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Comprehensive energy efficiency optimization algorithm for steel load considering network reconstruction and demand

Yuxiu Zang1,2, Shunjiang Wang3,4, Weichun Ge3,4

  • 1School of electrical engineering, Shenyang University of Technology, No. 111, Shenliao West Road, Economic & Technological Development Zone, Shenyang, 110000, China. xiudiubiu@163.com.

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This summary is machine-generated.

This study introduces a two-level optimization method to enhance energy efficiency in steel plants. The approach reduces energy costs by 17.77% and operating expenses by 26.2% through improved grid management and demand response strategies.

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Area of Science:

  • Energy Systems Engineering
  • Operations Research
  • Artificial Intelligence

Background:

  • Industrial energy consumption, particularly in steel plants, is significant and often inefficient.
  • Optimizing energy efficiency in steel plants is crucial for economic and environmental reasons but remains underdeveloped.
  • Existing power grid structures pose challenges to improving operational economy and load-side energy efficiency.

Purpose of the Study:

  • To propose a two-level collaborative optimization method for enhancing energy efficiency in steel plants.
  • To integrate dynamic reconstruction costs, transmission loss costs, energy costs, and demand response benefits into a unified optimization framework.
  • To improve the operational economy and load-side energy efficiency of steel plants.

Main Methods:

  • Developed a two-level collaborative optimization model considering grid topology, dynamic reconstruction costs, transmission losses, energy costs, and demand response.
  • Built mathematical models for stable, impact, and steel production line loads, identifying key parameters using Back Propagation neural networks.
  • Analyzed the impact of dynamic grid losses and real-time electricity prices on grid energy efficiency under operational constraints.

Main Results:

  • The proposed optimization model improved load-side energy efficiency by optimizing energy consumption and demand response timing.
  • Achieved a 17.77% reduction in load-side energy costs.
  • Reduced network loss by 1.8% and overall power grid operating costs by 26.2%.

Conclusions:

  • The two-level optimization method effectively enhances energy utilization efficiency in steel plants.
  • The approach successfully reduces distribution network losses and improves overall economic efficiency.
  • Optimizing energy consumption and demand response participation is key to achieving significant cost and efficiency improvements.