Receding Horizon Optimization of Power Demand Response for Production-Oriented Users With Real-Time Operating Status-Awareness

The advancement of Internet of Things (IoT) technologies in industrial environments is facilitating electricity consumers, particularly high-energy production users, from traditionally uncontrollable loads to controllable loads. This transformation enhances their capability to actively participate in power system operations, especially under real-time pricing demand response scenarios. This paper proposes a receding horizon optimization framework for integrated inventory and energy management in production-oriented users, driven by IoT-based monitoring of equipment operating statuses. An IoT-based architecture is developed to monitor electricity consumption, inventory status and feed-discharge flows (FDF) through entire production process (EPP). Energy consumption within the EPP is categorized into three types: production equipment (PE), storage equipment (SE) and auxiliary production equipment (APE), all of which are dynamically linked to inventory status and FDF variations. The controllable power of EPP is derived by modeling the FDF through material reservoirs (MRs), which serve as nodal elements in the production process. A receding horizon optimization model is then formulated to minimize electricity costs, equipment on-off costs and operation speed adjustment costs associated with production sub-processes (PSPs) and their corresponding electrical equipment. The model determines the on/off status and production rates of PEC as well as the inventory status of MRC at each time period using real-time operating status-awareness data. The simulation results validate that the proposed method improves peak-valley load shifting and economic efficiency under real-time electricity pricing mechanisms, while ensuring compliance with daily cement production requirements.

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