Optimal DG Distribution in 33-Node System via Particle Swarm Optimization Algorithm

In power systems, rational planning of Distributed Generation (DG) is crucial for enhancing grid stability, reliability, and economic efficiency. This article demonstrates how to implement optimal DG distribution—addressing both placement and sizing—in a 33-node system using Particle Swarm Optimization (PSO).

Particle Swarm Optimization is a swarm intelligence-based optimization technique that simulates bird flock foraging behavior. For DG planning, each particle represents a potential DG configuration (location and capacity), with iterative updates to particle velocity and position to converge toward optimal solutions. In code implementation, particles are typically represented as arrays containing DG locations and capacities, while velocity updates follow standard PSO equations with inertia weights.

For 33-node systems, optimization objectives typically include minimizing network losses, improving voltage profiles, or reducing investment costs. Core algorithm steps involve:

Particle Swarm Initialization: Randomly generate initial DG configurations (positions and capacities) within system constraints. Code implementation often uses uniform random distribution within predefined bounds for each variable.

Fitness Evaluation: Compute performance metrics (e.g., power losses, voltage deviations) for each configuration. This requires power flow calculations—commonly implemented using Newton-Raphson or forward-backward sweep methods—with embedded DG models.

Velocity and Position Updates: Adjust particle states based on personal best (pbest) and global best (gbest) information. The update equations incorporate cognitive and social parameters, typically implemented through vectorized operations for computational efficiency.

Iterative Optimization: Repeat evaluation and update cycles until convergence criteria are met (e.g., maximum iterations or minimum fitness improvement). Convergence monitoring can be implemented through fitness history tracking and tolerance thresholds.

The method excels at handling nonlinear, multi-objective optimization problems with high computational efficiency, making it particularly suitable for DG planning. Practical implementations must incorporate engineering constraints such as node voltage limits and power flow equations, which are typically enforced through penalty functions or constraint-handling techniques in the fitness evaluation.