Application of Particle Swarm Optimization in Power Grid Reactive Power Optimization

Particle Swarm Optimization is widely applied in power grid reactive power optimization. The algorithm typically employs the basic Particle Swarm Optimization method, combined with Newton's method for power flow calculation components. Particle Swarm Optimization is a heuristic optimization algorithm that solves optimization problems by simulating collective behavior in bird flocks. In the context of power grid reactive power optimization, PSO helps determine optimal reactive power generation schemes to achieve stable grid operation. The basic PSO implementation involves initializing a population of particles with random positions and velocities in the solution space. Each particle's position represents a potential solution, and the algorithm updates particle velocities and positions iteratively using personal best and global best values. Key functions include velocity update: v_i(t+1) = w*v_i(t) + c1*r1*(pbest_i - x_i(t)) + c2*r2*(gbest - x_i(t)) and position update: x_i(t+1) = x_i(t) + v_i(t+1). Newton's method, a numerical calculation approach, solves nonlinear equations through iterative approximation and is used for computing power flow distribution in the grid. The Newton-Raphson power flow method involves forming the power flow equations and iteratively solving using the Jacobian matrix. The implementation typically includes building the Y-bus matrix, calculating power mismatches, and updating voltage magnitudes and angles until convergence is achieved. By integrating Particle Swarm Optimization with Newton's method, power grid reactive power optimization problems can be computed more accurately, thereby improving grid operational efficiency and stability. The hybrid approach allows PSO to handle the optimization search space while Newton's method provides precise power flow solutions during fitness evaluation.