Adaptability Analysis and Optimization Algorithm for Data-Driven Virtual Power Plant Business Models

The growing integration of renewable energy sources into modern power systems has driven the emergence of Virtual Power Plants (VPPs) as an essential concept for managing distributed energy resources. However, the adaptability of VPP business models to varying market conditions, regulatory policies, and operational uncertainties remains a critical challenge. Traditional approaches to VPP management often rely on static optimization models that struggle to cope with dynamic fluctuations in electricity prices, renewable generation output, and demand-side flexibility. To address this challenge, this paper proposes a data-driven framework for analyzing and optimizing the adaptability of VPP business models. The framework integrates machine learning techniques for predictive analytics, scenario-based simulations to evaluate business model resilience, and robust optimization algorithms for decision-making under uncertainty. By assessing key performance indicators such as profitability, operational efficiency, and risk exposure under diverse scenarios, the proposed approach enables VPP operators to make informed strategic decisions. Experimental results demonstrate that the framework effectively enhances the adaptability of VPP business models, improving economic viability and system reliability while mitigating market risks. This study provides valuable insights into the dynamic adaptation of VPPs, offering a scalable solution for the efficient and resilient integration of renewable energy into modern power grids.