<p>Grid electricity purchase/sale prices.</p>

The integration of renewable energy sources (RESs) introduces significant challenges related to uncertainty and intermittency in power grids. While Artificial Intelligence (AI) offers promising solutions for Virtual Power Plants (VPP) optimization, existing approaches often treat load forecasting, system dispatch, and demand response as loosely coupled components, limiting their ability to holistically manage these deep uncertainties. To address this, we propose a novel AI-enhanced multi-timescale optimization strategy that creates a synergistic, integrated framework. Methodologically, the approach begins with an attention-augmented Bidirectional Long Short-Term Memory (BiLSTM) model that generates high-fidelity spatiotemporal load forecasts, providing crucial spatial-aware inputs often overlooked by traditional models. These enhanced forecasts are then leveraged by a Model Predictive Control (MPC) strategy for more robust and proactive day-ahead and intraday dispatch. Crucially, the framework integrates a dynamic demand response (DDR) mechanism that is directly coupled with real-time MPC outputs, ensuring that load flexibility is mobilized based on immediate system needs rather than static signals alone. Simulations, driven by real-world operational data, confirm that this integrated strategy not only reduces operational costs and improves forecasting accuracy but also establishes a more resilient and adaptive VPP operational paradigm compared to prior AI-based methods.可再生能源（Renewable Energy Sources, RESs）的并网为电网带来了显著的不确定性与间歇性挑战。尽管人工智能（Artificial Intelligence, AI）可为虚拟电厂（Virtual Power Plants, VPP）优化提供极具潜力的解决方案，但现有研究往往将负荷预测、系统调度与需求响应视为松散耦合的模块，限制了其对这类深层不确定性开展全局化管理的能力。 为解决上述问题，本文提出一种新型人工智能增强型多时间尺度优化策略，构建了协同一体化的优化框架。方法层面，该策略首先基于注意力增强双向长短期记忆网络（Attention-augmented Bidirectional Long Short-Term Memory, BiLSTM）生成高精度时空负荷预测结果，提供了传统模型常忽略的空间感知输入信息。随后，模型预测控制（Model Predictive Control, MPC）策略利用这些优化后的预测结果，实现更具鲁棒性且主动的日前与日内调度。尤为关键的是，该框架集成了动态需求响应（Dynamic Demand Response, DDR）机制，该机制与实时模型预测控制输出直接耦合，确保负荷灵活性可根据系统实时需求进行调动，而非仅依赖静态调度信号。 依托真实运行数据开展的仿真实验证实，相较于现有基于人工智能的优化方法，该一体化策略不仅可降低运行成本、提升预测精度，还构建了更具韧性与自适应能力的虚拟电厂运行范式。