Abstract
The optimization of economic emission load dispatch is one of the most significant tasks in power plants. This article aims to analyze a new application of the computational optimization by simulated annealing technique including turning off the motors with greatest losses. The incremental cost of fuel consumption and the lambda iteration methods are combined to determine the best parameters of active power of each $$i_{\mathrm{th}}$$ generator unit, ensuring that the total losses and demand are equal to the total generated power but minimizing the total cost of fuel consumption and carbon emission. Many materials and methods have been elaborated to fix the economic emission load dispatch, among them are as follows: differential evolution method, gradient method and Newton’s method. The results found for this case study, with the new application of simulated annealing, were outstanding having a reduction of 20.14% in the total fuel cost, comparing to classical methods that distribute the generation of power among all motors, including the least efficient ones. This method helps the expert in the decision making of preventive maintenance of machines that are not working in the moment of multi-objective optimization, improving not only the yield of generation and carbon emission reduction but also of the power plant generation planning.
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