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# Economic Load Dispatch Optimization by BAT Algorithm in MATLAB

Economic Load Dispatch Optimization by BAT Algorithm in MATLAB

Introduction:

This code implements power generation dispatch optimization with consideration for losses in the transmission network. The objective is to minimize the overall fuel cost while meeting the demand for electricity and accounting for transmission losses.

Data Preparation:

• Generated data includes parameters for different generators (A, B, C) and their corresponding limits.

• Demand is fixed at 150 units.

Cost Function:

• The fuel cost is calculated using a specified formula based on the power output of each generator.

Power Balance Equation:

• Ensures that the sum of power generation meets the demand and accounts for losses in the transmission network.

Objective Function:

• The objective function combines the power balance equation with the power loss equation, aiming to minimize the total fuel cost with losses.

Particle Swarm Optimization (PSO):

• PSO is used to optimize the objective function.

• Parameters such as the number of iterations, number of particles, loudness factor, and pulse rate are specified.

PSO Algorithm:

1. Initialization: Initial frequency, velocity, and solution are generated based on the lower and upper limits of power generation.

2. Fitness Calculation: Fitness of each solution is evaluated based on the objective function.

3. Iterative Update: PSO equations are applied to update frequency, velocity, and solution iteratively.

4. Solution Constraints: Solutions are checked to ensure they lie within the specified limits.

5. Solution Update: New solutions are compared with the previous best solution, and updates are made based on fitness and the loudness factor.

6. Convergence Check: The algorithm iterates until the maximum number of iterations is reached.

Results:

• The code outputs the power generation values for each generator, considering the optimized dispatch with losses.

• Total power generation is compared with demand plus losses to ensure balance.

• Fuel costs for optimized dispatch are provided.

Conclusion: This code successfully implements a power generation dispatch optimization algorithm using PSO, considering transmission losses and fuel costs. By optimizing the power generation schedule, the code helps minimize fuel expenses while ensuring reliable electricity supply to meet demand. The results demonstrate the effectiveness of the PSO algorithm in optimizing complex energy systems.