<![CDATA[In a generation, a generator can serve a load with a certain power value, so what needs to be considered is how much power must be generated by each generating unit, so that the optimum power generation and economical fuel costs are obtained or commonly called the Economic Dispatch (ED). Several methods such as conventional methods have been carried out in solving Economic Dispatch problems such as the Lagrange, Linear Programming and Quadratic Programming methods. In a problem such as economic dispatch, researchers using gravitational Search Algorithm methods, (GSA) is one of the modern methods applied based on the laws of gravity. In the ED representation the GSA method finds optimum values of optimally generated power as a reference for the lowest fuel cost ($ / h) as the best mass with the smallest distance. To test the Gravitational Search Algorithm method, it is tested with several different parameters from the IEEE standard system on 3 generator units with 6 generator units and 20 generator units. To test the effectiveness of the Gravitational Search Algorithm method, the results are compared with conventional methods, namely the Lagrange Multiplier method. One of the results of the test is the unit 3 generator test system with losses of transmission on the load test 125 MW with the GSA method having a more economical price than the LM method of ± 0.185%, in testing a load of 225 MW taking into account transmission loss, GSA method more economical than the LM method of ± 0.124%. Furthermore, transmission losses at the time of the test load 225 MW with the GSA method amounting to ± 2.52% of the power generated by calculating the transmission loss means that the total power to be generated is 230.8119 MW. While the LM method is ± 2.64% with a total power generation of 231,101 MW. The factors that affect the results of the GSA method optimization test are Mass Agent (N), alpha, G0, and Maximum Iteration Number. In this case the gravitational search algorithm method is one of the modern methods that can be used in economic dispatch problems because it has succeeded in completing the achievement of optimum prices for generating economic fuel costs with the accuracy of the results obtained optimally.]]>
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