Optimization Problems Using Improved Particle Swarm Algorithm with Multiple Distributed Energy Resources

The optimization problem using improved particle swarm algorithm with multiple distributed energy resources focuses on developing optimization methods for coordinated dispatch and economic operation of distributed energy resources (such as photovoltaics, wind power, energy storage systems) in power systems. Traditional particle swarm optimization (PSO) may exhibit slow convergence or susceptibility to local optima when solving such complex nonlinear optimization problems. The enhanced PSO algorithm incorporates dynamic weight adjustment, multi-strategy mutation, or hybrid optimization approaches to better accommodate the randomness and intermittency of distributed energy resources, thereby improving optimization performance.

Key implementation improvements include: dynamically adjusting the inertia weight in the particle velocity update formula to balance global exploration and local exploitation capabilities (e.g., using linear/nonlinear decreasing functions via code like w = w_max - iter*(w_max-w_min)/max_iter); introducing chaos mapping or simulated annealing mechanisms to enhance population diversity and prevent premature convergence (implementation may involve logistic maps for chaotic sequences or Metropolis criteria for annealing acceptance); integrating constraint handling techniques such as penalty function methods to ensure compliance with grid safety and economic operational requirements (commonly implemented through adaptive penalty coefficients in the fitness function). These enhancements enable the algorithm to efficiently find global or near-optimal solutions when solving optimization problems involving multiple types of distributed energy resources.

This optimization approach can be applied to scenarios like microgrid dispatch, distribution network reconfiguration, and multi-objective optimization, contributing to increased renewable energy integration rates and reduced system operational costs.