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This study introduces an AI-enhanced framework for optimizing Virtual Power Plants (VPPs) by integrating load forecasting, dispatch, and demand response. The novel approach improves VPP resilience and reduces operational costs in grids with renewable energy sources.

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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.