Smart energy management: Process structure-based hybrid neural networks for optimal scheduling and economic predictive control in integrated systems

Long Wu, Xunyuan Yin, Lei Pan and Jinfeng Liu

Applied Energy, 2025, vol. 380, issue C, No S0306261924023481

Abstract: Integrated energy systems (IESs) are complex prosumers consisting of diverse operating units spanning multiple domains. The tight integration of these units results in varied dynamic characteristics and intricate nonlinear process interactions, making detailed dynamic modeling and successful operational optimization challenging. To address these concerns, we propose a process structure-based 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. These agents collaborate in the decision-making process to achieve dynamic synergy in real time while reducing computational costs. 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.

Keywords: Integrated energy systems; Time-series neural networks; Process structure knowledge; Long-term management; Real-time coordination; Economic model predictive control (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2024.124965

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