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Process Knowledge-Guided Optimization Control for Once-Through Boiler-Turbine Units Based on Multi-Agent

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Summary
This summary is machine-generated.

This study introduces a novel distributed control framework for coal-fired power units, enhancing load response speed and control effectiveness using multiagent deep reinforcement learning guided by process knowledge.

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Area of Science:

  • Engineering
  • Computer Science
  • Energy Systems

Background:

  • Traditional centralized optimization control for coal-fired power units struggles with slow load response due to computational demands.
  • Limited computing power in devices exacerbates these challenges for fast dynamic adjustments.

Purpose of the Study:

  • To develop a process knowledge-guided distributed optimization control framework for once-through boiler-turbine units.
  • To improve the speed and effectiveness of load response in coal-fired power units.

Main Methods:

  • Utilized a centralized training distributed execution multiagent deep reinforcement learning (MADRL) algorithm.
  • Modeled the coordinated control system as a cooperative multiagent Markov decision process, dividing it into three subsystems.
  • Integrated process knowledge via a low-precision process model to guide MADRL training, using distributed model predictive control (DMPC) for initial actions and an action fusion strategy.

Main Results:

  • The proposed framework demonstrated faster response speeds compared to existing algorithms.
  • Achieved superior control effects in simulations for once-through boiler-turbine units.
  • Effectiveness verified on a simulation platform.

Conclusions:

  • The process knowledge-guided distributed optimization control framework effectively addresses the limitations of traditional methods.
  • MADRL, guided by process knowledge, offers a promising approach for optimizing power plant operations.