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Dynamic economic emission dispatch of combined heat and power system based on multi-objective differential evolution

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  • 1School of Economics and Trade, Henan University of Animal Husbandry and Economy, Zhengzhou, Henan, China.

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Summary

This study enhances the multi-objective differential evolution algorithm for combined heat and power systems. The improved algorithm effectively balances economic costs and pollutant emissions, optimizing system performance.

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Area of Science:

  • Engineering Optimization
  • Computational Intelligence
  • Sustainable Energy Systems

Background:

  • Multi-objective optimization is crucial in engineering, particularly for combined heat and power (CHP) systems.
  • Balancing economic costs and environmental emissions presents a significant challenge for conventional optimization methods.
  • Existing algorithms struggle to effectively manage the competing objectives in CHP system scheduling.

Purpose of the Study:

  • To improve the multi-objective differential evolution algorithm for enhanced performance in complex optimization tasks.
  • To develop a dynamic economic emission dispatch model for CHP systems using the enhanced algorithm.
  • To optimize both economic benefits and environmental impact in CHP system operations.

Main Methods:

  • Adaptive modification of scaling factor and crossover probability in the differential evolution algorithm.
  • Incorporation of non-dominated sorting and congestion distance calculation for multi-objective handling.
  • Integration of elite populations and quadratic mutation for improved convergence and diversity.
  • Application to a dynamic economic emission dispatch model for CHP systems.

Main Results:

  • The improved algorithm demonstrated superior performance on the Zitzler-Deb-Thiele 1 function test, with excellent generational distance and inverted generational distance metrics.
  • When applied to the IEEE 30-node system, the algorithm achieved significant reductions in fuel cost ($2,300,590) and pollution emissions (200,285 kg).
  • The algorithm's Pareto optimal frontier showed better distribution and convergence compared to other methods like the time-varying multi-objective PSO algorithm.

Conclusions:

  • The enhanced multi-objective differential evolution algorithm effectively balances operational costs and environmental performance in CHP systems.
  • The algorithm exhibits strong adaptability and optimization capabilities for practical engineering applications.
  • This research contributes to more efficient and sustainable operation of combined heat and power systems.