Multi-area Economic Dispatch with Tie-line Constraints Based on Improved Comprehensive Iearning Particle Swarm Optimization Algorithm with Forgetting Velocity
Traditional economic dispatch(ED)methods in power systems typically treat all generators within the system as a single entity for optimization.However,as the scale of power systems continues to expand,the limitations of such monolithic dispatch approaches have become increasingly apparent.Consequently,modern power systems are often divided into multiple regions,with power transfer and exchange facilitated through interconnection lines.Mathematically,this multi-area economic dispatch(MAED)becomes particularly complex when valve-point effects,multiple fuel options,and transmission capacity constraints of interconnection lines are considered.This transforms the problem into a highly intricate optimization challenge characterized by multi-constraint,multimodal,nonlinear,and multivariable coupling.To address this challenge,we propose an enhanced version of the comprehensive learning particle swarm optimization algorithm,named FV-ICLPSO.This algorithm incorporates three critical improvements to mitigate the slow convergence issues observed in the traditional CLPSO algorithm:(1)a novel adaptive strategy that dynamically adjusts the learning probability based on the iteration progress and problem dimensionality,thereby enhancing global search capabilities;(2)an adaptive particle selection method that assigns different selection ranges to particles based on their fitness levels,optimizing the choice of learning exemplars;(3)an improved velocity update formula that incorporates a mechanism to forget previous velocities,encouraging particles to explore more promising regions of the search space.To validate the performance of FV-ICLPSO,extensive comparative experiments were conducted on 30 benchmark functions from CEC2014.Subsequently,the algorithm was applied to MAED problems involving a 3 area with 10 Unit system and a 4 area with 40 Unit system.The simulation results demonstrate that FV-ICLPSO outperforms the traditional CLPSO,several other advanced optimization algorithms,and the results reported in recent literature,particularly in terms of solution quality,convergence speed,robustness,and statistical significance.
multi-area power systemcomprehensive learning particle swarm optimizereconomic dispatchtie-line
万方数据
维普
