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A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex

Ijaz Ahmed1, Um-E-Habiba Alvi1,2, Abdul Basit1

  • 1Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS), Islamabad, Pakistan.

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A new hybrid optimization method combines genetic algorithms (GA) and sequential quadratic programming (SQP) to efficiently solve the dynamic economic dispatch problem (DEDP). This approach ensures optimal power generation while considering complex constraints for a competitive energy market.

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Area of Science:

  • Electrical Power Systems Engineering
  • Optimization Techniques
  • Computational Intelligence

Background:

  • The electricity sector faces challenges from market reforms, increasing energy demand, and environmental concerns.
  • Traditional optimization methods struggle with the non-convex and nonlinear nature of the economic dispatch problem.
  • Meta-heuristic approaches are gaining prominence for solving complex optimization issues in power systems.

Purpose of the Study:

  • To propose a novel soft computing optimization technique for the dynamic economic dispatch problem (DEDP).
  • To address complex non-convex machines with multiple constraints, including valve point loading effect (VPLE) and multiple fueling options (MFO).
  • To develop an efficient and accurate method for optimal power generation in a competitive energy market.

Main Methods:

  • A hybrid optimization framework combining Genetic Algorithm (GA) for initial optimization and Sequential Quadratic Programming (SQP) for fine-tuning.
  • Simulation analysis using the proposed GA-SQP method on ten benchmark case studies, including non-convex IEEE bus systems.
  • Evaluation of the method's performance considering VPLE and MFO constraints in thermal power plants.

Main Results:

  • The GA-SQP hybrid method demonstrates reduced computational cost and finite execution time.
  • Optimal power generation is achieved, meeting targeted power demand and load requirements.
  • The strategy shows accuracy, convergence, and reliability, outperforming conventional methods in solving hard-bounded DEDP.

Conclusions:

  • The proposed GA-SQP hybrid optimization technique is robust and effective for solving the dynamic economic dispatch problem.
  • The method offers fast convergence and achieves optimal solutions within limited simulations.
  • Simulation results confirm the applicability and adequacy of the GA-SQP scheme for modern power system operations.