Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to high-fidelity dynamic operation. However, model mismatch across fidelities obscures the sources of performance loss and complicates the quantification of architecture-to-operation performance gaps. We propose an online, machine-learning-accelerated multi-resolution optimization framework that estimates an architecture-specific upper bound on achievable performance while minimizing expensive high-fidelity model evaluations. We demonstrate the approach on a pilot energy system supplying a 1 MW industrial heat load. First, we solve a multi-objective architecture optimization to select the system configuration and component capacities. We then develop an machine learning (ML)-accelerated multi-resolution, receding-horizon optimal control strategy that approaches the achievable-performance bound for the specified architecture, given the additional controls and dynamics not captured by the architectural optimization model. The ML-guided controller adaptively schedules the optimization resolution based on predictive uncertainty and warm-starts high-fidelity solves using elite low-fidelity solutions. Our results on the pilot case study show that the proposed multi-resolution strategy reduces the architecture-to-operation performance gap by up to 42% relative to a rule-based controller, while reducing required high-fidelity model evaluations by 34% relative to the same multi-fidelity approach without ML guidance, enabling faster and more reliable design verification. Together, these gains make high-fidelity verification tractable, providing a practical upper bound on achievable operational performance.

翻译：设计用于工业过程的可靠集成能源系统需通过从架构级规格到高保真动态运行的多保真度优化与验证模型。然而，不同保真度间的模型失配掩盖了性能损失的根源，并增加了量化架构到运行性能差距的复杂性。我们提出了一种在线、机器学习加速的多分辨率优化框架，该框架可在最小化昂贵高保真模型评估的同时，估算与特定架构相关的可实现性能上限。我们以供应1 MW工业热负荷的试点能源系统验证该方法。首先，通过求解多目标架构优化问题选择系统配置与组件容量。随后，我们构建了机器学习（ML）加速的多分辨率滚动时域最优控制策略，该策略可接近指定架构在考虑架构优化模型未捕捉的额外控制与动态因素后的可实现性能边界。该ML引导控制器基于预测不确定性自适应调度优化分辨率，并利用精英低保真解对高保真求解进行热启动。试点案例研究结果表明，与基于规则的控制相比，所提出的多分辨率策略将架构到运行性能差距最多降低42%，同时与未使用ML引导的同类多保真方法相比，所需高保真模型评估减少34%，从而加速并增强了设计验证的可靠性。这些改进共同使高保真验证变得可行，为可实现的运行性能提供了实用上限。