Thermal Economic Dispatch Using Improved Particle Swarm Optimization (IPSO) Algorithm

The present study addresses for solving economic dispatch (ED) problems​​ for power supply in the power system, which is an important role in the study of power system (Load Demand). The objective of Optimal (generation dispatch) Combination of power output to meet minimum cost (Fuel Cost Function equation) while satisfying the constraints. The Optimal (generation dispatch) such thermal power plant that difficult to Solve the Fuel Cost Function equation.
Therefore, researchers have been developed some of Power Optimize Techniques to solve these problems, in a traditional methods and modern methodartificial intelligence (AI), such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), etc. All of these methods are used to solve quadratic cost function equations. By the way in this thesis, indicate another method that uses Improved Particle Swarm Optimization (IPSO), to solve economic dispatch (ED), simulation using MATLAB R2016a to test 2different cases study according to IEEE standard test system. There are IEEE 6bus with 3generators and IEEE 26bus with 6generators, simulated in MATLAB R2016a by using Improved Particle Swarm Optimization (IPSO) with 2 cases study: In the first case study according to the standard test system IEEE 6Bus with 3generators have total power generators 217.907 MW, total power load 210.000 MW, total costs 2707.50 $/hrand total power loss 8.022 MW. In the second case, study according to the standard test system IEEE 26Bus with 6generators have total power generator 1274.166 MW, total power load 1263 MW, total cost 14,758.38 $/hr, and total power loss 11.182 MW. In conclusion, the results of IPSO methods are better than PSO methods.