PENYELESAIAN DYNAMIC ECONOMIC DISPATCH PADA SISTEM PEMBANGKIT THERMAL MENGGUNAKAN METODE CONSTRICTION FACTOR PARTICLE SWARM OPTIMIZATION

NUGROHO, NAUFAL INDRASTOTO (2023) PENYELESAIAN DYNAMIC ECONOMIC DISPATCH PADA SISTEM PEMBANGKIT THERMAL MENGGUNAKAN METODE CONSTRICTION FACTOR PARTICLE SWARM OPTIMIZATION. S1 thesis, UNIVERSITAS MUHAMMADIYAH PURWOKERTO.

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Abstract

Economic dispatch (ED) is a calculation to find the operation of power generators with the lowest cost for a limited set of generators, subject to individual constraints, and to meet the total generation that can fulfill the load demand and losses. ED focuses on coordinating the power produced and the costs incurred by all electric generators operating in the system. Dynamic economic dispatch (DED) extends the ED problem, where in DED, the load demand constantly changes in each period within a 24-hour range. In this research, ramp-rate limits and prohibited operating zones are also applied as constraints in the DED simulation. The DED simulation will be conducted in Matlab using the Constriction Factor Particle Swarm Optimization (CFPSO) algorithm. The CFPSO algorithm is tested on the IEEE 6-unit thermal system and the IEEE 15-unit thermal system. The results of the CFPSO algorithm on both systems are then compared with the results from the Bees Algorithm (BA) testing. The results show that both algorithms can solve the DED simulation without violating the applied constraints. From the testing results on both systems, the CFPSO algorithm yields a total cost of 317,328.2387 Btu/Hr and 770,141.9306 Btu/Hr, while the BA yields a total cost of 317,500.4784 Btu/Hr and 773,737.8976 Btu/Hr. In terms of computation time and convergence speed, the CFPSO algorithm completes the simulation in 8.557 seconds and 9.153 seconds, reaching convergence at the 30th and 55th iterations. On the other hand, the BA completes the simulation in 62.833 seconds and 68.338 seconds, reaching convergence at the 12th and 11th iterations.

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