An Unified PSO and Fuzzy Satisfaction Framework for Power Flow Optimization

This study introduces a new method for multi-objective optimization (MOO) in electrical power systems, aiming to minimise emission release, power losses and fuel costs concurrently. Diverging from traditional techniques that primarily target cost reduction, the proposed Power Flow Optimization (PFO) framework combines Fuzzy Satisfaction Maximization strategy with Particle Swarm Optimization (PSO), acknowledging the vagueness in decision-makers’ objectives. By formulating the optimization problem as a fuzzy satisfaction maximization problem, the method aims to find optimal trade-off solutions that balance conflicting objectives. Testing the PFO on a standard IEEE 30-bus system with six generators demonstrates its effectiveness in providing solutions that optimize both actual and imaginary powers while considering fuel cost, emission reduction, and power loss, thereby offering valuable insights for efficient power system operation.