用混合神经网络预测多时间尺度能源系统动态，提升调度与控制效率
Smart energy management: process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems
- 结合物理过程知识与分层神经网络，构建跨时间尺度的系统动态预测模型
- 日间调度与实时控制分别提升25%和40%，整体性能提升超70%
- 适合能源系统优化、智能控制及工业界实际部署场景
综合能源系统（IES）是由跨多个领域的多种运行单元构成的复杂系统。为应对其运行挑战，本文提出一种基于物理信息的混合时间序列神经网络（NN）代理模型，用于预测IES在多时间尺度下的动态表现。该方法将时间序列多层感知机（MLP）应用于各运行单元，并融合系统结构与基本动态的先验知识，形成三种混合神经网络（长期、慢速、快速MLP），实现对全系统动态的多时间尺度预测。基于这些MLP，设计了神经网络调度器与神经网络经济模型预测控制（NEMPC）框架，满足全局运行需求：快速响应电力操作请求、保障客户冷量供应、提升系统盈利能力，同时解决IES中广泛存在的动态时间尺度多样性问题。日间调度采用基于ReLU网络的MLP建模，从长期视角有效表征系统在多种工况下的性能，并精确转化为混合整数线性规划问题以实现高效求解。实时NEMPC基于慢速与快速MLP，包含两个串行分布式控制代理：慢速NEMPC用于冷却主导子系统（响应较慢），快速NEMPC用于电力主导子系统（响应较快）。大量仿真表明，所提出的调度与控制方案相比基准方法分别提升约25%和40%，整体性能优于基准方法超过70%。
原文摘要 · Abstract (English)
Integrated energy systems (IESs) are complex systems consisting of diverse operating units spanning multiple domains. To address its operational challenges, we propose a physics-informed hybrid time-series neural network (NN) surrogate to predict the dynamic performance of IESs across multiple time scales. This neural network-based modeling approach develops time-series multi-layer perceptrons (MLPs) for the operating units and integrates them with prior process knowledge about system structure and fundamental dynamics. This integration forms three hybrid NNs (long-term, slow, and fast MLPs) that predict the entire system dynamics across multiple time scales. Leveraging these MLPs, we design an NN-based scheduler and an NN-based economic model predictive control (NEMPC) framework to meet global operational requirements: rapid electrical power responsiveness to operators requests, adequate cooling supply to customers, and increased system profitability, while addressing the dynamic time-scale multiplicity present in IESs. The proposed day-ahead scheduler is formulated using the ReLU network-based MLP, which effectively represents IES performance under a broad range of conditions from a long-term perspective. The scheduler is then exactly recast into a mixed-integer linear programming problem for efficient evaluation. The real-time NEMPC, based on slow and fast MLPs, comprises two sequential distributed control agents: a slow NEMPC for the cooling-dominant subsystem with slower transient responses and a fast NEMPC for the power-dominant subsystem with faster responses. Extensive simulations demonstrate that the developed scheduler and NEMPC schemes outperform their respective benchmark scheduler and controller by about 25% and 40%. Together, they enhance overall system performance by over 70% compared to benchmark approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。