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  1. Home
  3. A Multi-objective Optimisation Approach With Improved Pareto-optimal Solutions To Enhance Economic And Environmental Dispatch In Power Systems.
  1. Home
  3. A Multi-objective Optimisation Approach With Improved Pareto-optimal Solutions To Enhance Economic And Environmental Dispatch In Power Systems.

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A multi-objective optimisation approach with improved pareto-optimal solutions to enhance economic and environmental

Muhammad Ilyas Khan Khalil1, Izaz Ur Rahman1, Muhammad Zakarya1,2

  • 1Department of Computer Science, Abdul Wali Khan University, Mardan, Pakistan.

Scientific Reports
|June 11, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

The Non-dominated Sorting Multi-objective Particle Swarm Optimization with Local Best (NS-MJPSOloc) algorithm effectively solves the economic and environmental dispatch problem. This novel approach reduces fuel costs by 6.4%, computational time by 9.1%, and emissions by 9.4%.

Keywords:
Evolutionary factorLarge-scale optimisationMarkov chainParticle swarm optimisationScalability

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Area of Science:

  • Electrical Engineering
  • Optimization Algorithms
  • Computational Intelligence

Background:

  • The economic/environmental dispatch (EED) problem requires balancing electricity demand with operational costs and environmental impact.
  • Traditional optimization methods struggle with the non-commensurable objectives of cost reduction and emission control in power systems.
  • Growing environmental regulations necessitate advanced methods for sustainable energy management.

Purpose of the Study:

  • To implement and evaluate the Non-dominated Sorting Multi-objective Particle Swarm Optimization with Local Best (NS-MJPSOloc) algorithm for the EED problem.
  • To enhance the performance of Particle Swarm Optimization (PSO) in multi-objective optimization by redefining local best candidates.
  • To achieve an optimal trade-off between economic costs and environmental emissions in power generation.

Main Methods:

  • Utilized the nth state Markovian jumping particle swarm optimization (PSO) with local search awareness.
  • Incorporated an evolutionary factor-based mechanism for identifying compromise solutions.
  • Employed a Markov chain state jumping technique to control Pareto-optimal set size and neighborhood topology.

Main Results:

  • The NS-MJPSOloc algorithm successfully generated a diverse and well-distributed set of Pareto-optimal solutions in a single iteration.
  • Demonstrated superior performance compared to classical PSO in terms of solution diversity and quality.
  • Achieved a reduction of 6.4% in fuel costs, 9.1% in computational time, and 9.4% in emissions (tons/hour).

Conclusions:

  • The proposed NS-MJPSOloc approach is effective for solving the multi-objective EED problem, providing high-quality trade-off solutions.
  • The algorithm's novel approach to local best candidates and Pareto-optimal set management enhances optimization performance.
  • Validated the practical applicability and efficiency of NS-MJPSOloc for sustainable power system operation.