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Related Experiment Video

Updated: Apr 29, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.6K

Interval multi-objective planning of integrated energy systems via constrained multi-objective chaotic evolution

Ge Lan1, Yingchao Dong2, Peng Ren1

  • 1School of Energy Engineering, Xinjiang Institute of Engineering, Urumqi, 830023, Xinjiang, China.

Scientific Reports
|April 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a robust optimization framework for integrated energy systems, balancing cost, efficiency, and emissions under demand response and renewable energy uncertainties. The developed algorithm efficiently determines optimal capacities, enhancing energy system planning.

Keywords:
Chaotic evolution optimizationDemand responseIntegrated energy systemsInterval multi-objective optimizationUncertainty

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Last Updated: Apr 29, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

12.6K

Area of Science:

  • Energy Systems Engineering
  • Optimization Theory
  • Environmental Science

Background:

  • Integrated energy systems (IES) face challenges in reliability due to uncertainties from demand response (DR) and renewable energy generation (REG).
  • Existing planning methods struggle to simultaneously address operational economy, energy efficiency, and carbon emissions under these volatile conditions.

Purpose of the Study:

  • To develop a comprehensive interval multi-objective optimization (IMOO) framework for IES planning.
  • To ensure robustness against multi-source uncertainties in DR and REG.
  • To optimize operational economy, energy efficiency, and carbon emissions concurrently.

Main Methods:

  • Utilized box uncertainty sets to model DR and REG fluctuations.
  • Transformed the uncertain model into a deterministic equivalent using reliability-based interval probability and order relations.
  • Developed a constrained multi-objective chaotic evolution algorithm (CMOCEO) with advanced constraint-handling and sorting strategies.

Main Results:

  • The proposed IMOO framework effectively identified optimal equipment capacities for a typical IES.
  • Achieved specific cost, efficiency, and emission intervals under defined uncertainty levels.
  • CMOCEO demonstrated superior computational efficiency compared to existing algorithms like NSGA-III.

Conclusions:

  • The research provides a practical tool for scientific IES planning amidst demand and supply volatility.
  • Decision-makers can flexibly balance system performance and robustness by adjusting objective weights.
  • The framework enhances the reliability and sustainability of modern energy systems.