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Economic and Emission 24 hours load dispatch using differential evolution


Simulink Model Overview:

The heart of this solution lies in a Simulink model designed to optimize a power plant's operation over a 24-hour cycle. The model incorporates 10 generator units, each contributing to the power generation. The differential evolution algorithm is employed to dynamically adjust the power generation parameters to minimize fuel costs, emission costs, and maintain power balance.

Differential Evolution Algorithm Implementation:

The MATLAB code showcases the implementation of the Differential Evolution algorithm for the optimization task. The algorithm takes into account the 24-hour load data, generator unit details, fuel costs, emission data, and power loss information. The optimization loop iterates over each hour, adjusting the power generation parameters to minimize costs and maintain balance.

Objective Function and Convergence:

The objective function is a key component of the optimization, combining fuel costs, emission costs, and a penalty factor for power balance. The algorithm converges over multiple iterations, dynamically adjusting the power generation for each hour. The convergence graph displays the progress of the optimization process, showcasing how the algorithm refines the parameters over time.

Results and Final Output:

Upon completion of the optimization, the model generates detailed results for each hour. This includes the optimal power generation for each generator unit, fuel costs, emission costs, power loss, and the overall fitness value. The final data provides insights into the optimized economic emission load dispatch for the entire 24-hour duration.

Conclusion:

This solution demonstrates the power and efficiency of the Differential Evolution algorithm in optimizing economic emission load dispatch for power plants. By dynamically adjusting the power generation parameters, the algorithm successfully minimizes fuel costs, emission costs, and maintains power balance. The comprehensive results obtained through this process offer valuable insights for enhancing the economic and environmental performance of power plants.

Thank you for joining us in exploring this optimization solution.


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