanced multi-timescale optimization strategy for synergistic VPP operation.
• To create an integrated framework that addresses the limitations of loosely coupled components in existing VPP optimization approaches.
• To improve the resilience and adaptability of VPP operations in the face of RES integration challenges.

Main Methods:

• Utilized an attention-augmented Bidirectional Long Short-Term Memory (BiLSTM) model for high-fidelity spatiotemporal load forecasting, incorporating spatial-aware inputs.
• Implemented a Model Predictive Control (MPC) strategy for robust day-ahead and intraday dispatch, leveraging enhanced load forecasts.
• Integrated a dynamic demand response (DDR) mechanism directly coupled with real-time MPC outputs for responsive load flexibility mobilization.

Main Results:

• The proposed integrated strategy demonstrated significant improvements in forecasting accuracy compared to traditional models.
• Simulations showed a reduction in operational costs for VPPs utilizing the novel framework.
• The AI-enhanced approach led to a more resilient and adaptive VPP operational paradigm.

Conclusions:

• The synergistic integration of advanced AI techniques, including BiLSTM and MPC with DDR, effectively addresses VPP optimization challenges posed by RES.
• The developed framework offers a more robust and proactive approach to managing power grid uncertainties.
• This study establishes a new paradigm for VPP operations, enhancing grid stability and economic efficiency.