top of page
Search

# Economic Load Dispatch using PSO in MATLAB

Economic Load Dispatch using PSO in MATLAB

Introduction:

We will explore the concept of Economic Load Dispatch (ELD) and its implementation using Particle Swarm Optimization (PSO). ELD involves the optimal allocation of power generation among different generators to meet the load demand while minimizing fuel costs. PSO is a powerful optimization technique inspired by the social behavior of bird flocks or fish schools, which iteratively searches for the optimal solution.

Understanding Economic Load Dispatch: Economic Load Dispatch aims to allocate power generation among multiple generators in a power system in such a way that the total fuel cost is minimized while satisfying the load demand and system constraints. It considers factors such as generator characteristics, fuel costs, transmission losses, and system constraints to determine the optimal power output for each generator.

Implementation Using Particle Swarm Optimization: The implementation of Economic Load Dispatch using PSO involves defining an objective function that represents the total fuel cost while considering the power balance equation. The objective function is formulated to minimize the total fuel cost subject to the power balance constraint. PSO is then applied to search for the optimal solution by iteratively updating the positions of particles in the search space.

Key Components of the Implementation:

1. Generator Data: The model includes data for six generators, including their characteristics and loss coefficients.

2. Load Demand: The load demand for the power system is specified as 1350 megawatts.

3. Objective Function: The objective function calculates the total fuel cost and ensures power balance by summing up the power generation of all generators and accounting for losses.

4. Particle Swarm Optimization: PSO is applied with a population size of 100 and 10,000 iterations to find the optimal power generation for minimizing fuel costs.

5. Results Analysis: After completing the iterations, the optimal power generation for each generator is obtained, along with the total fuel cost and losses in the power system.

Simulation Results: Upon running the simulation, the optimal power generation for each generator is determined as follows:

• Generator 1: 446.4 MW

• Generator 2: 200 MW

• Generator 3: 289.11 MW

• Generator 4: 145.16 MW

• Generator 5: 173.41 MW

• Generator 6: 0.1 MW The total losses in the power system are calculated as 14.28 MW, and the total fuel cost for the optimal solution is found to be 1.658.

Conclusion: Economic Load Dispatch is a critical task in power system operation, aiming to minimize fuel costs while meeting load demand and system constraints. Particle Swarm Optimization proves to be an effective technique for solving the ELD problem by iteratively searching for the optimal solution. By implementing PSO, power system operators can make informed decisions to optimize power generation and enhance system efficiency.